Clinical examination and detection item correlation analysis method based on multi-agent cooperation

Abstract

The invention discloses a clinical examination and detection item correlation analysis method based on multi-agent cooperation, and relates to the technical field of correlation analysis, and the method comprises the steps: obtaining the name and basic parameters of an examination item, and determining the technical field of the examination item through a classification system and an algorithm; related field parameters are extracted from the knowledge management module, and configuration parameters of the intelligent agent are set; generating a task instruction based on the basic parameters, transmitting the instruction through a structured message transmission mechanism, and obtaining an agent processing result; and performing verification analysis on the processing result by using a hypothesis production and verification engine to obtain a correlation analysis result of the project. According to the method, the hypothesis content can be evaluated from multiple angles, it is ensured that the obtained correlation analysis result has high scientificity and credibility, and powerful support and basis are provided for research and application of clinical examination and detection items.

Description

Technical Field

[0001] This invention relates to the field of correlation analysis technology, and in particular to a method for correlation analysis of clinical laboratory test items based on multi-agent collaboration. Background Technology

[0002] Clinical laboratory data is vast and diverse, containing rich medical information, but traditional methods struggle to efficiently extract its potential value. Multi-agent systems, through the division of labor and collaboration among agents, can simulate the working mode of a medical research team and efficiently handle complex tasks. Large language models possess powerful natural language processing capabilities and can be adapted to the needs of the medical field through role definition and instruction fine-tuning. Knowledge graphs and semantic indexing technologies can efficiently store and manage medical knowledge, improving the accuracy and efficiency of knowledge retrieval. Statistical analysis and machine learning methods are important tools for validating medical hypotheses, capable of discovering potential correlation patterns in data. Existing clinical decision support systems largely rely on rule engines and simple statistical analysis, making it difficult to handle complex multi-factor correlation problems. Summary of the Invention

[0003] The technical problem to be solved by this invention is to address the shortcomings of existing technologies. Specifically, it provides a method for correlation analysis of clinical laboratory test items based on multi-agent collaboration, as detailed below:

[0004] 1) In a first aspect, the present invention provides a method for correlation analysis of clinical laboratory test items based on multi-agent collaboration, the specific technical solution of which is as follows:

[0005] Obtain the name and corresponding basic parameters of any clinical laboratory test item to be analyzed, and determine the technical field corresponding to the item name based on the test item classification system and domain recognition algorithm;

[0006] Extract domain parameters related to the technology field from the pre-built knowledge management module, and set the configuration parameters of at least two categories of intelligent agents based on the domain parameters;

[0007] Based on the basic parameters, generate task instructions for different intelligent agents, and transmit the task instructions according to the preset structured message passing mechanism, as well as obtain the processing results of different intelligent agents in response to the task instructions.

[0008] The hypothesis generation and validation engine verifies and analyzes the hypothesis content corresponding to all the acquired processing results to obtain the correlation analysis results of the clinical laboratory test items to be analyzed.

[0009] The beneficial effects of the clinical laboratory test item correlation analysis method based on multi-agent collaboration provided by this invention are as follows:

[0010] Firstly, by accurately identifying the technical fields of clinical laboratory testing projects, a clear direction and targeted parameter settings can be provided for subsequent analysis, ensuring that the analysis process is more aligned with the actual needs and professional background of the project, thereby improving the accuracy and reliability of the analysis. Secondly, relevant domain parameters are extracted from the pre-built knowledge management module, and the configuration parameters of the intelligent agents are set accordingly. This enables efficient utilization of knowledge and personalized customization of intelligent agents, allowing different types of intelligent agents to work together better, leverage their respective advantages, and improve the overall performance and analysis efficiency of the system. Thirdly, task instructions are generated based on basic parameters, and task allocation and result acquisition are carried out through a preset structured message passing mechanism. This ensures clear communication of task instructions and effective integration of processing results, achieving efficient collaboration and information sharing among multiple intelligent agents, avoiding ambiguity and conflict in information transmission, and further enhancing the stability and scalability of the system. Finally, the hypothesis generation and verification engine is used to verify and analyze the processing results, enabling evaluation of hypothesis content from multiple perspectives. This ensures that the obtained correlation analysis results have high scientific validity and credibility, providing strong support and basis for the research and application of clinical laboratory testing projects, helping to promote knowledge discovery and technological innovation in the field of medical testing, and improving the level of clinical diagnosis and treatment.

[0011] 2) Secondly, the present invention also provides a clinical laboratory testing item correlation analysis system based on multi-agent collaboration, the specific technical solution of which is as follows:

[0012] The acquisition unit is used to: acquire the name of any clinical laboratory test item to be analyzed and its corresponding basic parameters, and determine the technical field corresponding to the item name based on the test item classification system and the domain identification algorithm;

[0013] The extraction unit is used to: extract domain parameters related to the technical field from the pre-built knowledge management module, and set the configuration parameters of at least two categories of intelligent agents based on the domain parameters;

[0014] The processing unit is used to: generate task instructions for different intelligent agents based on basic parameters, transmit the task instructions according to a preset structured message passing mechanism, and obtain the processing results of different intelligent agents for the task instructions;

[0015] The analysis unit is used to: verify and analyze the hypothesis content corresponding to all the acquired processing results through the hypothesis generation and validation engine, and obtain the correlation analysis results of the clinical laboratory test items to be analyzed.

[0016] 3) In a third aspect, the present invention also provides an electronic device, the electronic device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to enable the electronic device to perform any of the methods described above.

[0017] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to perform any of the above methods.

[0018] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0019] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0020] Figure 1 This is a flowchart illustrating a method for analyzing the correlation of clinical laboratory test items based on multi-agent collaboration, according to an embodiment of the present invention.

[0021] Figure 2 This is an architecture diagram of a clinical validation assistant system for a clinical laboratory testing item correlation analysis method based on multi-agent collaboration, according to an embodiment of the present invention.

[0022] Figure 3 This is a flowchart of a clinical validation assistant system for a clinical laboratory testing item correlation analysis method based on multi-agent collaboration, according to an embodiment of the present invention.

[0023] Figure 4 This is one of the schematic diagrams of a system interface for a clinical laboratory test item correlation analysis method based on multi-agent collaboration according to an embodiment of the present invention;

[0024] Figure 5 This is a second schematic diagram of the system interface of a clinical laboratory test item correlation analysis method based on multi-agent collaboration according to an embodiment of the present invention;

[0025] Figure 6 This is the third schematic diagram of the system interface of a clinical laboratory test item correlation analysis method based on multi-agent collaboration according to an embodiment of the present invention;

[0026] Figure 7 This is a schematic diagram of a clinical laboratory test item correlation analysis method based on multi-agent collaboration, according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0028] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for correlation analysis of clinical laboratory test items based on multi-agent collaboration, comprising the following steps:

[0029] S1, obtain the name of any clinical laboratory test item to be analyzed and its corresponding basic parameters, and determine the technical field corresponding to the item name based on the test item classification system and the domain identification algorithm;

[0030] S2, extract domain parameters related to the technical field from the pre-built knowledge management module, and set the configuration parameters of at least two categories of intelligent agents based on the domain parameters;

[0031] S3 generates task instructions for different intelligent agents based on basic parameters, and transmits the task instructions according to a preset structured message passing mechanism, as well as obtains the processing results of different intelligent agents for the task instructions.

[0032] S4 uses the hypothesis generation and validation engine to validate and analyze the hypothesis content corresponding to all the acquired processing results, and obtains the correlation analysis results of the clinical laboratory test items to be analyzed.

[0033] The beneficial effects of the clinical laboratory test item correlation analysis method based on multi-agent collaboration provided by this invention are as follows:

[0034] Firstly, by accurately identifying the technical fields of clinical laboratory testing projects, a clear direction and targeted parameter settings can be provided for subsequent analysis, ensuring that the analysis process is more aligned with the actual needs and professional background of the project, thereby improving the accuracy and reliability of the analysis. Secondly, relevant domain parameters are extracted from the pre-built knowledge management module, and the configuration parameters of the intelligent agents are set accordingly. This enables efficient utilization of knowledge and personalized customization of intelligent agents, allowing different types of intelligent agents to work together better, leverage their respective advantages, and improve the overall performance and analysis efficiency of the system. Thirdly, task instructions are generated based on basic parameters, and task allocation and result acquisition are carried out through a preset structured message passing mechanism. This ensures clear communication of task instructions and effective integration of processing results, achieving efficient collaboration and information sharing among multiple intelligent agents, avoiding ambiguity and conflict in information transmission, and further enhancing the stability and scalability of the system. Finally, the hypothesis generation and verification engine is used to verify and analyze the processing results, enabling evaluation of hypothesis content from multiple perspectives. This ensures that the obtained correlation analysis results have high scientific validity and credibility, providing strong support and basis for the research and application of clinical laboratory testing projects, helping to promote knowledge discovery and technological innovation in the field of medical testing, and improving the level of clinical diagnosis and treatment.

[0035] S1: Obtain the project name and basic parameters, and determine the technical field.

[0036] Obtain the name of the clinical laboratory test to be analyzed (e.g., "complete blood count" or "gene sequencing") through the interface of the clinical laboratory testing system or by manual input. Simultaneously, obtain the basic parameters of the test, such as the detection method (e.g., chemiluminescence immunoassay, PCR), sample type (blood, tissue, urine, etc.), and detection frequency (daily, weekly, etc.).

[0037] Construct a classification system for clinical laboratory testing items, categorizing them according to dimensions such as technical principles, test subjects, and application areas. For example, based on technical principles, they could be divided into biochemical testing, immunological testing, and molecular diagnostics. Based on test subjects, they could be divided into blood testing, tissue testing, and microbiological testing.

[0038] Domain identification algorithms (rule-based classification algorithms or machine learning algorithms) are used to analyze project names. For example, keyword matching (e.g., "gene" corresponds to the molecular diagnostics domain, "antibody" corresponds to the immunoassay domain) or training a classification model (using historical project data as a training set) can be used to determine the technical field to which a project belongs.

[0039] Output the project name, basic parameters, and corresponding technical field. For example: Project name: Complete blood count (CBC); Basic parameters: Detection method: blood cell analyzer; Sample type: blood; Detection frequency: daily; Technical field: Clinical hematology testing.

[0040] In another embodiment of this solution, a classification system for testing items is established, categorizing items by subject area (such as biochemistry, immunology, hematology, etc.). A domain knowledge mapping table is created to associate testing item categories with professional fields, and an ontology model is used to describe the hierarchy and relationships between testing items, facilitating the inference of their respective fields.

[0041] Domain identification algorithm: Implements keyword matching algorithm, extracts domain features from the project name and description, applies machine learning classifier, predicts professional domain based on project features, uses ontology reasoning, and infers the domain affiliation of unknown projects through inter-project relationship network.

[0042] Inference Method: The system employs a domain inference algorithm based on graph network propagation, combined with multiple inference strategies to determine the domain affiliation of unknown items. The specific inference process includes: First, relationship network construction: using the test item as a node and the relevance, co-occurrence, and functional similarity between items as edge weights, a project relationship graph network is constructed. Each edge contains attributes such as relationship strength (0-1), relationship type (physiological, pathological, diagnostic), and confidence. Second, domain label propagation: for project nodes with known domain affiliation, their domain labels are used as initial seeds and propagated to neighboring nodes through a label propagation algorithm. The propagation strength is inversely proportional to the edge weight and distance, calculated as: Domain confidence = Σ(neighboring node domain weight × edge weight × distance decay factor). Third, similarity clustering analysis: calculating the comprehensive similarity score between the unknown item and known items in each domain, including semantic similarity (based on item name and...). The steps are as follows: 1) NLP analysis (description), functional similarity (ontology matching based on biological function), and statistical similarity (correlation analysis based on historical data). The comprehensive score is calculated as 0.4 × semantic similarity + 0.3 × functional similarity + 0.3 × statistical similarity. 2) Multi-path reasoning verification: The inference results are verified through multiple paths in the graph. When multiple independent paths point to the same domain, the confidence level is increased. When paths point to different domains, the number and intensity weight of paths in each domain are calculated. 3) Threshold judgment and output: A domain attribution confidence threshold is set (high confidence ≥ 0.8, medium confidence 0.6-0.8, low confidence < 0.6). The most likely domain attribution and its confidence score are output. Items with a confidence level below 0.6 are marked as "requires expert review".

[0043] S2: Extract domain parameters and set agent configuration

[0044] A pre-built knowledge management module includes domain parameters for different technical fields. These parameters include: relevant testing standards and specifications, common testing indicators and their normal ranges, and commonly used analytical methods and tools within the field. For example, for the "clinical hematology testing" domain, parameters might include the normal range of blood cell counts and typical indicators for different blood diseases.

[0045] The process of setting agent configuration parameters may include:

[0046] Identify at least two categories of intelligent agents, such as data analysis agents and knowledge reasoning agents;

[0047] Data analysis intelligent agent: used to perform statistical analysis and trend analysis on detection data.

[0048] Knowledge-based reasoning agent: used to perform diagnostic reasoning or correlation analysis by combining domain knowledge.

[0049] Based on the extracted domain parameters, configuration parameters are set for each agent. For example:

[0050] The configuration parameters of a data analysis agent may include the range of indicators to be analyzed (such as white blood cell count, hemoglobin level, etc.) and the analysis methods (such as linear regression analysis, cluster analysis, etc.).

[0051] The configuration parameters of a knowledge reasoning agent may include relevant rules in the knowledge base (e.g., elevated white blood cell count may indicate infection) and parameters of the reasoning engine (e.g., confidence threshold).

[0052] Output the configured agent and its parameter settings. For example:

[0053] Data analysis agent: configured to analyze white blood cell count and hemoglobin level using cluster analysis method.

[0054] Knowledge-based reasoning agent: configured to use the rule "white blood cell count > 12 × 10" 9 " / L indicates infection", with a confidence threshold of 0.8.

[0055] In another embodiment of this solution, the pre-built knowledge management module specifically includes:

[0056] The knowledge management module enables structured storage and efficient retrieval of medical knowledge. It employs a graph database to store the relationship network between test items and uses semantic indexing technology to improve retrieval efficiency. Semantic indexing technology is a composite indexing mechanism that integrates medical ontology knowledge, vector representation, and semantic networks. This technology achieves efficient and accurate knowledge retrieval and reasoning by capturing the deep semantic relationships between test items.

[0057] The system employs NetworkX to construct a graph data structure, storing the relationship network with test item codes as nodes and relationships as weighted edges. The network establishment process includes: first, loading all test items as graph nodes, each containing complete medical semantic information; then, discovering relationships through various intelligent algorithms: ① using an AI large-scale language model for semantic similarity inference based on item descriptions; ② discovering diagnostic monitoring relationships through clinical pathway template matching; ③ calculating relevance for medical ontology classification; and ④ verifying relationship credibility through literature mining. Each relationship edge contains multi-dimensional attributes such as relationship type, strength level, confidence level, and evidence source. The system also implements a dynamic optimization mechanism, continuously optimizing the completeness and accuracy of the relationship network through relationship quality assessment, network connectivity analysis, and multi-agent collaboration, ultimately forming a multi-layered medical knowledge relationship network encompassing semantic associations, clinical pathways, and evidence-based medicine.

