Medical document intelligent generation and quality control method and system

Abstract

This application discloses a method and system for intelligent generation and quality control of medical documents, relating to the field of medical information technology. The disclosed method and system for intelligent generation and quality control of medical documents acquires multi-source medical data, constructs a medical semantic graph network, performs clinical logical reasoning, generates and verifies medical documents, and updates them based on feedback. It can automatically construct a patient-specific medical knowledge network, identify deep semantic relationships between clinical entities, generate logically rigorous professional descriptions, and improve the quality of medical documents and medical safety.

Description

Technical Field

[0001] This application relates to the field of medical information technology, and in particular to methods and systems for intelligent generation and quality control of medical documents. Background Technology

[0002] In clinical practice, medical records serve as the core carrier for recording the evolution of a patient's condition, treatment decisions, and outcomes; their quality directly impacts medical safety and efficiency. Currently, a key challenge for clinicians lies in transforming diverse and heterogeneous medical information, such as electronic health records, laboratory test data, and imaging reports, into logically rigorous and clinically sound professional narratives.

[0003] Existing medical documentation assistance systems generally employ time-axis-based template-filling mechanisms, arranging discrete data points such as vital signs, test values, and diagnostic conclusions according to a preset format. However, this approach only reaches the surface-level alignment of data. The system cannot identify deep semantic relationships between clinical entities. For example, when detecting a history of "hypertension," a test result of "proteinuria," and an indicator of "elevated serum creatinine," it cannot infer that all three point to the pathological entity of "hypertensive nephropathy," nor can it analyze the causal chain between "irregular medication" and "poor blood pressure control." This limitation is particularly pronounced in complex case management: the system can display the diagnosis of "diabetes" and the examination results of "retinopathy" in isolation, but cannot establish a dynamic correlation between blood glucose control levels and the development of complications; in scenarios involving the combined use of multiple drugs, the system can list the drug names but cannot assess the potential risks of drug interactions. The resulting documentation exhibits a mechanical splicing characteristic, lacking a coherent description of disease progression and logical support for treatment decisions, forcing doctors to repeatedly adjust the text structure to reflect the clinical reasoning process.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a method and system for intelligent generation and quality control of medical documents, which aims to improve the quality of medical documents and medical safety.

[0006] To achieve the above objectives, this application proposes a method for intelligent generation and quality control of medical documents, the method comprising:

[0007] Acquire multi-source medical data and preprocess it to obtain standardized medical data and individual patient characteristic data;

[0008] Based on the standardized medical data, clinical entities are extracted and the relationships between entities are identified. A patient-centered medical semantic graph network is constructed to obtain semantic graph data containing nodes and edges; the nodes represent clinical entities and the edges represent clinical relationships between entities.

[0009] Based on the semantic graph data and the patient's individual characteristic data, a dynamic reasoning engine is used to perform clinical logical reasoning, identify potential clinical abnormal patterns and predict risks, and obtain clinical reasoning result data.

[0010] Based on the clinical reasoning results data and the standardized medical data, a draft of medical documents that conforms to clinical logic is generated.

[0011] The initial draft data of the medical document is compared and verified with the semantic graph data to obtain verified medical document data;

[0012] The verified medical document data is displayed to medical staff, and the medical staff's modification operations on the medical document data are received to obtain modification feedback data. Based on the modification feedback data, the semantic graph data is updated and adjusted to obtain updated semantic graph data.

[0013] In one embodiment, the steps of extracting clinical entities and identifying relationships between entities based on the standardized medical data, and constructing a patient-centered medical semantic graph network to obtain semantic graph data containing nodes and edges include:

[0014] Clinical entity information is identified and extracted from the standardized medical data to obtain clinical entity information data;

[0015] The clinical entity information data is matched and linked with standard medical terms in a preset medical knowledge base to obtain standardized clinical entity data.

[0016] Analyze the temporal, causal, spatial, and therapeutic relationships among the clinical entities in the standardized clinical entity data to obtain entity relationship data;

[0017] Using the patient as the central node, the standardized clinical entity data as child nodes, and the entity relationship data as edges, a multi-layered medical semantic graph network is constructed to obtain the semantic graph data.

[0018] In one embodiment, the step of matching and linking the clinical entity information data with standard medical terms in a preset medical knowledge base to obtain standardized clinical entity data includes:

[0019] The standard medical terminology database is retrieved from a pre-set medical knowledge base; the standard medical terminology database contains standard names and codes for symptom terms, sign terms, laboratory test terms, diagnostic terms, and treatment terminology.

[0020] The medical terms in the clinical entity information data are matched with the standard medical terminology database to identify the closest standard terms and obtain the matching result data.

[0021] For successfully matched clinical entities, the original expressions are replaced with standard names and codes from the standard medical terminology database to obtain preliminary standardized data.

[0022] For clinical entities that cannot be directly matched, semantic reasoning and classification are performed based on contextual semantic analysis and medical background knowledge to generate supplementary standard terminology data;

[0023] The preliminary standardized data and the supplementary standard terminology data are merged and processed to convert all clinical entities into standardized expressions in the standard medical terminology database, thus obtaining the standardized clinical entity data.

[0024] In one embodiment, the step of analyzing the temporal, causal, spatial, and therapeutic relationships among clinical entities in the standardized clinical entity data to obtain entity relationship data includes:

[0025] Analyze the chronological order of different clinical entities in the standardized clinical entity data to establish a temporal relationship chain data;

[0026] Based on a pre-set medical causal knowledge base, the causal relationship between symptom entities and sign entities, the indicative relationship between sign entities and test indicator entities, the supporting relationship between test indicator entities and diagnostic entities, and the treatment plan relationship between diagnostic entities and treatment measure entities are analyzed in the standardized clinical entity data to establish causal reasoning chain data.

[0027] Based on clinical diagnosis and treatment guidelines, the collaborative or conflicting relationships between different treatment measures entities in the standardized clinical entity data are analyzed to establish treatment association network data.

[0028] The time relationship chain data, causal reasoning chain data, and treatment association network data are integrated to obtain the complete entity relationship data.

[0029] In one embodiment, the step of performing clinical logical reasoning through a dynamic reasoning engine based on the semantic graph data and the patient's individual characteristic data to identify potential clinical abnormal patterns and predict risks, and obtaining clinical reasoning result data includes:

[0030] Traverse all nodes and edges in the semantic graph data to identify whether there are contradictory clinical information and obtain contradiction detection result data;

[0031] The semantic graph data is analyzed to determine whether there are logical gaps or missing information in the clinical development path, and the completeness analysis results are obtained.

[0032] Based on a pre-set medical risk knowledge base, the semantic graph data is matched to see if there are high-risk combination patterns. The high-risk combination patterns include specific combinations of multiple symptom entities and sign entities to obtain risk pattern recognition result data.

[0033] Based on the current clinical status of the semantic graph data, combined with the preset disease development pattern knowledge base and the patient's individual characteristic data, the clinical events that may occur in the future are predicted, and the degree of potential risk is calculated according to the preset risk assessment criteria to obtain the predictive analysis results data.

[0034] The contradiction detection results, integrity analysis results, risk pattern recognition results, and predictive analysis results are comprehensively analyzed to generate the clinical reasoning results data, which include specific recommendations and evidence.

[0035] In one embodiment, the step of matching the semantic graph data based on a preset medical risk knowledge base to determine whether a high-risk combination pattern exists, wherein the high-risk combination pattern includes a specific combination of multiple symptom entities and sign entities, and obtaining risk pattern recognition result data includes:

[0036] The system reads predefined risk combination rules from a pre-defined medical risk knowledge base. These risk combination rules define the risk level and clinical significance when multiple clinical entities occur simultaneously.

[0037] All clinical entities in the semantic graph data are matched one by one with the risk combination rules to identify entity combinations that meet the rule requirements and obtain entity combination matching results.

[0038] For each identified entity combination in the entity combination matching results, its risk score is calculated and the risk level is determined to obtain risk level assessment data. The risk score is based on the number of entities, their importance and the strength of their interrelationships in the combination.

[0039] Based on the risk level assessment data, content including high-risk early warning information and handling suggestions is generated as the risk pattern recognition result data.

[0040] In one embodiment, the steps of predicting potential future clinical events based on the current clinical status of the semantic graph data, combined with a preset disease development pattern knowledge base and the patient's individual characteristic data, and calculating the potential risk level according to preset risk assessment criteria to obtain predictive analysis results data include:

[0041] Based on a pre-defined knowledge base of disease development patterns, data on the natural development trajectory of a specific disease or clinical condition is obtained.