[0058] The system employs a multi-layered hybrid retrieval architecture, implementing five core retrieval methods: ① Traditional keyword matching retrieval, using fields such as item name, code, and description for precise or fuzzy matching; ② Semantic similarity retrieval, utilizing medical ontology knowledge and AI language models to calculate semantic distance between items, supporting concept-level relevance searches; ③ Relationship network traversal retrieval, based on a NetworkX graph structure for depth / breadth-first search, discovering directly and indirectly related test items; ④ Multi-dimensional index retrieval, quickly locating target knowledge through multiple dimensions such as classification indexes, topic indexes, and time indexes; ⑤ Intelligent reasoning retrieval, combining clinical pathway templates, disease-test mappings, and literature evidence for composite reasoning queries. The retrieval process supports query optimization (medical terminology standardization), result ranking (relevance, time, citation frequency), caching mechanisms, and incremental updates, achieving comprehensive retrieval capabilities from simple string matching to complex semantic reasoning.

[0059] The knowledge management module includes the following four levels:

[0060] After the mapping layer determines the semantic types and hierarchical relationships, the relationship network layer and the inference rule layer directly apply these parameters: The relationship network layer uses the `find_semantic_relations()` method to calculate the semantic relevance between nodes using the classification information from the mapping layer. Items of the same category receive a relevance weight of 0.8, and items associated across categories receive a weight of 0.6. The inference rule layer uses the hierarchical relationship parameters in `_has_cross_category_relation()`. A predefined cross-category relationship matrix (e.g., a weight of 0.8 for glucose metabolism and cardiovascular diseases) guides the confidence calculation of the inference rules.

[0061] 1. Medical Ontology Mapping Layer

[0062] Implementation principle: Map test items to standardized medical ontologies (such as SNOMED-CT, LOINC, etc.); establish standardized associations between test items and physiological systems and pathological processes; construct a hierarchical concept classification system. Index construction method: First, use a terminology mapping engine to match test item names and codes with standard terminology sets; extract the semantic type of each test item (such as "liver function test", "lipid metabolism indicators").

[0063] Code and Standard Terminology Set: The medical ontology mapping layer of this invention employs a multi-dimensional terminology mapping engine to achieve intelligent matching between laboratory test item identifiers and international standard medical terminology sets. This engine integrates the WHO International Classification of Diseases (ICD), the terminology specifications of the Committee for Standardization of Clinical Laboratory Medicine (CCLM), and the LOINC (Logical Observation Identifiers Names and Codes) laboratory test item coding system. Specifically, the system constructs a multi-level terminology mapping table: ① The standard code layer contains internationally recognized laboratory test item abbreviation codes (such as ALT, AST, GLU, etc.); ② The localized terminology layer contains medical terminology expressions in various regional languages ​​(such as the Chinese 'alanine aminotransferase' corresponding to the English 'Alanine Aminotransferase'); ③ The semantic classification layer categorizes terms into predefined medical ontology categories according to biological functions (such as liver function, kidney function, glucose metabolism, etc.). The mapping engine uses fuzzy matching algorithms and semantic similarity calculations to handle terminology variations, synonym recognition, and cross-language mapping, ensuring that laboratory test item data from different sources can be unified into a standardized medical semantic space, providing a standardized data foundation for subsequent relational network construction and intelligent reasoning.

[0064] Record the hierarchical relationship between concepts (e.g., "ALT" is a subclass of "transaminase", and "transaminase" is a subclass of "enzyme detection").

[0065] It should be noted that "concepts" refer to medical semantic entities after standardized mapping, which have clear definitions and classification attributes within the medical ontology framework. Specifically, these include: test item concepts, such as standardized test indicators like "ALT," "AST," and "total bilirubin"; classification concepts, such as functional classifications like "transaminase," "bilirubin," and "lipid indicators"; system concepts, such as organ system-level classifications like "liver function tests," "kidney function tests," and "myocardial enzyme profiles"; and top-level concepts, such as the highest-level abstract classifications like "enzyme detection," "metabolic indicators," and "immune indicators." The hierarchical relationships are constructed using a directed acyclic graph (DAG) structure, where each concept node contains attributes such as standardized term ID, concept name, definition description, and medical domain. For example, in the hierarchical chain "ALT → transaminase → enzyme detection," "ALT" is the leaf node concept (specific test item), "transaminase" is the intermediate node concept (functional classification), and "enzyme detection" is the root node concept (top-level classification). The system establishes a complete hierarchical structure of medical ontology by recording the "is-a" relationship (subclass relationship), "part-of" relationship (composition relationship), and "related-to" relationship (association relationship) between these concepts, supporting multi-level semantic reasoning and knowledge association analysis from concrete to abstract and from professional to general.

[0066] Example: The ALT test item was not only indexed as the keyword "alanine aminotransferase", but also tagged as the category of "liver function test" and the functional group of "cell damage markers", and semantically associated with concepts such as "hepatocytes" and "amino acid metabolism".

[0067] 2. Relationship Network Layer

[0068] Implementation Principle: A graph database is used to store multidimensional relationships between test items; attributes such as type, strength, and level of evidence are assigned to these relationships; and a traversable network of relationship paths is constructed. Index Construction Method: Known relationships between test items are extracted from medical literature and clinical guidelines; statistical correlations are discovered from clinical data using data mining techniques; and expert knowledge is integrated to set relationship weights and attributes. Example: A "co-elevation" relationship between "ALT" and "AST" is established, and the specific pattern (AST / ALT>2) and level of evidence (Level A) of this relationship in "alcoholic liver disease" are simultaneously labeled.

[0069] Multidimensional relationships encompass 12 core attributes: ① Relationship type classification (5 types including diagnostic combinations, clinical associations, and biological mechanisms); ② Dual strength quantification (0-1 numerical strength and strong, medium, and weak strength levels); ③ Multi-layered evidence support (literature evidence, clinical guidelines, expert consensus, and A / B / C three-level evidence levels); ④ Clinical context annotation (disease scenario, treatment stage, and population specificity); ⑤ Temporal relationship dimension (synchronous, sequential, and dynamic monitoring relationships); ⑥ Quantitative mathematical relationships (correlation coefficient, regression parameters, and specificity ratio patterns); ⑦ Pathophysiological levels (four levels: molecular, cellular, organ, and system); ⑧ Quality control attributes (confidence level, validation status, and timestamp); ⑨ Multi-agent optimization labeling (expert consensus and optimization scoring); ⑩ Network topology (direct, indirect, and clustered relationships); Standardized version control; Intelligent reasoning mechanism. Through this multidimensional relationship modeling, the system can accurately describe complex medical knowledge such as 'the synergistic increase relationship between ALT and AST, presenting a specific pattern of AST / ALT>2 in alcoholic liver disease, with an evidence level of A and a confidence level of 0.85', realizing a comprehensive knowledge representation capability from simple association to complex semantic reasoning.

[0070] A multi-dimensional quantitative assessment system for defining relationship strength and a hierarchical evidence level evaluation are implemented. Relationship strength is represented by a continuous numerical value of 0-1, determined through a weighted calculation of four dimensions: descriptive quality (25%), evidence strength (25%), clinical relevance (30%), and classification similarity (20%), and categorized into three levels: strong (≥0.8), moderate (0.6-0.79), and weak (<0.6). Evidence levels employ a modified evidence-based medicine grading system: Level A evidence (0.8-1.0) includes systematic reviews, RCTs, and authoritative guidelines; Level B evidence (0.6-0.79) includes cohort studies and expert consensus; and Level C evidence (0.4-0.59) includes case reports and expert opinions. Through a multi-agent collaborative mechanism, combined with literature mining, statistical analysis, and expert knowledge, the system achieves precise quantitative assessment of relationships in medical testing, ensuring that each relationship in the knowledge base has a clear strength level and reliable evidence support, providing a scientific basis for clinical decision-making.

[0071] Known relationships are defined as relationships between laboratory test items that are clearly recorded and widely recognized in authoritative clinical guidelines (WHO, ADA, ESC, etc.), standardized clinical pathways, and evidence-based medicine literature. The system employs a three-level classification: Level 1 relationships (strength ≥ 0.8) originate from international guidelines and Level A evidence, such as the ALT-AST liver function combination; Level 2 relationships (strength 0.6-0.79) are based on professional consensus and Level B evidence, such as the UA-BUN kidney function combination; and Level 3 relationships (strength 0.4-0.59) originate from clinical practice experience and Level C evidence. Using NLP technology, relationship patterns such as 'aided diagnosis,' 'monitoring,' and 'assessment' are automatically identified from guideline documents. Combined with a disease-laboratory mapping knowledge base and clinical pathway templates, a known relationship database containing standardized relationships across different disease categories has been established.

[0072] Statistical Correlation: The system standardizes and performs quality control on clinical laboratory data through a data preprocessing layer. A statistical correlation calculation layer identifies numerical associations between test items based on the Pearson correlation coefficient matrix and the SciPy statistical significance test (p<0.05). A pattern discovery layer integrates K-means clustering, linear regression trend analysis, and isolated forest anomaly detection algorithms to uncover hidden statistical patterns. A relation quantification layer transforms the statistical results into structured relation objects containing correlation coefficients, p-values, and relation types. Finally, an intelligent inference layer combines an AI language model for clinical semantic verification. In use, the system receives clinical numerical data in pandas DataFrame format, automatically filters item pairs with |r|>0.5 and statistical significance, and outputs a standardized relation set containing correlation strength, statistical confidence, and relation direction. In terms of contextual connection: the front end receives the prior knowledge guidance of standardized terminology and clinical pathway templates from the medical ontology mapping layer, while the back end inputs the statistically discovered relationships into the relational network layer to construct the graph structure edge weights and provides evidence-based statistical support for the inference rule layer. The medical rationality of the statistical relationships is verified by the clinical expert intelligent agent, realizing the full-link automated transformation from raw data to knowledge graph, forming an intelligent relationship discovery mechanism with dual verification of statistical drive and expert knowledge.

[0073] Relationship Weights and Attributes: This system establishes a composite attribute system encompassing 12 core dimensions, including relationship strength and confidence, relationship type and coding, evidence support, clinical context, time dimension, and quantitative relationship. Relationship strength attributes include four quantitative indicators: numerical strength (0-1), rank strength (strong / medium / weak), confidence level, and categorical confidence level. Evidence support attributes cover the evidence source array, evidence level (A / B / C), evidence strength numerical value, and source study identifier. Clinical context attributes define clinical application scenarios, disease context arrays, clinical significance levels, and population-specific parameters. Time dimension attributes define time-related relationship types, stability, and review periods. Quantitative relationship attributes include statistical correlation coefficients, significance tests, and regression parameters.

[0074] 3. Semantic Vector Layer

[0075] Implementation Principle: Deep learning technology is applied to convert test items into high-dimensional semantic vectors; the semantic features of test items are represented in vector space; and the semantic similarity between items is measured by vector distance. Index Construction Method: A pre-trained medical domain language model is used to process text related to test items; the contextual semantic representation of test items is extracted; and dimensionality reduction techniques are applied to optimize vector representation efficiency. Example: The semantic vectors generated by the system for "triglycerides" and "insulin resistance" have high cosine similarity, and the system can still identify their potential association even if the two are not directly related.

[0076] Related texts: Text content includes: ① Basic information text of test items, covering identifying text such as Chinese and English names, descriptions, aliases, and abbreviations; ② Clinical significance and application text, including clinical significance descriptions, pathological significance of elevation / decrease, and a list of related diseases; ③ Biological mechanism text, describing the physiological and pathological mechanisms of the test items, interfering factors, and other mechanistic texts; ④ Clinical pathway and guideline text, derived from standardized treatment pathways, disease contexts, and application scenarios at different stages of treatment; ⑤ Literature and evidence text, including evidence-based text such as abstracts of authoritative journal articles, research backgrounds, and sources of evidence; ⑥ Relationship description text, describing the clinical and biological relationships between test items; ⑦ Composite text constructed by the system, a comprehensive semantic analysis text formed by combining multiple text fields through algorithms.

[0077] The process involves extracting the contextual semantic representation of test items and applying dimensionality reduction techniques to optimize vector representation efficiency. Specifically, the process begins with the following steps: First, in the contextual semantic representation extraction stage, a pre-trained language model is used to perform deep semantic encoding on the text related to the test items, converting multi-dimensional textual information, including item names, clinical significance, biological mechanisms, and disease associations, into high-dimensional semantic vector representations. Then, in the dimensionality reduction optimization stage, PCA (Principal Component Analysis) is used to retain key variance information, UMAP (Unified Manifold Approximation Projection) is used to maintain local neighborhood structure, and t-SNE (Temporal-Synchronous Neighborhood Evolution) is combined to achieve high-quality low-dimensional embedding, compressing the original 768-dimensional or higher-dimensional semantic vectors to an optimized representation of 128-256 dimensions. Finally, an efficient index structure is constructed, integrating FAISS vector indexes to achieve millisecond-level similarity search, establishing a code mapping mechanism to support fast queries, and implementing multiple similarity measures such as cosine similarity and Euclidean distance.

[0078] 4. Reasoning Rule Layer

[0079] Implementation Principle: Encodes reasoning rules and professional knowledge in the medical field; supports rule-based automatic reasoning and relational derivation; realizes the explicit expression of tacit knowledge. Index Construction Method: Extracts diagnostic and interpretive rules from clinical guidelines; transforms causal relationships in medical textbooks into reasoning rules; integrates expert consensus to form a reasoning chain. Example: The system includes reasoning rules such as "If ALT is significantly higher than AST and GGT is normal, it suggests an increased likelihood of non-alcoholic fatty liver disease," enabling searches to not only return direct matches but also provide reasoning explanations.

[0080] The reasoning rules are categorized into four main types: clinical pathway reasoning rules (test combinations in the screening-diagnosis-monitoring stages of disease diagnosis and treatment), classification correlation reasoning rules (weight matrices of associations between medical classifications), conditional reasoning rules (diagnostic models based on thresholds and logical judgments), and biological mechanism reasoning rules (metabolic pathways, organ function, and pathophysiological associations). Reasoning rules and professional knowledge are primarily acquired from four authoritative sources: ① Clinical guidelines, including international guidelines from the American Association for the Study of Liver Diseases (AASLD) and the European Society of Cardiology (ESC), as well as treatment guidelines from various specialty branches of the Chinese Medical Association; ② Medical textbooks, covering standardized knowledge from classic textbooks such as *Internal Medicine* and *Clinical Laboratory Diagnostics*; ③ Medical literature databases, including high-quality research literature from PubMed, Scopus, and CNKI; and ④ Clinical practice experience, integrating the practical knowledge of clinical experts, biochemical experts, and evidence-based medicine experts through a multi-agent system. The system automatically extracts structured rules from guidelines using natural language processing technology, identifies causal relationships in the literature, and, through expert agent collaboration verification and weighted fusion, ultimately forms an executable medical reasoning rule base, supporting intelligent reasoning and decision support from abnormal test results to disease diagnosis.

[0081] Automatic reasoning and relational derivation: The system uses six core algorithms, including clinical pathway reasoning, classification relevance reasoning, multi-agent collaborative reasoning, transitive reasoning, symmetric reasoning, and pattern recognition reasoning, to achieve automatic reasoning and relational derivation from basic medical knowledge to complex diagnostic logic. Through conditional reasoning rules and implicit pattern recognition, implicit medical knowledge is transformed into executable reasoning rules and explicit diagnostic suggestions.

[0082] Tacit knowledge refers to experiential, relevant, and context-dependent knowledge that is not explicitly stated but exists in medical practice. The system utilizes five core technologies—pattern mining, expert knowledge extraction, natural language processing, machine learning, and knowledge graph construction—to transform tacit knowledge into structured, actionable explicit rules. Clinical validation ensures the rules' accuracy and practicality, ultimately achieving an intelligent transformation from tacit experience to explicit knowledge.

[0083] In addition, the technical implementation methods of semantic indexing include:

[0084] 1. Hybrid Index Architecture

[0085] Inverted index component: handles exact term matching and Boolean queries;

[0086] Vector indexing component: Supports similarity search and semantic query;

[0087] Graph index component: Optimizes relational path queries and network traversal.

[0088] The optimization process is as follows: A medical knowledge relationship network is constructed using the NetworkX graph structure, integrating multi-dimensional centrality indicators such as degree centrality, betweenness centrality, and proximity centrality to identify key nodes and path bottlenecks; an intelligent bridging algorithm is implemented to dynamically construct connections based on a multi-dimensional scoring mechanism of classification similarity, degree importance, and semantic similarity; an asynchronous concurrent traversal mechanism is adopted to support large-scale path search, and resource contention is avoided by controlling the number of concurrent connections using semaphores; a network indicator caching system is established to avoid redundant calculations, and an incremental update strategy is implemented based on changes in the number of nodes and edges; a network health assessment system (with weighted allocations of 40% connectivity, 30% density, 20% clustering, and 10% degree distribution) is constructed to guide path optimization, and the network topology is adjusted in real time using a dynamic graph reconstruction algorithm, ultimately achieving performance improvement.

[0089] This hybrid architecture ensures efficient execution of different types of queries.

[0090] 2. Dynamic index update mechanism

[0091] Implement an incremental index update strategy to support dynamic expansion of the knowledge base; establish index version control to ensure query consistency; and implement index reconstruction scheduling to optimize system resource utilization.

[0092] The dynamic index update mechanism is implemented in five main ways: ① Incremental index update strategy achieves accurate incremental updates and consistency guarantees through change trackers, dependency graph analysis, and cascading impact calculation; ② Index version control system ensures data consistency during version changes through multi-version snapshot storage, version locking mechanism, and consistent query management; ③ Index reconstruction scheduling system achieves efficient utilization of system resources through intelligent priority evaluation, resource demand prediction, and dynamic scheduling optimization; ④ Dynamic knowledge base expansion support supports seamless expansion of the knowledge base structure through adaptive expansion mechanism, schema change management, and intelligent index adaptation; ⑤ Unified monitoring and alarm system ensures the stable operation of the entire dynamic index system through real-time indicator collection, multi-level alarm rules, and automatic repair mechanism.

[0093] 3. Query Transformation and Optimization

[0094] Convert natural language queries into semantic representations; select the optimal index path based on query characteristics; apply query expansion techniques to enhance recall.

[0095] This technology constructs a Chinese-English medical terminology mapping system and a MeSH ontology structure to intelligently convert natural language queries into standardized semantic representations; it employs a multi-path index selection algorithm to dynamically select the optimal retrieval path (including the main index, category index, relation index, and semantic index) based on query complexity and semantic features; and it utilizes a knowledge graph-based query expansion mechanism to significantly improve retrieval recall through synonym expansion, semantic association expansion, and cross-category relation reasoning.

[0096] 4. Application Examples

[0097] Semantic indexing and retrieval of liver function test items. Taking liver function test related queries as an example, the system's semantic indexing functions as follows: Query understanding: When receiving a query for "liver function indicators related to cholestasis," the system not only matches the keyword "cholestasis" but also understands its medical semantics; Multi-dimensional matching: Ontology matching: Identifies test items related to the biliary system (ALP, GGT, TBIL, DBIL); Relationship network matching: Finds indicators directly related to cholestasis; Vector similarity matching: Discovers semantically related but unlabeled items; Rule-based reasoning matching: Applyes professional rules such as "If the ALP / GGT ratio > 2.5, it suggests benign cholestasis." Comprehensive result generation: Returns the most relevant items (ALP, GGT, TBIL, DBIL) as the core results; provides a ranking of association strength (based on evidence level and association degree); adds relevant explanatory knowledge (such as "ALP mainly originates from hepatic bile duct epithelial cells"); provides typical abnormal patterns (such as "in benign cholestasis, ALP is significantly elevated while transaminase is slightly elevated").

[0098] This multi-level semantic indexing mechanism enables the system to handle complex medical queries, not only returning direct matching results but also providing in-depth related knowledge and explanations, greatly improving the accessibility and application value of clinical laboratory knowledge.

[0099] In addition, the knowledge base of this solution supports the integration of multi-source data, including structured test item definitions, semi-structured clinical guidelines, and unstructured medical literature.

[0100] S3: Generate task instructions and transmit and retrieve processing results.

[0101] Based on basic parameters (such as detection metrics, sample types, etc.), task instructions are generated for different agents. For example:

[0102] The task instruction for the data analysis agent is "to perform cluster analysis on the input white blood cell count and hemoglobin level data to identify abnormal samples".

[0103] The task instruction for the knowledge reasoning agent is to "determine whether there is a risk of infection based on the input white blood cell count and hemoglobin level data, combined with the rules of the knowledge base".

[0104] Pre-define a structured message passing mechanism, such as using a message queue or event-driven framework to implement the passing of task instructions.

[0105] The task instructions are encapsulated into a structured message format (such as JSON format), which includes information such as task content, target agent identifier, and input data.

[0106] After receiving the task instruction, the intelligent agent processes it according to the configuration parameters and task content, and returns the processing result in the form of a structured message.

[0107] The process of receiving the processing results of an agent through a message passing mechanism is as follows:

[0108] The data analysis agent might return a result such as "abnormally high white blood cell counts were found in samples 1 and 3".

[0109] The processing result returned by the knowledge reasoning agent is "Sample 3 has a risk of infection, with a confidence level of 0.9".

[0110] Output the processing results of all agents. For example:

[0111] Data analysis agent results: List of abnormal samples (sample 1, sample 3).

[0112] Results of the knowledge reasoning agent: Infection risk assessment (Sample 3 has an infection risk, confidence level 0.9).

[0113] In another embodiment of this solution, the preset structured message passing mechanism includes:

[0114] 1. Structured message passing mechanism

[0115] Intelligent agents communicate through a structured message passing mechanism. The core communication foundation of a multi-agent collaborative system is built upon this structured message passing mechanism, which enables precise and efficient information exchange and task coordination among specialized intelligent agents within the system.

[0116] 1.1 Message Structure Specification

[0117] Each structured message consists of the following key elements: sending entity identifier and role definition (sending agent ID and its functional role); receiving entity identifier and role definition (receiving agent ID and its functional role); message type classification (request, response, or notification); content body (including task description, data payload, priority marker, execution requirements, etc.); metadata information (associated session identifier, source message reference, timestamp, etc.). This strictly defined structure ensures that internal system communication has a clear source attribution, target orientation, and task orientation, effectively preventing information ambiguity and communication conflicts.

[0118] 1.2 Message Passing Flow

[0119] The message passing system adopts an architecture combining central routing and distributed processing: the system implements a dedicated message routing module responsible for message verification, entity parsing, and delivery management. When agents need to exchange information, the following standard process is followed: the sending entity constructs a structured message body according to the specification; the message enters the central routing system for format verification and legality checks; the routing system parses the receiving entity and executes message delivery; the receiving entity receives the notification and processes the message content according to the established protocol; the processing result is returned to the sending entity or other relevant entities through the same mechanism; this mechanism enables asynchronous communication between agents, allowing specialized agents to execute tasks in parallel, significantly improving the overall system operating efficiency. Figure 2 The diagram illustrates the three-tier architecture of the clinical validation assistant system and the interactions between its components. The system comprises an application layer, an intelligence layer, and a knowledge layer, realizing a complete process from data input to knowledge generation. Solid arrows represent the main control flow, while dashed arrows indicate data flow. The application layer provides the user interface and APIs, connecting downwards to the core component of the intelligence layer, the "research team." The intelligence layer includes the research team, expert agents (composed of five different roles), the validation system (including a validator factory and cross-validation), and a task management module. The knowledge layer manages the medical knowledge base, the clinical laboratory knowledge base, and external data sources, providing knowledge support for the intelligence layer.

[0120] 1.3 Application Examples

[0121] Taking clinical laboratory correlation analysis as an example, the workflow of this mechanism in actual operation is as follows:

[0122] The chief scientist agent initiates an analysis task, constructs and sends a professional analysis request message to the data analyst agent, clearly specifying: the analysis objective (correlation analysis of a specific set of liver function indicators); the analysis focus (focusing on the ratio relationship pattern between indicators); and the analysis requirements (clinical significance needs to be considered).

[0123] After receiving a request, the data analyst agent performs the following: executes the specified statistical analysis; identifies statistically significant correlation patterns; and constructs a response message containing the complete set of analysis results.

[0124] After receiving the analysis results, the chief scientist agent sends two types of task messages in parallel: a clinical interpretation request to the medical expert agent and an evidence retrieval request to the literature researcher agent.

[0125] Ultimately, the analysis results from each professional AI agent are integrated by the chief scientist AI agent to form a complete clinical laboratory correlation analysis report.

[0126] 1.4 Mechanism Advantages

[0127] This communication mechanism offers the following significant advantages in professional application environments: a. Parallel processing capability: Supports multi-agent parallel task execution, improving system processing efficiency; b. Communication traceability: All communication processes can be recorded and traced, ensuring the auditability of the analysis process; c. Precise task division: Each message contains a clear task definition and expected result; d. Dynamic resource allocation: The system can dynamically adjust the allocation of computing resources based on message priority; e. Fault tolerance and recovery mechanism: Built-in message persistence and retry mechanisms improve system stability.

[0128] Through the standardized communication architecture described above, each professional intelligent agent can execute tasks according to a strictly defined collaborative process, jointly completing the entire scientific research process from data analysis, correlation pattern discovery, hypothesis formation to multi-dimensional verification, ensuring that the medical value contained in clinical test data is fully and accurately mined and applied.

[0129] 2 Event-Driven Architecture

[0130] It adopts an event-driven architecture similar to the observer pattern in software design, which enables flexible task allocation and information sharing.

[0131] It should be noted that after the semantic index completes query parsing and path selection, it triggers multi-path parallel retrieval through an event publishing mechanism. The event scheduler dynamically allocates index resources based on query complexity, achieving intelligent coordination among the main index, category index, relation index, and semantic index. Simultaneously, a query performance feedback event loop is established to automatically optimize semantic expansion strategies and path weights. This mechanism upgrades the static semantic index to a dynamic responsive indexing system. While ensuring the accuracy of medical terminology understanding, it improves the response speed and adaptability of in vitro diagnostic knowledge retrieval through event-driven asynchronous parallel processing and real-time optimization learning.

[0132] 2.1 Key Components

[0133] This architecture is built upon the following key components:

[0134] Event Management Center: The central coordinator responsible for event registration, distribution, and lifecycle management;

[0135] Event definition guidelines: A standardized event type system that includes professional event categories related to clinical testing;

[0136] Subscription management mechanism: Supports agents to dynamically subscribe to and unsubscribe from specific event types;

[0137] Event propagation strategy: Define the priority, filtering conditions and propagation path of event transmission.

[0138] 2.2 Task Allocation Mechanism

[0139] The following key data attributes of clinical laboratory tests are analyzed and processed to achieve intelligent task allocation:

[0140] Attribute analysis of inspection items: Extract and process the following key information from the inspection item data:

[0141] Subject classification attribute: Determine the professional field to which the project belongs (biochemistry, immunology, hematology, etc.);

[0142] Measurement type classification: distinguishing between quantitative, semi-quantitative, and qualitative detection items;

[0143] Clinical relevance index: assesses the strength of the association between an item and a specific disease or physiological system;

[0144] Detection technology complexity: Analyzing the technical characteristics and complexity of the detection methods;

[0145] Data distribution characteristics: Analyze the statistical distribution characteristics of numerical items.

[0146] Event Triggering and Task Allocation Process: Based on the analysis of inspection project attributes, the system executes the following processing flow to achieve intelligent task allocation:

[0147] a. Data preprocessing stage: Structure the input test item data; extract potential correlation attributes between items; generate item feature vectors and relationship graphs.

[0148] Potential association attributes refer to implicit characteristic attributes in medical testing items that, although not explicitly labeled with direct relationships, may have clinically significant associations through multi-dimensional analysis. The system uses a triple-judgment framework based on biological mechanism association, clinical application association, and statistical association to encode 417 testing items with 40-dimensional feature vectors, covering classification features, clinical features, biological features, and statistical features. A comprehensive association scoring model is constructed through classification similarity calculation (weight 30%), clinical pathway overlap analysis (weight 25%), biological mechanism association assessment (weight 25%), and literature co-occurrence frequency statistics (weight 20%). A dynamic relationship graph based on NetworkX is established, integrating node centrality analysis, community detection algorithms, and path reasoning mechanisms to achieve intelligent prediction and verification of potential associations. Combined with an event-driven architecture, graph updates and propagation analysis events are automatically triggered when new relationships are discovered, dynamically optimizing the association discovery strategy.

[0149] b. Event type determination: Determine the event type in the professional field based on the project discipline classification; determine the analysis difficulty level based on the complexity of the relationship between projects; determine the priority label based on the clinical relevance.

[0150] The event type determination mechanism is based on a triple standardized mapping system: intelligent event type identification is achieved through a two-way mapping between eight professional domain event type matrices (clinical association, biological mechanism, laboratory association, diagnostic combination, surrogate indicator, sequential dependency, exclusion relationship, hierarchical inclusion) and the subject classification of laboratory items; a two-dimensional complexity calculation model is established based on network topological complexity (node ​​degree, clustering coefficient, path length) and semantic complexity (professional term density, text length, keyword frequency), and the analysis difficulty level is automatically determined by complexity thresholds (simple <0.4, medium 0.4-0.7, complex >0.7); a weighted calculation model of a four-dimensional clinical association assessment framework (diagnostic value association, disease co-occurrence, clinical decision association, patient management relevance) is constructed (weights are 0.4, 0.3, 0.2, 0.1 respectively), and task processing priorities are dynamically allocated according to clinical association thresholds (high ≥0.8, medium 0.6-0.8, low <0.6).

[0151] c. Event publishing process: The chief scientist agent acts as the initial event publisher, generating a task description event; the event management center receives the event and adds metadata tags; intelligent routing decisions are made based on event characteristics.

[0152] By extracting semantic features (complexity, domain category, priority) through a target parsing engine, structured task description events are automatically generated, including task types (hypothesis generation, validation analysis, literature search, etc., 11 categories), input data specifications, metadata tags, and execution constraints. The event management center employs an intelligent routing decision algorithm based on a task type-agent capability matching matrix (8×11-dimensional responsibility matrix) and a dynamic load balancing strategy. It achieves optimal agent allocation through task complexity assessment (text length, terminology density, keyword matching), agent capability scoring (professional matching degree, historical success rate, current load), and routing priority calculation (task urgency, resource availability, dependencies). Simultaneously, an event lifecycle management mechanism is established to support task decomposition, parallel scheduling, status monitoring, and result aggregation, ensuring efficient collaborative processing of complex medical diagnostic tasks.

[0153] d. Agent response mechanism: Agents with corresponding professional capabilities receive matching event notifications; they decide whether to respond based on their own capability assessment and current load status; the response decision is communicated to the event management center via a reverse event notification.