[0042] The clinical status of the current semantic graph data is compared and analyzed with the natural development trajectory data to identify the position of the current status in the natural development trajectory and obtain the status position information;

[0043] Based on the status location information and combined with the patient's individual characteristic data, predict the clinical events that may occur within a preset time period in the future to obtain event prediction data;

[0044] For each predicted clinical event in the event prediction data, its probability of occurrence and potential risk level are calculated according to the preset risk assessment criteria to obtain risk assessment data;

[0045] Based on the risk assessment data, a predictive report containing prevention recommendations and intervention timing is generated as the predictive analysis result data.

[0046] In one embodiment, the step of generating initial draft data of medical documents that conforms to clinical logic based on the clinical reasoning result data and the standardized medical data includes:

[0047] Select the appropriate document template from the preset document template library according to the type of medical document. The document template defines the structural framework and required content items of the document.

[0048] The key findings and conclusions in the clinical reasoning results data are organized into a coherent narrative text according to the clinical logical order to obtain narrative text data.

[0049] The specific numerical values ​​and time information from the standardized medical data are filled into the corresponding positions in the narrative text data to generate basic information content data;

[0050] By combining the clinical development context in the semantic graph data, explanatory and transitional statements are added to the basic information content data to form logically coherent complete text data.

[0051] According to the structural framework of the document template, the basic information content data and the complete text data are combined and arranged to generate the first draft data of the medical document that conforms to the standard format.

[0052] In one embodiment, the step of comparing and verifying the initial draft data of the medical document with the semantic graph data to obtain verified medical document data includes:

[0053] Extract all mentioned clinical entities and relationship descriptions from the initial draft data of the medical document to obtain the document content entity data;

[0054] The document content entity data is compared one by one with the nodes and edges in the semantic graph data to check for inconsistencies in expression or missing information, and the comparison result data is obtained.

[0055] For any inconsistencies found in the comparison results data, the specific location and problem type are marked to obtain problem marking data. The problem types include entity description errors, relationship description errors, information omissions, and logical contradictions.

[0056] Based on the semantic graph data, inconsistencies in the problem-marked data are automatically corrected or correction suggestions are provided to ensure that the document content is completely consistent with the underlying semantic representation, resulting in corrected document data.

[0057] Based on the corrected document data, a consistency verification report is generated, recording all verification processes and results, to obtain the verified medical document data.

[0058] Furthermore, to achieve the above objectives, this application also proposes a medical document intelligent generation and quality control system, which includes: a memory, a processor, and a medical document intelligent generation and quality control program stored in the memory and executable on the processor. The medical document intelligent generation and quality control program is configured to implement the steps of the medical document intelligent generation and quality control method.

[0059] The intelligent generation and quality control method and system for medical documents proposed in this application acquires multi-source medical data, constructs a medical semantic graph network, performs clinical logical reasoning, generates and verifies medical documents, and updates them based on feedback. It can automatically construct a patient-specific medical knowledge network, identify deep semantic relationships between clinical entities, generate logically rigorous professional descriptions, and improve the quality of medical documents and medical safety. Attached Figure Description

[0060] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0061] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 A flowchart illustrating an embodiment of the intelligent generation and quality control method for medical documents in this application;

[0063] Figure 2This is a schematic diagram of a structure provided for an embodiment of the intelligent generation and quality control system for medical documents in this application.

[0064] Explanation of icon numbers:

[0065] 10. Memory; 20. Processor.

[0066] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0067] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0068] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0069] In existing technologies, medical document assistance systems primarily employ template-filling methods when integrating multi-source heterogeneous medical data. This lack of understanding of the deep semantic relationships between clinical entities hinders effective clinical reasoning and logical narrative construction. Consequently, the generated documents are mechanical and lack a clear clinical thought process. Medical staff still need to invest significant time in manual organization and revision, making it difficult to improve the quality and efficiency of medical document generation.

[0070] Based on this, embodiments of this application provide a method for intelligent generation and quality control of medical documents, referring to... Figure 1 The intelligent generation and quality control method for medical documents includes steps S100 to S600, wherein:

[0071] Step S100: Acquire multi-source medical data and preprocess it to obtain standardized medical data and patient individual characteristic data;

[0072] Step S200: Based on the standardized medical data, extract clinical entities and identify the relationships between entities, construct a patient-centered medical semantic graph network, and obtain semantic graph data containing nodes and edges; the nodes represent clinical entities, and the edges represent clinical relationships between entities;

[0073] Step S300: Based on the semantic graph data and the patient individual characteristic data, clinical logical reasoning is performed through a dynamic reasoning engine to identify potential clinical abnormal patterns and predict risks, and to obtain clinical reasoning result data.