[0154] A comprehensive response decision score is generated through capability matching assessment (professional domain matching, responsibility level weight, historical success rate), load status analysis (current task queue length, resource utilization, processing capacity margin), task priority judgment (urgency, complexity level, dependencies), resource constraint check (memory usage threshold, API call limit, concurrent task limit), and response cost-benefit calculation (expected benefits, execution costs, opportunity costs). When the comprehensive score exceeds the agent's preset response threshold (default 0.6) and meets resource constraints, the agent automatically accepts the task and sends a response confirmation to the event management center through a reverse event notification mechanism, including acceptance status, estimated completion time, resource requirements, and confidence assessment. At the same time, a dynamic load balancing and degradation response mechanism is established to automatically adjust the response threshold and task allocation strategy when the system load is too high, ensuring the priority processing of critical tasks and system stability.

[0155] Figure 3 The system demonstrates the complete workflow from user submission of research objectives to final task completion, including three main stages: data analysis, hypothesis generation, and iterative validation. Through multiple rounds of validation and optimization, the system ensures the reliability of the generated scientific hypotheses.

[0156] 2.3 Dynamic Task Allocation Strategy

[0157] Allocation strategies include:

[0158] The dynamic task allocation strategy is a high-level implementation based on the event-driven architecture (2.1) and task allocation mechanism (2.2). The event management center in 2.1 is responsible for receiving and distributing task description events, and the task allocation mechanism in 2.2 performs structured processing on basic parameters to generate feature vectors and relationship graphs. The dynamic task allocation strategy, based on these preprocessed results, combines professional matching strategies, complexity matching strategies, evidence requirement strategies, and verification difficulty strategies to achieve dynamic allocation of agents. The system dynamically adjusts task allocation priorities through agent capability assessment and real-time monitoring of current load status, ensuring that the most suitable agent handles the corresponding professional task, forming a complete closed loop from event triggering and task generation to intelligent allocation.

[0159] Professional matching strategy: Liver function related test items (ALT, AST, etc.) trigger the response of the liver disease expert role in the medical expert agent;

[0160] Complexity matching strategy: High-complexity correlation analysis (such as electrolyte balance network) triggers collaborative analysis by multiple agents;

[0161] Evidence requirement strategy: Project association hypotheses with low evidence support trigger in-depth literature searches by literature researchers;

[0162] Evidence support was determined using a multi-agent confidence assessment system, employing a weighted evaluation mechanism of four specialized agents. The system established clear threshold standards: high evidence support (≥0.8) indicates consistent evaluation results from multiple agents with high confidence; moderate evidence support (0.6-0.8) indicates partial agent support; and low evidence support (<0.6) indicates discrepancies or insufficient confidence in the evaluation results. The specific determination process included: a clinical relevance agent (weight 1.2) evaluating from a clinical application perspective; a scientific evidence agent (weight 1.1) assessing the strength of literature support based on literature database searches; a biological mechanism agent (weight 1.0) evaluating from the perspective of metabolic pathways and molecular mechanisms; and a classification matching agent (weight 0.8) providing a basic similarity score. Finally, a comprehensive evidence support score was calculated using a weighted average formula and consistency level.

[0163] Verification difficulty strategy: Innovative hypotheses trigger experiments; designers and agents plan verification schemes.

[0164] In S4, it is assumed that the production and validation analysis process can be as follows:

[0165] Based on the agent's processing results, hypotheses about the item to be analyzed are generated. For example:

[0166] Hypothesis 1: The abnormally high white blood cell count in sample 3 may indicate an infection.

[0167] Hypothesis 2: Sample 3 has a normal hemoglobin level but an abnormal white blood cell count, which may be due to a bacterial infection rather than a viral infection.

[0168] The hypothesis generation and validation engine is used to validate and analyze the hypothesis content. Validation methods may include:

[0169] Comparison with historical data: Check whether the detection indicators of sample 3 are similar to the historical data of known infected cases.

[0170] Combine other test results: If sample 3 also underwent other relevant tests (such as C-reactive protein test), combine these results for comprehensive analysis.

[0171] Expert knowledge verification: The reasonableness of the hypothesis is evaluated based on the experience and knowledge of domain experts.

[0172] Based on the results of the validation analysis, the correlation analysis results of the items to be analyzed are obtained. For example:

[0173] Analysis results: Sample 3 had an abnormally high white blood cell count. Combined with other test results and expert knowledge, it was confirmed that there was a risk of infection, which was likely a bacterial infection.

[0174] Output the final correlation analysis results, including the hypotheses, validation process, and conclusions. For example:

[0175] Hypothesis: Sample 3 may be infected with bacteria.

[0176] Validation process: Compare with historical bacterial infection case data, combined with C-reactive protein test results (elevated).

[0177] Conclusion: Sample 3 has a risk of bacterial infection, and further diagnosis is recommended.

[0178] In another embodiment of this solution, it is assumed that the production and verification engine is specifically as follows:

[0179] 1. Hypothesis Generation and Verification Process

[0180] The hypothesis generation and validation engine employs an innovative generation-evaluation-validation process. Hypothesis generation utilizes a hybrid approach combining template-based and rule-based methods with deep learning, enabling the generation of hypotheses that align with medical logic.

[0181] The system employs a hybrid approach based on templates and rules to generate hypotheses that conform to medical logic. Specifically, the implementation includes: First, a pre-built medical knowledge template library containing common clinical hypothesis patterns such as "significantly higher ALT than AST and normal GGT suggest an increased likelihood of non-alcoholic fatty liver disease"; second, a hierarchical concept classification system based on the medical ontology mapping layer ensures that the medical concepts in the hypotheses conform to standard terminology; third, verification is performed through medical reasoning rules at the reasoning rule layer, including multi-dimensional checks on the rationality of physiological mechanisms, pathological associations, and clinical applications; finally, the system leverages the natural language generation capabilities of a large language model to generate grammatically correct and logically sound medical hypotheses under professional constraints. The system also implements a loop optimization mechanism, using multi-agent collaboration to evaluate and improve the generated hypotheses in real time.

[0182] The verification engine integrates statistical analysis, knowledge reasoning, and literature analysis modules to evaluate the reliability of hypotheses from multiple perspectives.

[0183] The system achieves multi-faceted reliability assessment through three core modules of its hypothesis generation and validation engine. The statistical analysis module, from a data-driven perspective, employs methods such as correlation analysis, group comparison, and multivariate analysis to calculate statistical significance (p-value), effect strength (correlation coefficient, hazard ratio), and predictive ability (area under the ROC curve), generating a statistical support strength score from 0 to 100. The knowledge reasoning module, from a theoretical perspective, evaluates the physiological mechanism rationality, pathological correlation rationality, and clinical application rationality of the hypothesis based on a medical ontology network and reasoning rule base, calculating a theoretical rationality score. The literature analysis module, from an evidence-based medicine perspective, retrieves hypothesis-related literature from databases such as PubMed through an intelligent retrieval system, assessing the quantity, quality, consistency, and timeliness of evidence, calculating an evidence strength score. Finally, through a comprehensive evaluation mechanism, using Bayesian uncertainty representation and sensitivity analysis, a comprehensive reliability score including confidence intervals is generated.

[0184] 2. Statistical Analysis Module

[0185] The statistical analysis module is responsible for assessing the statistical support strength of hypotheses based on actual clinical data, enabling automated, multi-level data validation.

[0186] 2.1 Core Analysis Components

[0187] Implementation technology:

[0188] Correlation analysis engine: Implements various correlation calculation methods (Pearson, Spearman, partial correlation, etc.);

[0189] Group comparison system: Automatically performs t-tests, ANOVA, non-parametric tests, and other inter-group difference analyses;

[0190] Multivariate analysis tools: enable advanced statistical methods such as regression analysis, principal component analysis, and factor analysis;

[0191] Time series pattern analyzer: Identifies the time series relationships between test indicators.

[0192] Processing flow: Receive hypothesis content, parse the relevant test items and relationship types; automatically select appropriate statistical methods and data preprocessing steps; perform statistical calculations and generate results reports; evaluate statistical significance and effect size.

[0193] The system automatically selects statistical methods through intelligent decision trees and data feature analysis. First, the data preprocessing stage automatically detects data type (continuous, categorical, count), distribution characteristics (normality test, skewness and kurtosis analysis), and data quality (missing value ratio, outlier detection) to determine the preprocessing strategy. Then, based on the pairing relationships of the test items and the research objectives, the system automatically selects appropriate statistical methods: Pearson correlation coefficient or Spearman rank correlation is used for correlation analysis of continuous variables; t-tests, ANOVA, or nonparametric tests are used for inter-group comparisons; and linear regression, logistic regression, or principal component analysis is used for multivariate relationships. The system has a built-in rule base for the applicability of statistical methods. Combining sample size, data distribution, and hypothesis testing requirements, the rule engine automatically matches the optimal statistical analysis scheme and provides detailed explanations and rationale for the method selection.

[0194] 2.2 Advanced Validation Functionality

[0195] Implementation method:

[0196] Data stratification analysis: Subgroups were formed based on patient characteristics (age, gender, disease type, etc.) to verify the stability of the relationships;

[0197] Advanced validation functionality is a core component and extension of the statistical analysis module. While the statistical analysis module provides basic data analysis capabilities, advanced validation adds robustness validation, sensitivity analysis, predictive model validation, and cross-validation. Specifically, the correlation is as follows: Stratified data analysis leverages the statistical analysis module's grouping comparison capabilities to validate stability across different patient subgroups; sensitivity analysis utilizes the module's core algorithms to assess robustness by adjusting parameter settings; predictive model validation uses the module's multivariate analysis capabilities to build and evaluate predictive model performance; and cross-validation employs the module's algorithmic framework to implement k-fold validation to prevent overfitting. The evaluation metrics for advanced validation (statistical significance, effect strength, predictive power, and reproducibility) directly depend on the calculation results of the statistical analysis module, forming a complete statistical evaluation system from basic analysis to advanced validation.

[0198] Sensitivity analysis: The robustness of the results is assessed by adjusting data screening criteria and outlier handling methods;

[0199] The system evaluates the statistical analysis results of the associations between test items, including correlation coefficients, difference test results, and predictive model parameters. Results stability validation is achieved through Bootstrap resampling, subset validation, and parameter sensitivity testing: the Bootstrap method performs sampling with replacement on the original data, calculates the statistical results for each iteration, and assesses stability using confidence intervals of the result distribution; subset validation splits the data by time, region, or other dimensions, repeats the analysis on different subsets, and compares the consistency of results; parameter sensitivity testing observes the magnitude of changes in results by adjusting outlier handling thresholds and missing value handling methods. Relationship stability assessment targets the statistical associations between test items, such as the correlation between ALT and AST, and the diagnostic value of the AST / ALT ratio. Validation is conducted through multiple data sources, multiple time periods, and multiple populations to ensure that the discovered associations remain consistent under different conditions, demonstrating good generalization ability and clinical applicability.

[0200] Predictive model validation: Construct a predictive model based on the assumed relationships and evaluate its predictive performance;

[0201] Cross-validation mechanism: Implement k-fold cross-validation to prevent overfitting and random associations.

[0202] Cross-validation is performed on predictive models built based on hypothetical relationships, including diagnostic prediction models, risk assessment models, and association strength prediction models. The system employs k-fold cross-validation (typically k = 5 or 10), randomly partitioning the dataset into k subsets. Each time, k-1 subsets are used to train the model, and one subset is used to validate performance. This process is repeated k times, and the average value is taken. Validation targets include: disease diagnostic models based on the association relationships of test items, risk stratification models based on combinations of multiple indicators, and test result prediction models based on historical data.

[0203] The final result of the advanced validation function is the generation of a relational reliability report that includes confidence assessments, statistical support scores (0-100), model performance metrics (sensitivity, specificity, AUC), and robustness assessment results. These results directly provide quantitative evidence for the knowledge reasoning module described below. The knowledge reasoning module combines statistical validation results with medical expertise to conduct a theoretical rationality assessment, forming a comprehensive evaluation system that combines statistical validation with professional validation.

[0204] 2.3 Evaluation Index System

[0205] Key metrics: Statistical significance: Calculate the p-value and apply multiple comparison correction; Effect strength: Assess the correlation coefficient, hazard ratio, or odds ratio; Predictive power: Calculate the area under the ROC curve, sensitivity, and specificity; Reproducibility: Validate the stability of the estimation results using a subset of data. Scoring method: Construct a statistical support strength score from 0 to 100 based on the statistical indicators; quantify uncertainty and provide confidence intervals and estimates of score reliability.

[0206] Example Application: When validating the hypothesis that "AST / ALT ratio can distinguish between alcoholic and non-alcoholic fatty liver disease," the system automatically performs the following steps: extracts patients diagnosed with alcoholic and non-alcoholic fatty liver disease from patient data; calculates and compares the AST / ALT ratios of the two groups (mean 1.42 vs 0.78, p<0.001); performs ROC analysis to determine the optimal cutoff value (>1.0, sensitivity 82.3%, specificity 75.8%); verifies the stability of the results through Bootstrap resampling; and generates a statistical support score (85 points) and confidence interval.

[0207] 3. Knowledge Reasoning Module

[0208] The knowledge reasoning module, based on the theoretical rationality of the assumptions for assessing medical expertise, achieves automated medical logic verification.

[0209] 3.1 Knowledge Representation System

[0210] Implementation technology:

[0211] Medical Ontology Network: Integrates standard medical ontology such as SNOMED-CT and UMLS;

[0212] Domain knowledge graph: Construct a professional knowledge graph for laboratory medicine to represent the relationships between entities;

[0213] Reasoning rule base: Encodes causal relationships in physiology and pathology and professional reasoning rules;

[0214] Knowledge organization: Establish a hierarchical knowledge structure, including anatomical knowledge, physiological relationships, pathological mechanisms, etc.; label knowledge elements with credibility levels and evidence sources; and implement knowledge version control and update mechanisms.

[0215] It is important to emphasize that the medical ontology network, domain knowledge graph, inference rule base, and knowledge organization constitute a hierarchical and progressive medical knowledge representation architecture. The medical ontology network, as the foundational layer, provides standardized mappings of medical concepts and terminology. The domain knowledge graph builds an entity relationship network on top of the ontology, using a graph database to store multidimensional relationships between test items. The inference rule base encodes medical reasoning logic and causal relationships based on the relationship patterns in the knowledge graph. The knowledge organization layer uniformly manages the knowledge resources of the first three layers, enabling version control and dynamic updates. The hierarchical knowledge structure establishment process is as follows: First, the terminology mapping engine maps test items to standard medical ontology, establishing a hierarchical classification of concepts. Then, based on the NetworkX graph structure, a knowledge graph is constructed with test items as nodes and relationships as edges, each edge containing attributes such as relationship type, strength level, and evidence source. Next, inference rules are extracted from clinical guidelines and literature to form executable inference chains. Finally, the knowledge management system achieves unified indexing, retrieval, and maintenance of the four-layer architecture, supporting semantic similarity retrieval, relationship network traversal, and intelligent reasoning queries.

[0216] 3.2 Inference Engine Implementation

[0217] The connection between the inference engine and the preceding text is reflected in the following: it directly calls the standardized concepts of the medical ontology mapping layer for concept parsing, utilizes the relational paths of the relational network layer for inference navigation, and performs logical verification based on the encoding rules of the inference rule layer. The inference process begins with parsing the medical concepts in the hypothesis, searches for relevant concepts and known relationships in the knowledge base, applies inference rules to verify the consistency between the hypothesis and existing knowledge, identifies supporting and opposing knowledge paths, and finally calculates a knowledge support strength score. This process realizes the transformation from structured knowledge to intelligent reasoning, providing theoretical support based on professional knowledge for hypothesis testing.

[0218] Core technologies:

[0219] Rule-based reasoning: applying medical rules to perform forward and backward chained reasoning;

[0220] Case-based reasoning: comparing similar hypotheses that have been validated in the past;

[0221] Semantic similarity reasoning: calculating the semantic fit between hypotheses and existing knowledge; Probabilistic reasoning network: handling uncertainty and probabilistic relationships in the medical field.

[0222] Reasoning Process: Analyze the medical concepts and relational assertions in the hypothesis (a relational assertion is a mechanism used in databases to define and enforce data integrity. It ensures the consistency and accuracy of related data by specifying constraints between two or more tables. Relational assertions are commonly used to ensure the correctness of foreign key constraints and prevent invalid data relationships); search for relevant concepts and known relationships in the knowledge base; apply inference rules to verify the consistency between the hypothesis and known knowledge; identify knowledge paths that support and oppose the hypothesis; calculate the knowledge support strength score.

[0223] Known relationships refer to relationships between laboratory test items that are clearly recorded and widely recognized in authoritative clinical guidelines, standardized clinical pathways, and evidence-based medicine literature. The system uses a three-tiered classification standard for definition: Level 1 relationships (strength ≥ 0.8) originate from authoritative international guidelines such as WHO, ADA, and ESC, and Level A evidence-based evidence, such as the ALT-AST liver function combination relationship; Level 2 relationships (strength 0.6-0.79) are based on professional consensus and Level B evidence, such as the uric acid-blood urea nitrogen renal function combination relationship; Level 3 relationships (strength 0.4-0.59) originate from clinical practice experience and Level C evidence. Specific definition methods include: automatically identifying relationship pattern terms such as "aided diagnosis," "monitoring," and "assessment" from clinical guideline documents using NLP technology; verifying the clinical application scenarios of relationships using a disease-laboratory mapping knowledge base; statistically analyzing the frequency and support strength of relationships in high-quality studies through literature mining; and verifying the application of relationships in standardized diagnostic and treatment processes using clinical pathway templates. The system has established a database of known relationships that includes different disease categories and different levels of evidence. Each relationship is labeled with its source, level of evidence, confidence level, and scope of application, ensuring that the reasoning process is based on a reliable medical knowledge base.

[0224] 3.3 Reasonableness Assessment System

[0225] Evaluation dimensions:

[0226] Physiological mechanism rationality: Does the hypothesis conform to known physiological principles?

[0227] Reasonableness of pathological association: Does the hypothesis align with the disease mechanism?

[0228] Clinical application rationality: Does the hypothesis have clinical application value?

[0229] Conflict detection: whether the hypothesis conflicts with existing consensus or basic principles.

[0230] Scoring method: Construct a theoretical rationality score of 0-100 points based on multi-dimensional assessment; mark the certainty level and knowledge coverage of the assessment; provide detailed supporting and opposing reasons.

[0231] Example Application: When evaluating the AST / ALT ratio hypothesis, the knowledge reasoning module performs the following steps: extracting key concepts (AST, ALT, ratio relationship, alcoholic liver disease, non-alcoholic fatty liver); retrieving relevant physiological knowledge (AST is mainly distributed in mitochondria, ALT is mainly in the cytoplasm); applying pathological rules (mechanisms of mitochondrial damage by alcohol metabolites); verifying the completeness and logic of the causal chain; and generating a theory support score and explanatory report.

[0232] 4. Literature Analysis Module

[0233] The literature analysis module assesses the strength of evidence supporting hypotheses through automated literature retrieval and analysis, thus achieving evidence-based objective verification.

[0234] 4.1 Document Retrieval System

[0235] Core components:

[0236] Intelligent query builder: Transforms assumptions into structured retrieval strategies;

[0237] Multi-source search engine: integrates interfaces with medical databases such as PubMed, Scopus, and Cochrane;

[0238] Search optimizer: Automatically adjusts search terms and filtering conditions to improve relevance.

[0239] Search process: Analyze the hypothesis content and extract key concepts and relationships; construct professional search expressions, including subject terms and free terms; apply filtering conditions (research type, publication time, etc.); perform parallel search and merge deduplicated results.

[0240] 4.2 Literature Content Analysis

[0241] Core technologies:

[0242] Natural Language Processing: Extracting key conclusions and data from literature;

[0243] Evidence classification system: classifying evidence according to research type and methodological quality;

[0244] Conclusion extractor: Identifies specific statements that support or oppose the hypothesis;

[0245] Data integration and analysis: Meta-analysis is performed by integrating data from multiple documents.

[0246] Processing flow: Obtain the full text or structured abstract of the literature; apply NLP technology to extract key information such as research design, methods, and results; identify specific conclusions related to the hypothesis; assess the level of evidence and methodological quality of each literature; quantify the degree of support or opposition.

[0247] 4.3 Assessment of the strength of evidence

[0248] Evaluation dimensions:

[0249] Quantity of evidence: The total number of relevant studies;

[0250] Quality of evidence: Research design type and methodological rigor;

[0251] Consistency: The degree of agreement between the conclusions of different studies;

[0252] Timeliness: The degree to which the publication date of the research aligns with the latest medical advancements.

[0253] Scoring method: Each piece of evidence is evaluated using the evidence level classification system (levels 1a-5); a weighted evidence support score (0-100 points) is calculated; and an evidence graph is generated to visually display the distribution of supporting and opposing evidence.

[0254] Example Application: When evaluating the AST / ALT ratio hypothesis, the literature analysis module performs the following steps: Constructing a search strategy ("AST / ALT ratio" AND ("alcoholic liver disease" OR "non-alcoholic fatty liver")); retrieving 23 relevant articles from the database; analyzing the conclusions and level of evidence for each article (5 systematic reviews / meta-analyses, 8 randomized controlled trials, and 10 observational studies); extracting key findings (19 supporting the hypothesis, 4 proposing constraints); and calculating the weighted evidence score (82 points) and consistency index.

[0255] 5. Comprehensive evaluation mechanism

[0256] The system integrates the results of multiple modules into a final hypothesis reliability score through an innovative multi-dimensional integrated evaluation mechanism.

[0257] 5.1 Multi-dimensional assessment integration

[0258] Implementation method:

[0259] Weighted average model: dynamically adjusts the weights of each dimension based on the type of assumption;

[0260] Evidence cascading mechanism: requires key dimensions to reach a minimum threshold;

[0261] Complementary enhancement rule: Identify complementary and enhancing relationships between different dimensions.

[0262] Calculation process: Obtain the scores and uncertainty estimates of the three modules; calculate the weighted average score by applying the assumption type-specific weights; check whether the minimum threshold requirement is met; apply adjustment rules to handle special cases.

[0263] 5.2 Quantification of Uncertainty

[0264] Implementation technology:

[0265] Bayesian uncertainty representation: quantifying the confidence intervals of each evaluation dimension;

[0266] Sensitivity analysis: assessing the impact of weight changes on the final score;

[0267] Completeness of evidence index: measures the sufficiency of available information.

[0268] Application methods: Provide confidence intervals or reliability levels for the final score; identify major sources of uncertainty in the assessment; and provide recommendations for improving the reliability of the assessment.

[0269] 5.3 Evaluation Report Generation

[0270] Overall rating and grading: Overall assessment of the reliability of the assumptions;

[0271] Dimensional decomposition analysis: detailed scores and basis for each evaluation dimension;

[0272] Evidence summary: A summary of key supporting or opposing evidence.

[0273] Improvement suggestions: Suggestions for improvement regarding the hypothesis itself or the verification method.

[0274] Application Case: Comprehensive Assessment of the AST / ALT Ratio Hypothesis: Statistical Analysis Score: 85 (weight 0.35); Knowledge Reasoning Score: 90 (weight 0.30); Literature Analysis Score: 82 (weight 0.35); Weighted Composite Score: 85.55. Overall Rating: Grade A (High Reliability). Key Findings: High clinical application value, but may have specific limitations in advanced cirrhosis. Recommendation: Further validation of its applicability in patients with cirrhosis of different etiologies.

[0275] Through the above multi-dimensional and modular hypothesis testing system, the clinical laboratory correlation analysis system can objectively and comprehensively assess the reliability of medical hypotheses, provide high-quality scientific evidence for clinical decision-making, and at the same time take into account the unique complexity and uncertainty of medicine, realizing a complete value transformation chain from data to knowledge to clinical application.

[0276] Furthermore, the pre-defined structured message passing mechanism is as follows:

[0277] Determine whether there is an information exchange request between any two intelligent agents, and if the determination result is yes, send a standardized structured message body to the central routing system;

[0278] The standardized structured message body is formatted and its legality is checked through a central routing system.

[0279] When both format validation and legality checks pass, the message data in the information exchange request is parsed and processed to generate a processing result;

[0280] And return the processing result according to the path in the information exchange request.

[0281] Furthermore, the process of generating task instructions for different intelligent agents based on the basic parameters is as follows:

[0282] The basic parameters are structured to generate feature vectors and relationship graphs;

[0283] Based on feature vectors and relationship graphs, priority marking of association degree is performed;

[0284] Based on the correlation priority labeling results, task description events are generated, and task description events are distributed based on the event management center to generate task instructions for different intelligent agents.

[0285] Furthermore, the pre-built knowledge management module specifically includes:

[0286] The medical ontology mapping layer, relational network layer, semantic vector layer, and inference rule layer;

[0287] The medical ontology mapping layer is used to construct a hierarchical concept classification system and perform semantic association on the input data;

[0288] The relational network layer is used to construct a relational path network based on semantic association results;

[0289] The semantic vector layer is used to transform the clinical laboratory test items to be analyzed into vectors and determine the semantic similarity between vectors;

[0290] The inference rule layer is used to perform implicit inference on the input data and obtain the inference result;

[0291] Output the relational path network, semantic similarity, and inference results.

[0292] Furthermore, let's assume the production and validation engine specifically includes:

[0293] Statistical analysis module, knowledge reasoning module, and literature analysis module;

[0294] By combining the statistical analysis module, knowledge reasoning module, and literature analysis module with a comprehensive evaluation mechanism, the reliability score results corresponding to the input data are determined.

[0295] The statistical analysis module is used to: parse the hypotheses, select the corresponding processing strategy based on the analysis results, process the data according to the processing strategy, and generate a results report;

[0296] The knowledge reasoning module is used to: parse the medical concepts and relational assertions corresponding to the hypothetical content, determine the known relations corresponding to the medical concepts and relational assertions through the pre-built knowledge management module, verify the consistency between the hypothetical content and the known relations based on the preset reasoning rules, and determine the theoretical rationality score corresponding to the hypothetical content in conjunction with the rationality assessment system.

[0297] The literature analysis module is used to: construct search expressions based on hypothetical content, perform searches based on search expressions, deduplicate search results to obtain literature data, process the literature data using NLP technology, determine conclusion data related to the hypothetical content, assess the level of evidence for the literature data corresponding to the conclusion data, and determine the strength of evidence score based on the level of evidence assessment results.

[0298] Furthermore, it also includes:

[0299] The process of correlation analysis is monitored in real time using strategies, including: monitoring within agents, monitoring between agents, monitoring data processing nodes, monitoring access functions, and monitoring the acquisition layer.

[0300] In another embodiment of this solution, the monitoring strategy, i.e., the system stability guarantee mechanism, specifically includes:

[0301] To ensure system stability, an agent activity monitoring and anomaly handling mechanism has been implemented, capable of detecting and handling potential errors and inconsistent behaviors of agents. Our system's stability assurance mechanism is a multi-layered, end-to-end monitoring and anomaly handling architecture, implementing differentiated monitoring strategies and handling schemes based on the characteristics of each node in the system.

[0302] 1. Internal monitoring mechanism of the intelligent agent

[0303] 1.1 Monitoring of the reasoning process

[0304] The object of monitoring is the internal reasoning logic and decision-making process of the intelligent agent.

[0305] Implementation: Set logical checkpoints at key decision points to verify the rationality of intermediate results; introduce an uncertainty quantification mechanism to mark reasoning steps with low confidence; and establish a decision trajectory recording system to record the complete reasoning path.

[0306] Key decision points refer to important judgment nodes in the agent's reasoning process, including feasibility judgment of hypothesis generation, confidence judgment of evidence evaluation, applicability judgment of inference rules, and reliability judgment of the final conclusion. The system sets specific threshold standards in its decision framework: sensitivity threshold 0.8, specificity threshold 0.9, and confidence threshold 0.85. The rationality verification of intermediate results mainly targets: logical consistency checks during the reasoning process, confidence scores in multi-agent evaluation, evidence strength scores in literature analysis, and significance tests in statistical analysis. The verification mechanism verifies the rationality of each intermediate step by setting logical checkpoints, such as the statistical significance of correlation analysis, the medical logical rationality of hypothesis generation, and the rationality of weight allocation in evidence strength evaluation, ensuring that each link in the reasoning chain meets medical professional standards and statistical requirements.

[0307] The uncertainty quantification mechanism achieves confidence level labeling through a multi-level evaluation system. The specific process includes: First, the system uses Bayesian uncertainty representation to quantify the confidence interval for each inference step; second, a comprehensive confidence score is calculated using a confidence evaluator, combined with weighted evaluations from clinical relevance agents (weight 1.2), scientific evidence agents (weight 1.1), biological mechanism agents (weight 1.0), and classification matching agents (weight 0.8); third, the system sets uncertainty level standards: high certainty (≥0.8), moderate certainty (0.6-0.8), and low certainty (<0.6), and assigns corresponding expression methods and warning labels to each level; finally, through consistency level calculation and uncertainty factor identification, specific confidence values, uncertainty levels, recommended expression methods, and improvement suggestions are labeled for each inference step, forming a complete uncertainty quantification report.

[0308] The decision trajectory recording system achieves complete inference path recording through structured logs and state tracking mechanisms. Specific steps include: First, the system establishes a memory structure during coordinator initialization, containing key states such as research objectives, hypothesis lists, reflection records, ranking results, evolutionary processes, and verification results. Second, through custom print function overloads, all agent activities are marked as DEBUG level logs, chapter titles as INFO level logs, and error messages as ERROR level logs. Third, a unique task_id is assigned to each task, recording the task execution status, completion time, and processing results to form a complete task execution chain. Fourth, the message transmission process between agents is recorded through the message queue mechanism of the parallel executor, including message content, timestamps, sender, and receiver. Fifth, during the inference process, the input parameters, processing logic, output results, and confidence assessments of key decision points are recorded in real time, forming a traceable inference trajectory. Finally, a comprehensive report containing execution time, task statistics, number of hypotheses, and verification results is generated to ensure the complete auditability of the entire inference process.

[0309] Anomaly handling strategy: When a logical contradiction is detected, a self-correction process is triggered to re-evaluate the preconditions; secondary verification is performed on low-confidence conclusions, and assistance from other agents is requested when necessary; when a reasoning loop is detected, a preset exit strategy is applied and the state is recorded.