[0074] Step S400: Based on the clinical reasoning result data and the standardized medical data, generate initial draft data of medical documents that conform to clinical logic;

[0075] Step S500: Compare and verify the initial draft data of the medical document with the semantic graph data to obtain verified medical document data;

[0076] Step S600: Display the verified medical document data to medical staff, receive modification operations from medical staff on the medical document data, obtain modification feedback data, update and adjust the semantic graph data based on the modification feedback data, and obtain updated semantic graph data.

[0077] In this embodiment, multi-source medical data refers to comprehensive medical information originating from various systems and equipment within the hospital, external medical data, and wearable devices on the patient's end. Internal hospital data includes electronic medical record systems, medical imaging systems, and laboratory information systems; external data includes patient-held examination reports and medical records from other hospitals; and patient-end data comes from customized wearable health monitoring devices. In addition to conventional data types such as patient history, physical examination, laboratory test results, imaging reports, medication records, and vital signs, the data also includes image-based data from external medical records, dynamic vital sign data collected by wearable devices, and patient interaction feedback data. External examination reports and medical records can be uploaded to the system via photography. The system uses image recognition, optical character recognition, and natural language processing technologies to convert images to text and further performs semantic analysis of the text content, extracting clinical entities and relational information, and incorporating it into the multi-source medical data system. Standardized medical data refers to medical data that has undergone preprocessing, such as data cleaning, format standardization, encoding conversion, and de-identification, to conform to specific data standards and specifications. This data facilitates efficient storage, retrieval, and analysis by computer systems. Patient individual characteristic data refers to information reflecting the individual specificity of a patient, such as unstructured or semi-structured data like age, gender, height, weight, allergy history, family medical history, past medical history, and lifestyle habits. Clinical entities refer to conceptual units with clear semantics and independent meaning in the medical field, such as specific symptoms (e.g., fever, cough), signs (e.g., elevated blood pressure, tachycardia), diagnoses (e.g., pneumonia, diabetes), treatment measures (e.g., antibiotic treatment, surgery), medications (e.g., aspirin), and laboratory tests (e.g., complete blood count, liver function). A medical semantic graph network is a model that represents a patient's clinical state and medical knowledge using a graph structure. In this network, nodes represent different clinical entities, and edges represent various clinical relationships between these entities, such as temporal, causal, spatial, and therapeutic relationships. This network can intuitively demonstrate the occurrence and development of a patient's disease and the interactions between various clinical factors. A dynamic inference engine is a computational module capable of processing and analyzing medical semantic graph network data in real time. The dynamic inference engine also integrates wearable device data parsing, patient status assessment, risk warning triggering, and doctor-patient interaction response functions. It can monitor patient recovery status based on real-time data from wearable devices and accurately predict clinical risks through multi-dimensional analysis. It also supports intelligent interactive answers to general medical inquiries from patients. Clinical logical reasoning refers to simulating the thinking activities of clinicians during diagnosis and treatment. This reasoning process utilizes structured knowledge in medical semantic graph networks and individual patient data, employing a series of logical rules and algorithms to deeply analyze the patient's clinical state, identify abnormal patterns, assess disease risks, and provide support for medical decision-making.

[0078] In this embodiment, the intelligent generation and quality control method for medical documents first acquires and preprocesses multi-source medical data to obtain standardized medical data and patient individual characteristic data. For example, raw medical data can be obtained from sources such as hospital information systems, laboratory systems, imaging systems, patient handheld terminals, and customized wearable health monitoring devices through methods such as manual entry, file import, database connection, collection of photos of outpatient materials, and automatic uploading from wearable devices. Subsequently, this raw data undergoes preliminary cleaning and format conversion, such as removing duplicates, standardizing date formats, converting text data into structured data, and denoising, correcting, and semantically converting the images of outpatient materials collected by photography, thereby forming preliminary standardized medical data. At the same time, information such as the patient's age, gender, and past medical history can be extracted from their medical records or outpatient converted medical records as patient individual characteristic data. For data collected by wearable devices, the system can automatically receive and parse the data according to a preset time frequency, perform structured processing on the vital sign data and patient response status data collected by the device, and simultaneously incorporate them into the standardized medical data system.