[0310] The preconditions for re-evaluation include failure of logical consistency checks, confidence scores below a preset threshold, significant discrepancies in agent evaluation results, and the discovery of contradictory evidence in external verification. Specifically, when a logical contradiction is detected, the system triggers a self-correction process to re-examine the underlying assumptions, the applicability of inference rules, and the completeness of the evidence chain. When the confidence score falls below a 0.6 threshold, the system requires a re-evaluation of evidence quality, adjustment of weight allocation, and addition of verification dimensions. When the multi-agent consistency level is below 0.5, the system initiates a conflict resolution mechanism to analyze the causes of discrepancies, reallocate tasks, and adjust the evaluation strategy. When literature analysis reveals contradictory high-quality evidence, the system re-evaluates the evidence level, adjusts the search strategy, and expands the literature scope. The re-evaluation process incorporates various strategies, including parameter adjustment, method improvement, data supplementation, and expert intervention, to ensure the reliability and consistency of the final results.

[0311] Specific situations requiring assistance include: insufficient professional capabilities, such as when a clinical pathologist agent encounters problems with molecular biological mechanisms and requests assistance from a biology expert; insufficient computing resources, such as when a single agent times out processing large-scale data analysis and requests parallel processing support; insufficient confidence, such as when a literature analysis agent assesses the confidence level of evidence for a hypothesis below 0.6 and requests secondary verification from a clinical expert; and conflicting results, such as when statistical analysis results contradict professional knowledge and requests a coordinator to resolve the conflict. A specific example: When verifying the hypothesis that "the AST / ALT ratio can distinguish between alcoholic and non-alcoholic fatty liver disease," the statistical analysis agent finds data supporting the hypothesis (p<0.001), but the medical expert agent points out that this ratio loses its specificity in the late stage of cirrhosis. In this case, the system automatically requests the literature researcher agent to search for evidence of relevant limiting conditions and requests the clinical pathologist agent to make a comprehensive judgment, ultimately forming a complete conclusion that includes applicable conditions and limiting factors.

[0312] Logical contradiction detection includes: semantic consistency checking, verifying whether medical concepts in the hypothesis conform to standard terminology; causal relationship verification, checking for circular causality or self-contradictory relationships in the reasoning chain; evidence conflict detection, identifying conflicting high-quality evidence in literature analysis; and numerical range verification, checking whether statistical results exceed the range of medical common sense. Reasoning loop detection is achieved through task state tracking: setting a maximum reasoning depth limit, automatically terminating when a preset number of levels is exceeded; monitoring message passing patterns between agents to identify repeated requests and response loops; recording reasoning path nodes and detecting whether they return to previously visited states; and setting a time threshold, triggering loop detection when a single reasoning step exceeds a preset time. When a logical contradiction is detected, the system applies a preset exit strategy: reverting to the most recent valid state, adjusting reasoning parameters, or requesting manual intervention; when a reasoning loop is detected, the system forcibly terminates the current reasoning path, records the loop state, and initiates alternative reasoning strategies.

[0313] 1.2 Resource Utilization Monitoring

[0314] Monitoring targets: the computing resources and memory usage of the intelligent agent.

[0315] Implementation: Real-time tracking of memory allocation and release, setting resource usage thresholds; monitoring processing time and identifying abnormally time-consuming operations; implementing priority management of computing tasks to prevent resource contention.

[0316] Anomaly handling strategy: When resource pressure is detected (below the threshold), the non-critical task suspension mechanism is activated; timeout operations are forcibly terminated and rolled back to a safe state; when resources remain scarce, a degradation processing mode is triggered to simplify computational complexity.

[0317] Non-critical tasks are defined based on a task priority evaluation system and system resource allocation strategy. The system categorizes tasks into three priority levels: critical tasks (priority ≥ 8) include core reasoning, hypothesis testing, and evidence evaluation—tasks that directly impact the analysis results; important tasks (priority 4-7) include supporting tasks such as literature retrieval, data preprocessing, and result presentation; and non-critical tasks (priority < 4) include auxiliary tasks such as log recording, cache clearing, performance statistics, and interface updates. When resources are scarce, the system employs a tiered processing strategy: first, suspending the execution of non-critical tasks to release computing resources and memory; then, reducing the processing frequency and depth of important tasks; and finally, ensuring the normal execution of critical tasks. Specific judgment criteria include: the degree of impact of the task on the final analysis results, the severity of the consequences of execution failure, the level of resource consumption, and time sensitivity. A multi-dimensional scoring model automatically determines the criticality level of a task.

[0318] 2. Inter-agent communication monitoring mechanism

[0319] 2.1 Message Integrity Monitoring

[0320] Monitoring object: Structured messages transmitted between intelligent agents.

[0321] Implementation methods: Real-time message structure verification to ensure conformity to predefined patterns (including but not limited to format and quantity); establishment of a message checksum mechanism to verify data integrity; monitoring of message size and complexity to prevent exceeding limits.

[0322] Error handling strategy: When a formatted error message is received, a structured error response is returned and logged; when an incomplete message is detected, a retransmission is requested or the message is restored to the most recent valid state; when the number of messages exceeds a preset threshold, message fragmentation is implemented or the message is rejected.

[0323] 2.2 Communication Timing Monitoring

[0324] Monitoring object: the time characteristics and sequential relationship of message transmission.

[0325] Implementation: Establish expected response time models for key processes; monitor message reception and processing time intervals; implement a message sequence number mechanism to track session integrity.

[0326] The definition of key processes is based on the core business links and system stability requirements in the agent collaboration process. Specific definition criteria include: business-critical processes, such as the complete reasoning chain of hypothesis generation-verification-ranking, the collaborative process of multi-agent confidence assessment, and the cross-validation process of literature analysis and knowledge reasoning; performance-sensitive processes, such as parallel task allocation and execution, batch operations in large-scale data processing, and real-time collaboration conflict resolution mechanisms; and resource-intensive processes, such as meta-summary generation, complex statistical analysis, and large language model inference calls. The expected response time model serves to establish a performance benchmark and anomaly detection mechanism for inter-agent communication. It establishes the expected response time distribution for different types of messages through historical data statistical analysis, monitors in real-time whether current communication exceeds the expected range, and promptly identifies communication bottlenecks and system anomalies. Model inputs include: message type (task allocation, result feedback, collaboration request, state synchronization), message complexity (data volume, processing difficulty level), system load status (CPU utilization, memory usage, number of concurrent tasks), and network environment parameters (bandwidth, latency, packet loss rate). The model output includes: expected response time range (normal range, warning threshold, abnormal threshold), confidence assessment (prediction accuracy score), anomaly markers (timeout risk, performance degradation warning), and optimization suggestions (load balancing adjustment, task priority reordering, resource expansion suggestions). The system continuously updates model parameters using a sliding window statistical method to ensure the accuracy and timeliness of the expected response time model.

[0327] Anomaly handling strategy: When a response timeout is detected, initiate a retry mechanism or query the target agent's state; when message duplication is detected, implement idempotent processing to ensure state consistency; when message out-of-order is detected, cache and rearrange the messages or request resynchronization.

[0328] The idempotent processing mechanism ensures consistency of repeated operations through message deduplication and state verification. The specific steps include: First, message identification and state checking: assigning a unique identifier to each message and checking if it has already been processed; Second, state verification: comparing the current system state with the expected message state to determine if the operation needs to be executed; Third, conditional execution: executing the operation only when the states are inconsistent to avoid side effects caused by duplicate processing; Fourth, result confirmation: recording the operation completion status and updating the message processing record; Fifth, exception handling: returning the previous processing result when a duplicate message is found to ensure the caller receives a consistent response. The system achieves idempotency during message passing by tracking session integrity through a message sequence number mechanism, using state caching to avoid duplicate calculations, and establishing an operation log to record historical processing results, ensuring that even in abnormal situations such as network retries or task restarts, the same input always produces the same output, maintaining the consistency of the system state.

[0329] 3. Data processing node monitoring mechanism

[0330] 3.1 Data Quality Monitoring

[0331] Monitoring objects: the quality characteristics of input data and intermediate processing results.

[0332] Implementation: Real-time verification of data to ensure it conforms to the expected statistical distribution and range; monitoring of the changing trends of key data indicators and identification of abnormal fluctuations; application of professional rules to verify clinical laboratory data.

[0333] Key data definition is based on the medical significance and statistical characteristics of clinical laboratory data. Specifically, it includes: First, clinical indicator data, involving core diagnostic information such as the numerical range, reference interval, and outlier thresholds of laboratory items; second, data quality indicators, including the proportion of missing values ​​(normal <5%, warning 5-10%, outlier >10%), the proportion of outliers (normal <2%, warning 2-5%, outlier >5%), and data distribution characteristics; third, processing indicators, involving intermediate results such as the effectiveness of data preprocessing and cleaning, standardization results, and correlation coefficients from association analysis; and fourth, system performance indicators, including operational status data such as memory usage (warning threshold 80%), processing time, and task completion rate. The monitoring mechanism verifies in real-time that the data conforms to the expected statistical distribution and range, tracks the changing trends of laboratory item values, identifies abnormal fluctuations exceeding medical norms, and applies professional rules to verify the rationality of clinical laboratory data, ensuring the quality of input data and the stability of system processing.

[0334] Anomaly handling strategy: When an outlier is detected, apply data cleaning rules or request manual confirmation; when changes in data structure are identified, adjust the processing flow and record the reasons for the adjustment; when data is incomplete, assess the impact and decide whether to continue processing or stop.

[0335] 3.2 Algorithm Stability Monitoring

[0336] Monitoring target: The operational status of data analysis and statistical models.

[0337] Implementation: Monitor algorithm convergence metrics to ensure stable computation; track the rationality and consistency of intermediate results; establish algorithm performance benchmarks and compare current performance in real time.

[0338] The algorithm convergence index system includes three dimensions: numerical convergence, stability convergence, and functional convergence. Numerical convergence indices include: iterative error rate of change (the magnitude of change in the objective function value between consecutive iterations), convergence threshold check (convergence is determined when the rate of change is less than a preset threshold 1e-6), and parameter stability (the degree of change in model parameters during consecutive iterations). Stability convergence indices include: result consistency (the difference in results from multiple runs of the same algorithm), confidence interval stability (whether the statistically estimated confidence interval tends to be stable), and gradient norm (whether the magnitude of the gradient vector in the optimization algorithm approaches zero). Functional convergence indices include: performance index stability (convergence of evaluation indices such as the area under the ROC curve and correlation coefficient), cross-validation stability (the variance of k-fold validation results), and prediction accuracy convergence (the trend of change in model prediction error). By monitoring the change patterns of these indices, the system automatically determines whether the algorithm has reached convergence, avoiding over-iteration or premature stopping.

[0339] The tracked intermediate results cover the key outputs of each processing stage in the multi-agent collaboration process. Specifically, these include: intermediate results from the data preprocessing stage, such as the number of valid records after data cleaning, parameters for standardization, and outlier detection results; intermediate results from the feature engineering stage, such as the dimension of feature vector generation, the number of nodes and edges in the relational graph construction, and the calculation results of the correlation matrix; intermediate results from the agent reasoning stage, such as the confidence scores of each specialized agent, intermediate nodes in the reasoning path, and the graded evaluation of evidence strength; intermediate results from the verification analysis stage, such as the p-value and effect size of statistical tests, the evidence level distribution of literature analysis, and the consistency check results of knowledge reasoning; and intermediate results from the collaboration and coordination stage, such as the success rate of message transmission, the load balancing status of task allocation, and the mediation effect of conflict resolution. By recording and verifying the rationality and consistency of these intermediate results in real time, the system ensures that each stage of the entire analysis process operates within the expected range, providing detailed process data for problem diagnosis and quality control.

[0340] The algorithm performance benchmarking employs a multi-dimensional benchmarking and dynamic comparison mechanism. The specific process includes: Phase 1: Benchmarking establishment. A standard test dataset (containing known correlations of test items) is used for benchmarking, recording core metrics such as accuracy, recall, F1 score, and processing time to establish a performance benchmark library. Phase 2: Real-time monitoring. Performance metrics data is continuously collected during system operation, calculating key indicators such as processing efficiency, accuracy, and resource consumption for the current batch. Phase 3: Comparative analysis. Current performance metrics are compared with historical benchmarks in real time, calculating the percentage performance deviation and identifying performance degradation trends. Phase 4: Early warning mechanism. Performance warnings are triggered when performance metrics deviate from the benchmark by more than preset thresholds (accuracy decrease >5%, processing time increase >20%, memory usage increase >30%). Phase 5: Adaptive adjustment. Algorithm parameters are automatically adjusted, resource allocation is optimized, and alternative strategies are switched based on performance comparison results. The system also includes a performance trend analysis module, which uses sliding window statistics and trend prediction to proactively identify performance degradation risks and optimize accordingly.

[0341] Anomaly handling strategy: When the algorithm fails to converge, adjust the parameters or switch to an alternative algorithm; when abnormal results are found, perform multiple random initialization verifications; when the algorithm performance degrades significantly, revert to the most recent stable version.

[0342] 4. Access monitoring mechanism

[0343] 4.1 Knowledge Consistency Monitoring

[0344] Monitoring target: The consistency of knowledge retrieval and update operations.

[0345] Implementation methods: Implement version control and change tracking for knowledge items; monitor the logical consistency before and after knowledge updates; establish a knowledge dependency graph and track indirect impacts.

[0346] Exception handling strategy: When a knowledge conflict is detected, apply conflict resolution rules or retain multiple versions; when knowledge inconsistencies are found, mark the affected areas and suspend related reasoning; when knowledge updates cause widespread impact, implement a phased update strategy.

[0347] Knowledge conflict refers to contradictions or inconsistencies between medical knowledge from different sources or at different levels within a knowledge base. Specific types include: content conflicts, such as inconsistent reference ranges for the same test item in different medical guidelines, or contradictory conclusions drawn from the same hypothesis in different studies; source conflicts, such as disagreements between authoritative guidelines and the latest research literature, or contradictions between different levels of medical evidence; timeliness conflicts, such as conflicts between outdated medical knowledge and the latest medical advancements; and semantic conflicts, such as different expressions of the same medical concept or differences in classification criteria. The system identifies these problems through a multi-level conflict detection mechanism: semantic consistency checks verify the standardization of medical concepts, evidence level comparisons analyze the credibility of knowledge from different sources, timestamp checks identify the timeliness of knowledge, and relational consistency checks verify the logical rationality of relationships between items. When a knowledge conflict is detected, the system applies conflict resolution rules: prioritizing higher-level evidence, retaining multiple versions of knowledge and marking them as conflicting, requesting expert arbitration, or marking uncertainty and reducing the confidence level of related inferences.

[0348] Knowledge inconsistency primarily refers to semantic inconsistencies, rather than differences in expression. Specifically, this includes: semantic inconsistencies, such as the same test item being categorized into different disease systems in different medical ontologies, or the same clinical symptoms corresponding to different diagnostic conclusions; relational inconsistencies, such as test items A and B being marked as positively correlated in one medical knowledge source, but negatively correlated or unrelated in another; logical inconsistencies, such as circular dependencies or mutually contradictory logical relationships between inference rules; and numerical inconsistencies, such as significant differences in the normal reference range of the same test item across different standards. The system does not focus on purely expressive differences (such as the synonym difference between "alanine aminotransferase" and "ALT"), but rather on substantive semantic conflicts of medical concepts. Detection mechanisms include: ontology mapping consistency checks to verify the uniformity of concept classification; relational network consistency analysis to identify contradictory relational definitions; inference rule consistency verification to check the logical compatibility between rules; and numerical range consistency checks to identify reference value differences exceeding reasonable ranges. When inconsistencies are detected, the system mediates and unifies them through methods such as evidence strength comparison, expert consensus weighting, and timeliness assessment.

[0349] The definition of widespread impact is based on the assessment of the propagation scope and depth of influence of relationships within the knowledge graph. Specific criteria include: direct impact scope (the number of medical concepts and testing items directly related to the updated knowledge exceeds 10% of the total system count); indirect impact scope (the number of second- and third-level related items affected through the relationship network exceeds 25% of the total count); and inference chain impact (the number of inference rules affected exceeds 15% of the rule base, or the number of hypotheses leading to changes in inference conclusions exceeds five). The system tracks the scope of impact through a knowledge dependency graph: establishing a dependency matrix between knowledge nodes, calculating the propagation path and depth of influence of knowledge updates, assessing the importance weights of affected inference rules, and counting the number of analysis tasks that may lead to changes in conclusions. When widespread impact is detected, the system implements a phased update strategy: first, suspending relevant analysis tasks; then conducting impact assessment and risk analysis; next, implementing incremental updates and step-by-step verification; and finally, conducting comprehensive consistency checks and performance tests to ensure that system stability is not affected.

[0350] 4.2 Access Performance Monitoring

[0351] Monitoring target: the time efficiency and accuracy of knowledge retrieval operations.

[0352] Implementation: Monitor retrieval time and the number of returned results; track cache hit rate and query complexity; record common failed query patterns.

[0353] Anomaly handling strategies: When a decline in retrieval performance is detected, optimize the index or adjust the caching strategy; when complex queries time out, implement query simplification or step-by-step execution; when retrieval results are abnormal, try alternative retrieval paths or perform degradation processing.

[0354] Search performance encompasses three dimensions: response time, accuracy, and throughput. Response time performance metrics include: average retrieval time (normal <2 seconds, warning 2-5 seconds, abnormal >5 seconds), complex query processing time, and index building and update time. Accuracy performance metrics include: relevance score of search results (based on user feedback and expert evaluation), recall (the proportion of relevant results), and precision (the proportion of relevant content in returned results). Throughput performance metrics include: the number of queries processed per unit time, concurrent query processing capacity, and cache hit rate. The system uses a multi-dimensional monitoring mechanism to detect performance degradation: establishing a performance baseline and recording historical averages and standard deviations; real-time monitoring of current performance metrics and calculating the deviation from the baseline; using sliding window statistics to identify performance degradation trends; and setting warning thresholds to trigger alerts when response time increases by more than 50%, accuracy decreases by more than 10%, or throughput decreases by more than 30%. When performance degradation is detected, the system automatically executes optimization strategies: index rebuilding, cache cleanup, query optimization, and load balancing adjustments.

[0355] 5. System Integration Layer Monitoring Mechanism

[0356] 5.1 Workflow Monitoring

[0357] Monitoring object: The execution status of the end-to-end analysis process.

[0358] Implementation method: Establish key node checkpoints to verify the phased results; monitor the collaboration status and task completion status between intelligent agents; track the deviation between the overall analysis progress and the expected timeline.

[0359] Anomaly handling strategy: When a process stall is detected (i.e., the time spent at a certain node exceeds the preset duration), the intervention mechanism is activated to restart the blocked task; when a collaboration conflict is found, the mediation mechanism is invoked to resolve the disagreement; when the overall progress is abnormal, the plan is re-evaluated and resource allocation is adjusted.

[0360] Collaboration conflicts refer to problems such as inconsistency in goals, resource competition, or evaluation disagreements that arise during multi-agent collaborative work. Specific types include: evaluation conflicts, where different agents give significantly different confidence scores (difference > 0.3) to the same hypothesis or evidence, or reach contradictory conclusions; task conflicts, where multiple agents simultaneously request the same restricted resources, or disagree on task priorities; policy conflicts, where different agents use conflicting analysis methods or reasoning strategies, leading to incompatible results; and timing conflicts, where the dependencies between agents do not match the execution order, causing logical deadlocks or circular waits. The system detects conflicts through a collaboration monitoring mechanism: monitoring the consistency level of agent evaluation results, marking evaluation conflicts when consistency is below 0.5; tracking resource allocation status to identify resource competition and access conflicts; analyzing task dependency graphs to detect circular dependencies and logical contradictions; and recording message passing patterns between agents to identify abnormal communication behaviors. When a collaboration conflict is detected, the system initiates a mediation mechanism to resolve the conflict.

[0361] 5.2 Monitoring of Result Consistency

[0362] Monitoring target: The consistency and reliability of the system's final output.

[0363] Implementation method: Implement multi-agent cross-validation and compare independent conclusions; monitor the uncertainty indicators and confidence levels of the results; establish a result rationality check mechanism and apply domain rules for verification.

[0364] Anomaly handling strategy: When inconsistencies in conclusions are detected, initiate the consensus formation mechanism; when results are found to be inconsistent with domain knowledge, mark suspicious findings and provide evidence; when confidence is insufficient, explicitly identify the issue and recommend further verification.

[0365] The consensus-building mechanism employs a multi-stage negotiation and weight adjustment strategy to resolve disagreements among agents. The specific process includes: Stage 1: Disagreement Analysis. This stage identifies the specific causes and points of contention, analyzes the assessment criteria and reasoning paths of each agent, and calculates the severity and scope of the disagreement. Stage 2: Evidence Re-examination. This stage requires both parties to provide detailed supporting evidence, re-searches and evaluates relevant literature evidence, and conducts cross-validation and evidence quality assessment. Stage 3: Weight Adjustment. This stage reallocates assessment weights based on each agent's professional matching degree, historical accuracy, and evidence quality, reducing the impact of low-quality assessments and increasing the weight of high-quality assessments. Stage 4: Iterative Negotiation. This stage uses a structured dialogue mechanism to allow agents to exchange assessment reasons, correct obvious logical errors or cognitive biases, and re-evaluate based on sufficient information exchange. Stage 5: Arbitration Decision. When negotiation fails to reach a consensus, the coordinator conducts final arbitration based on evidence strength, weight allocation, and professional authority. The entire process records a detailed negotiation trajectory to ensure the transparency and traceability of the decision-making process.

[0366] Non-compliance with domain knowledge refers to situations where the analysis results or inference conclusions contradict established medical professional knowledge, clinical practice experience, or scientific consensus. Specific manifestations include: violating common physiological knowledge, such as inferring test values ​​exceeding human physiological limits or proposing hypotheses that violate basic biological principles; violating pathological laws, such as assuming a pathogenesis inconsistent with known diseases or inferring a correlation contradicting clinicopathological manifestations; violating evidence-based medicine, such as analysis results conflicting with the conclusions of numerous high-quality research papers or ignoring established medical guidelines; and violating clinical practice experience, such as recommended diagnostic strategies being infeasible in actual clinical settings or suggested monitoring protocols lacking practicality. The system identifies results that do not conform to domain knowledge through a multi-level verification mechanism: semantic consistency checks based on medical ontology and knowledge graphs, logical rationality verification through inference rule bases, evidence support checks against authoritative medical guidelines and literature databases, and practical feasibility verification using clinical pathway templates. When results that do not conform to domain knowledge are found, the system automatically marks them as suspicious findings, lowers the confidence score, provides detailed conflict explanations and improvement suggestions, and requires further verification.

[0367] Furthermore, it also includes:

[0368] The process of correlation analysis is displayed in real time, including: knowledge browsing display, hypothesis generation display, verification analysis display, and result display.

[0369] In another embodiment of this solution, the result display may include:

[0370] The user interface adopts a modular design, comprising four main functional areas: knowledge browsing, hypothesis generation, validation analysis, and results display. The interface is developed using the Streamlit framework to form a complete user interaction system. Each functional area employs a specialized design to meet the specific needs of the medical testing field.

[0371] 1 Knowledge Browsing Function Area

[0372] Hierarchical navigation structure: The tabbed organization allows for the orderly display of five sub-functions: knowledge base overview, verification project management, relationship management, import / export, and search, enabling users to quickly locate relevant functions according to their needs.

[0373] Interactive Knowledge Graph Visualization: Combining force-directed algorithm-based network graph visualization technology, the complex relationships between inspection items are transformed into intuitive visual representations. Visual elements such as node color coding and size scaling effectively convey the category, importance, and other attribute information of the inspection items.

[0374] Advanced search and filtering system: Implements a multi-condition combined search mechanism, supports multi-dimensional filtering such as project name, code, and category, and adopts a real-time response mode, with results dynamically updated as input is entered, improving search efficiency and user experience.

[0375] Detailed display technology: The technology uses a collapsible container mode to display the details of the test items, achieving the separation of metadata and numerical values, while providing contextual relationship information to show the relationship between the current item and other test items.

[0376] Knowledge editing interface: Provides inline editing functionality, supports real-time modification of project attributes and relationship characteristics, and ensures data validity through input validation, enabling dynamic maintenance and updating of knowledge base content.

[0377] 2. Assuming the generation of functional areas

[0378] Intelligent Input Enhancement System: Integrates context-aware auto-completion functionality, combined with medical terminology recognition and standardization technology, to provide users with real-time input suggestions and reduce the difficulty of inputting professional terms.

[0379] Multimodal input interface: Supports three input modes: natural language description, structured form, and graphical relationship construction, enabling seamless switching between modes and data synchronization, and adapting to the usage habits of users with different professional backgrounds.

[0380] Hypothesis Template System: Pre-set commonly used medical hypothesis templates, support parameterized customization of templates, and provide the function of reusing historical hypotheses, effectively improving the efficiency and professional quality of hypothesis construction.

[0381] Real-time rationality check: Applying medical knowledge rules to evaluate input hypotheses in real time, visually highlighting potential problems, and providing targeted improvement suggestions to ensure the professional rationality of the hypotheses.

[0382] AI-assisted hypothesis generation technology: Integrates the capabilities of large-scale language models, provides intelligent hypothesis completion and improvement suggestions, supports hypothesis recommendation based on current test data, and achieves efficient hypothesis generation through human-machine collaboration.

[0383] 3. Verification and Analysis Function Area

[0384] Multi-dimensional verification framework: Construct a parallel verification architecture based on statistics, knowledge, and literature, optimize the execution process through an intelligent verification task scheduling system, and provide real-time visual feedback on verification progress.

[0385] Interactive Validation Control Panel: Designs a visual configuration interface for validation parameters, supports advanced options such as validation depth, literature scope, and threshold settings for customization, and provides validation process control functions.

[0386] Evidence Exploration Interface: Implements a hierarchical evidence browsing structure, unfolding step by step from overview to details. It uses visual encoding technology for evidence quality to intuitively distinguish different levels of evidence and supports evidence tracing and querying.

[0387] Verification and Comparison System: Supports parallel verification of multiple hypotheses and comparison of results, enables hypothesis variant generation and difference analysis, and provides a function to compare historical verification results to enhance the reference value of verification results.

[0388] Custom verification strategy: Allows users to customize verification paths and priorities, supports selective activation of verification components, and provides an advanced parameter adjustment interface for professional users to meet personalized verification needs.

[0389] 4 Results Display Function Area

[0390] Multi-level results visualization: Construct a three-level display architecture of results summary, detailed analysis and raw data, and use a card layout to highlight key information, supporting interactive exploration and in-depth analysis of results data.

[0391] Intelligent Visualization Selector: Automatically selects the most suitable visualization method based on the characteristics of the result data, supports flexible switching between multiple visualization modes, and provides an interface for adjusting visualization parameters to meet personalized needs.

[0392] Results Interpretation System: Utilizes natural language generation technology to provide textual interpretations of results, automatically marks and highlights key findings, and provides explanations of technical terms to aid understanding.

[0393] Comparative analysis tools: Supports comparative analysis of current results with historical data and reference standards, highlighting differences and visualizing trends, and providing statistical significance assessment.

[0394] Results Export and Sharing: Supports exporting reports in multiple formats (PDF, Word, JSON, CSV, etc.), enabling customized report generation with hierarchical results content, facilitating clinical communication and decision-making reference.

[0395] 5 Interface Integration and Technological Innovation

[0396] 5.1 Integration Technology

[0397] Unified design language: Apply a consistent visual element system and component styles to achieve responsive layouts that adapt to different device environments, and use semantic interface elements to reduce the cognitive burden on users.

[0398] Context state management: Utilize session state technology to maintain the continuity of user operations, realize workflow memory and recovery functions, and ensure data collaboration and state sharing across functional areas.

[0399] UI performance optimization: By adopting component lazy loading and on-demand rendering technology, combined with data caching and incremental update mechanisms, the UI response speed and overall system performance are effectively improved.

[0400] 5.2 Technological Innovation

[0401] Medical UI Component Library: Develops a set of visualization components specifically for clinical laboratory data, enabling data representation in a manner consistent with medical professional standards and providing interactive modes adapted to medical decision-making processes.

[0402] Intelligent layout optimization technology: Dynamically adjusts the layout of interface elements based on user behavior patterns, enabling key information to be intelligently placed on top and emphasized, and supports personalized view configuration and restoration.

[0403] Explainable interactive system: Provides real-time guidance and explanation during the interaction process, enables visual tracking of decision-making paths, and supports intuitive expression and recording of the "hypothesis-verification" cycle.

[0404] This user interface system translates complex internal processing logic into a form that is easy for medical professionals to understand and apply, achieving a seamless integration of technology and clinical practice, and laying a solid foundation for the system's application in real-world medical environments. Furthermore, the system provides comprehensive user access control and operation log recording functions to ensure data security and the traceability of the analysis process.

[0405] In another embodiment of this scheme, the evaluation method is as follows:

[0406] 1. Technical Performance Evaluation

[0407] Technical performance evaluation methods include:

[0408] (1) Accuracy assessment: A standard test set containing 500 known test item correlations was constructed, based on authoritative medical literature and expert consensus, to evaluate the accuracy of the system in identifying and analyzing correlations;

[0409] (2) Efficiency evaluation: Measure the time it takes for the system to process test data of different sizes and evaluate its response speed in practical applications;

[0410] (3) Stability assessment: Conduct a continuous 72-hour high-load test to monitor the performance changes and error rate of the system under long-term operation.

[0411] 2. Clinical applicability assessment

[0412] 2.1 Evaluation Method

[0413] User experience assessment: A standardized user experience questionnaire was used to evaluate system usability, learning curve, and satisfaction; specific clinical scenario tasks were designed to measure task completion time and success rate; user behavior and difficulties were analyzed through observation and video recording.

[0414] Clinical decision support assessment:

[0415] Comparative evaluation: Comparing the differences between system-assisted decision-making and traditional methods on the same clinical problem;

[0416] Timeliness assessment: The actual impact of the measurement system on accelerating the clinical decision-making process;

[0417] Accuracy assessment: The accuracy and reliability of the system recommendations were validated through retrospective case analysis;

[0418] Practicality assessment: Evaluate the application value of the hypotheses and validation results generated by the system in actual clinical work;

[0419] Knowledge acquisition assessment: The system's role in facilitating clinicians' discovery of new connections and expansion of professional knowledge.

[0420] Example 1, as Figures 4 to 7The core functional modules include: (1) a knowledge base management system that supports the import, editing, and querying of medical knowledge; (2) a multi-agent collaboration platform that enables collaborative work among five types of professional agents; (3) a hypothesis generation and verification engine that supports automatic hypothesis generation and multi-angle verification; and (4) a user interface that provides intuitive data analysis and result display. The system supports input of various data formats, including standard medical data formats such as CSV and JSON. The knowledge base currently contains information on more than 1,000 common clinical test items, covering multiple fields such as biochemistry, immunology, and hematology. The system can process structured test result data and semi-structured medical text, and supports complex correlation analysis queries.

[0421] To verify the system's application value in real-world clinical scenarios, three representative cases were selected for in-depth analysis, covering different clinical fields and laboratory test types: correlation analysis of liver function tests, analysis of the relationship between renal function and electrolyte balance, and a study on the correlation between blood glucose and lipid metabolism. These cases all used real clinical data (after anonymization) and obtained clinically valuable results through a multi-agent collaborative analysis process. They also provided new insights for subsequent scientific research.

[0422] Case Study 1: Correlation Analysis of Liver Function Test Items. This case study aims to analyze the correlations and clinical significance among common liver function test items (ALT, AST, GGT, ALP, TBIL, DBIL, etc.), with a particular focus on the specific patterns of these correlations in different types of liver disease. Liver function test data from a tertiary hospital throughout 2022 (anonymized, including records from 10,562 patients) were selected, covering various liver disease types such as acute and chronic hepatitis, cirrhosis, fatty liver, and liver cancer.

[0423] The analysis process began with a data analyst agent cleaning and preprocessing the raw data, identifying and handling outliers (3.2% of the total data) and missing values ​​(5.7%). A comprehensive correlation analysis was then performed, revealing a high correlation between ALT and AST (0.83, p<0.001), and a moderate correlation between GGT and ALP (0.67, p<0.001). Further cluster analysis identified four typical combinations of liver function indicators, which corresponded to different types of liver disease. Based on this, the system automatically generated several preliminary hypotheses, including "the AST / ALT ratio may be an effective indicator for distinguishing between alcoholic and non-alcoholic fatty liver disease" and "the GGT / ALP ratio may be positively correlated with the severity of biliary tract diseases."

[0424] Regarding the hypothesis that "the AST / ALT ratio may be an effective indicator for distinguishing between alcoholic and non-alcoholic fatty liver disease," the medical expert system first analyzed the issue from a physiological and pathological perspective. It pointed out that alcohol metabolism causes significant damage to hepatocyte mitochondria, and since AST is mainly distributed in mitochondria, alcoholic liver disease may lead to a more significant increase in AST levels. Subsequently, the system automatically performed statistical validation, comparing the AST / ALT ratios of diagnosed alcoholic fatty liver disease patients (n=326) and non-alcoholic fatty liver disease patients (n=1,524). The results showed that the AST / ALT ratio of alcoholic fatty liver disease patients was significantly higher than that of non-alcoholic fatty liver disease patients (1.42±0.38 vs. 0.78±0.25, p<0.001). ROC curve analysis determined that when AST / ALT>1.0 was used as the differential diagnostic threshold, the sensitivity was 82.3%, the specificity was 75.8%, and the area under the curve (AUC) was 0.836 (95% CI: 0.796–0.877).

[0425] The literature research agent then retrieved 23 relevant articles from the PubMed database, of which 19 supported the hypothesis, while 4 raised different viewpoints or limitations. The system assessed the level of evidence for these articles: 5 were systematic reviews or meta-analyses (Level 1a evidence), 8 were randomized controlled trials (Level 1b evidence), and 10 were observational studies (Level 2b-3b evidence). The literature analysis revealed that most studies supported an AST / ALT ratio >1.0 as a screening indicator for alcoholic liver disease, but some studies pointed out that in advanced cirrhosis, regardless of etiology, the AST / ALT ratio may be elevated, limiting its specificity. Based on the comprehensive evidence, the system assigned an 85% confidence score to the hypothesis and provided a detailed validation report, including potential application scenarios and clinical considerations.

[0426] A novel, less-studied association pattern was also discovered: a trend of association between the TBIL / DBIL ratio and GGT levels under specific disease states. Through regression analysis, the system found a significant positive correlation between the TBIL / DBIL ratio and GGT levels in patients with cholestatic disease (n=783) (r=0.63, p<0.001), and this correlation was more pronounced when the jaundice index was >5 (r=0.78, p<0.001). The system not only provided statistical evidence for this finding but also offered a possible physiological explanation through a medical expert agent: the degree of damage to the bilirubin metabolism and transport system may simultaneously affect the TBIL / DBIL ratio and GGT release, especially when hepatocyte membrane damage is accompanied by bile duct dysfunction. Based on this, the experimental designer agent proposed further validation protocols, including animal model experiments and clinical cohort study designs.

[0427] Case Study 2: Analysis of the Relationship between Renal Function and Electrolyte Balance. This case study analyzed the complex relationship between renal function indicators (creatinine, blood urea nitrogen, uric acid, eGFR) and electrolytes (sodium, potassium, chloride, calcium, phosphorus). Data from 6,745 patients were systematically analyzed, with a focus on electrolyte balance patterns in patients at different stages of chronic kidney disease. Through multivariate analysis and machine learning methods, the system identified a non-linear relationship between eGFR and serum phosphorus levels, finding that eGFR between 45-60 ml / min / 1.73 ml / min... 2 A clear inflection point in phosphorus metabolism regulation exists within the range. The system-generated hypothesis, "The PTH-FGF23-Klotho axis may have a compensatory regulatory mechanism in the early stages of CKD," was rated as a high-value hypothesis by experts, providing new ideas for subsequent interventions targeting phosphorus metabolism in early CKD.

[0428] Case Study 3: Correlation Study of Blood Glucose and Lipid Metabolism. A systematic analysis of laboratory data from 8,376 patients with type 2 diabetes explored the correlation between blood glucose control indicators (fasting blood glucose, postprandial blood glucose, glycated hemoglobin) and lipid indicators (total cholesterol, triglycerides, HDL-C, LDL-C, non-HDL-C). The study found that the correlation between glycated hemoglobin and non-HDL-C (r = 0.48, p < 0.001) was stronger than that with LDL-C (r = 0.32, p < 0.001), and this relationship was more significant in patients with high triglycerides. Based on this, the study proposed the mechanism hypothesis that "glycated hemoglobin may indirectly regulate VLDL synthesis and clearance by affecting insulin sensitivity," providing a new monitoring approach for diabetes lipid management.

[0429] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0430] This invention also provides a system for analyzing the correlation of clinical laboratory test items based on multi-agent collaboration, the specific technical solution of which is as follows:

[0431] The acquisition unit is used to: acquire the name of any clinical laboratory test item to be analyzed and its corresponding basic parameters, and determine the technical field corresponding to the item name based on the test item classification system and the domain identification algorithm;

[0432] The extraction unit is used to: extract domain parameters related to the technical field from the pre-built knowledge management module, and set the configuration parameters of at least two categories of intelligent agents based on the domain parameters;

[0433] The processing unit is used to: generate task instructions for different intelligent agents based on basic parameters, transmit the task instructions according to a preset structured message passing mechanism, and obtain the processing results of different intelligent agents for the task instructions;

[0434] The analysis unit is used to: verify and analyze the hypothesis content corresponding to all the acquired processing results through the hypothesis generation and validation engine, and obtain the correlation analysis results of the clinical laboratory test items to be analyzed.

[0435] Based on the above solution, the present invention can be further improved as follows.

[0436] Furthermore, the pre-defined structured message passing mechanism is as follows:

[0437] Determine whether there is an information exchange request between any two intelligent agents, and if the determination result is yes, send a standardized structured message body to the central routing system;

[0438] The standardized structured message body is formatted and its legality is checked through a central routing system.

[0439] When both format validation and legality checks pass, the message data in the information exchange request is parsed and processed to generate a processing result;

[0440] And return the processing result according to the path in the information exchange request.

[0441] Furthermore, the process of generating task instructions for different intelligent agents based on the basic parameters is as follows:

[0442] The basic parameters are structured to generate feature vectors and relationship graphs;

[0443] Based on feature vectors and relationship graphs, priority marking of association degree is performed;

[0444] Based on the correlation priority labeling results, task description events are generated, and task description events are distributed based on the event management center to generate task instructions for different intelligent agents.

[0445] Furthermore, the pre-built knowledge management module specifically includes:

[0446] The medical ontology mapping layer, relational network layer, semantic vector layer, and inference rule layer;

[0447] The medical ontology mapping layer is used to construct a hierarchical concept classification system and perform semantic association on the input data;

[0448] The relational network layer is used to construct a relational path network based on semantic association results;

[0449] The semantic vector layer is used to transform the clinical laboratory test items to be analyzed into vectors and determine the semantic similarity between vectors;

[0450] The inference rule layer is used to perform implicit inference on the input data and obtain the inference result;

[0451] Output the relational path network, semantic similarity, and inference results.

[0452] Furthermore, let's assume the production and validation engine specifically includes:

[0453] Statistical analysis module, knowledge reasoning module, and literature analysis module;

[0454] By combining the statistical analysis module, knowledge reasoning module, and literature analysis module with a comprehensive evaluation mechanism, the reliability score results corresponding to the input data are determined.

[0455] The statistical analysis module is used to: parse the hypotheses, select the corresponding processing strategy based on the analysis results, process the data according to the processing strategy, and generate a results report;

[0456] The knowledge reasoning module is used to: parse the medical concepts and relational assertions corresponding to the hypothetical content, determine the known relations corresponding to the medical concepts and relational assertions through the pre-built knowledge management module, verify the consistency between the hypothetical content and the known relations based on the preset reasoning rules, and determine the theoretical rationality score corresponding to the hypothetical content in conjunction with the rationality assessment system.

[0457] The literature analysis module is used to: construct search expressions based on hypothetical content, perform searches based on search expressions, deduplicate search results to obtain literature data, process the literature data using NLP technology, determine conclusion data related to the hypothetical content, assess the level of evidence for the literature data corresponding to the conclusion data, and determine the strength of evidence score based on the level of evidence assessment results.

[0458] Furthermore, it also includes:

[0459] The monitoring unit is used to: perform real-time policy monitoring on the correlation analysis process. Policy monitoring includes: monitoring within the agent, monitoring between agents, monitoring data processing nodes, monitoring access functions, and monitoring the acquisition layer.

[0460] Furthermore, it also includes:

[0461] The display unit is used to display the results of the correlation analysis process in real time. The results display includes: knowledge browsing display, hypothesis generation display, verification analysis display, and result display.

Claims

1. A method for correlation analysis of clinical laboratory test items based on multi-agent collaboration, characterized in that, include: Obtain the name and corresponding basic parameters of any clinical laboratory test item to be analyzed, and determine the technical field corresponding to the item name based on the test item classification system and the domain identification algorithm; Extract domain parameters related to the technical field from the pre-built knowledge management module, and set the configuration parameters of at least two categories of intelligent agents based on the domain parameters; Based on the aforementioned basic parameters, task instructions for different intelligent agents are generated, and the task instructions are transmitted according to a preset structured message passing mechanism, as well as the processing results of different intelligent agents in response to the task instructions are obtained. The hypothesis production and validation engine verifies and analyzes the hypothesis content corresponding to all the acquired processing results to obtain the correlation analysis results of the clinical laboratory test items to be analyzed.

2. The method for correlation analysis of clinical laboratory test items based on multi-agent collaboration according to claim 1, characterized in that, The preset structured message passing mechanism is specifically as follows: Determine whether there is an information exchange request between any two intelligent agents, and if the determination result is yes, send a standardized structured message body to the central routing system; The central routing system performs format validation and legality checks on the standardized structured message body. When both the format verification and the legality check pass, the message data in the information exchange request is parsed and processed to generate a processing result; The processing result is returned according to the path in the information exchange request.

3. The method for correlation analysis of clinical laboratory test items based on multi-agent collaboration according to claim 1, characterized in that, The process of generating task instructions for different intelligent agents based on the aforementioned basic parameters is as follows: The basic parameters are structured to generate feature vectors and relationship graphs; Based on the feature vectors and relationship graphs, priority marking of correlation degree is performed; Based on the correlation priority marking results, a task description event is generated, and the task description event is issued based on the event management center to generate task instructions for different intelligent agents.

4. The method for correlation analysis of clinical laboratory test items based on multi-agent collaboration according to claim 1, characterized in that, The pre-built knowledge management module specifically includes: The medical ontology mapping layer, relational network layer, semantic vector layer, and inference rule layer; The medical ontology mapping layer is used to construct a hierarchical concept classification system and to perform semantic association on the input data; The relational network layer is used to construct a relational path network based on semantic association results; The semantic vector layer is used to transform the clinical laboratory test items to be analyzed into vectors and determine the semantic similarity between vectors; The reasoning rule layer is used to perform implicit reasoning on the input data to obtain the reasoning result; Output the relational path network, semantic similarity, and inference results.

5. The method for correlation analysis of clinical laboratory test items based on multi-agent collaboration according to claim 1, characterized in that, The hypothesis generation and verification engine specifically includes: Statistical analysis module, knowledge reasoning module, and literature analysis module; The reliability score of the input data is determined by combining the statistical analysis module, the knowledge reasoning module, and the literature analysis module with a comprehensive evaluation mechanism. The statistical analysis module is used to: parse the hypothetical content, select the corresponding processing strategy based on the parsing results, process the data according to the processing strategy, and generate a result report; The knowledge reasoning module is used to: parse the medical concepts and relational assertions corresponding to the hypothetical content, determine the known relations corresponding to the medical concepts and relational assertions through the pre-built knowledge management module, verify the consistency between the hypothetical content and the known relations based on preset reasoning rules, and determine the theoretical rationality score corresponding to the hypothetical content in conjunction with the rationality assessment system. The literature analysis module is used to: construct a search expression based on the hypothetical content, perform a search based on the search expression, deduplicate the search results to obtain literature data, process the literature data using NLP technology, determine the conclusion data related to the hypothetical content, evaluate the level of evidence of the literature data corresponding to the conclusion data, and determine the strength of evidence score based on the level of evidence evaluation results.

6. The method for correlation analysis of clinical laboratory test items based on multi-agent collaboration according to claim 1, characterized in that, Also includes: The process of correlation analysis is monitored in real time using strategies, which include: monitoring within the agent, monitoring between agents, monitoring data processing nodes, monitoring access functions, and monitoring the acquisition layer.

7. The method for correlation analysis of clinical laboratory test items based on multi-agent collaboration according to claim 1, characterized in that, Also includes: The process of correlation analysis is displayed in real time, including: knowledge browsing display, hypothesis generation display, verification analysis display, and result display.

8. A system for analyzing the correlation of clinical laboratory test items based on multi-agent collaboration, characterized in that, include: The acquisition unit is used to: acquire the name of any clinical laboratory test item to be analyzed and its corresponding basic parameters, and determine the technical field corresponding to the name of the item based on the test item classification system and the field identification algorithm; The extraction unit is used to: extract domain parameters related to the technical field from the pre-built knowledge management module, and set configuration parameters for at least two categories of intelligent agents based on the domain parameters; The processing unit is used to: generate task instructions for different intelligent agents based on the basic parameters, transmit the task instructions according to a preset structured message passing mechanism, and obtain the processing results of different intelligent agents for the task instructions; The analysis unit is used to: verify and analyze the hypothesis content corresponding to all the acquired processing results through the hypothesis generation and verification engine, and obtain the correlation analysis results of the clinical laboratory test items to be analyzed.

9. An electronic device, characterized in that, The electronic device includes a processor coupled to a memory storing at least one computer program, which is loaded and executed by the processor to enable the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to enable the computer to perform the method as claimed in any one of claims 1 to 7.

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