[0079] In this embodiment, secondly, based on the standardized medical data, clinical entities are extracted and relationships between entities are identified, thereby constructing a patient-centered medical semantic graph network to obtain semantic graph data containing nodes and edges. Specifically, keyword matching or rule matching can be used to identify clinical entities such as symptoms, diagnoses, and treatments from the standardized medical data, including clinical entities extracted after semantic transformation of outpatient data, patient recovery status entities parsed from wearable device data, and consultation and response entities extracted from doctor-patient interactions. Subsequently, through preset simple association rules, such as based on time sequence or co-occurrence frequency, the possible relationships between these entities are initially determined. For example, if "fever" and "cough" appear in the same medical record, they are considered to have some kind of association; if the wearable device collects "persistently elevated blood pressure" and the patient reports "dizziness," they are considered to have a potential causal relationship. Then, the identified clinical entities are used as nodes, and the initially determined relationships are used as edges to construct a basic graph structure, forming semantic graph data. In this semantic graph data, each node represents a clinical entity, and each edge represents the clinical relationship between entities.

[0080] In this embodiment, based on the semantic graph data and the patient's individual characteristic data, a dynamic reasoning engine performs clinical logical reasoning to identify potential clinical abnormal patterns and predict risks, obtaining clinical reasoning result data. For example, the dynamic reasoning engine can make a preliminary judgment on the information in the semantic graph based on preset simple logical rules, such as "if the patient has symptoms A and B, then there may be disease C." By traversing the nodes and edges in the semantic graph, patterns that match these simple rules are identified and marked as potential abnormal patterns. The engine can combine patient recovery status data collected periodically by wearable devices to continuously assess changes in the patient's condition during treatment. If potential risk patterns such as worsening condition or failure to meet expectations are identified, risk warning information is generated according to preset risk level judgment rules, and real-time reminders can be sent to medical staff via SMS, system messages, etc. In addition, the dynamic reasoning engine can also connect to the patient's interactive portal to receive general medical consultations initiated by patients. Based on a preset medical knowledge base and patient individual data in the semantic graph, it provides standardized answers to patients and stores information such as consultation questions, answers, and interaction times in a structured manner, incorporating them into the patient's medical data system. Simultaneously, based on individual characteristics such as the patient's age and medical history, combined with some empirical risk assessment indicators, the engine can make rough predictions of potential future risks, thereby generating preliminary clinical reasoning data. For abnormal vital signs detected by wearable devices, the engine will perform real-time risk prediction and trigger corresponding early warning processes, ensuring that medical staff can promptly grasp the patient's condition and replace the traditional manual, scheduled ward rounds to complete the routine recording of the patient's recovery status.

[0081] In this embodiment, based on the clinical reasoning result data and the standardized medical data, a preliminary draft of the medical document, conforming to clinical logic, is then generated. Specifically, based on the abnormal patterns and predicted risks identified in the clinical reasoning result data, and key information in the standardized medical data, such as diagnoses, treatment plans, historical medical information from outpatient records, patient recovery status data recorded by wearable devices, and consultation and response information from doctor-patient interactions, a narrative text can be manually or through simple text splicing. For example, information such as diagnostic results, main symptoms, treatment recommendations, daily changes in vital signs monitored by the patient's wearable device, and historical results of outpatient examinations can be arranged in a fixed order to form the preliminary content of the medical document.

[0082] Furthermore, the initial draft of the medical document is compared and verified with the semantic graph data to obtain verified medical document data. For example, the initial draft of the medical document can be manually read and compared with the clinical facts represented in the semantic graph data to check for any statements in the document that do not match the semantic graph, or for any omissions of important information contained in the semantic graph, including key clinical information converted from outpatient data, patient recovery status data recorded by wearable devices, and important content of doctor-patient interactions. Any inconsistencies or omissions found are manually corrected to obtain verified medical document data.

[0083] In this embodiment, the verified medical document data is finally displayed to medical staff, who then modify the data, generating modification feedback data. Based on this feedback data, the semantic graph data is updated and adjusted to obtain updated semantic graph data. For example, after reviewing the verified medical document data, medical staff may modify the document content based on their clinical experience or new diagnostic information, including patient status data subsequently collected by wearable devices, new outpatient medical records, and subsequent doctor-patient interactions. These modifications, such as adding new diagnoses, modifying treatment plans, or supplementing symptom descriptions, are recorded by the system, forming modification feedback data. Subsequently, the system can perform corresponding addition, deletion, and modification operations on the original semantic graph data based on this feedback data, either manually or through simple rules, thereby maintaining the consistency of the semantic graph data with the latest clinical information and obtaining updated semantic graph data. Simultaneously, medical staff can manually review and adjust the risk warning results from wearable devices and the answers to patient inquiries; related operation records are also synchronously updated to the semantic graph data, achieving closed-loop management of medical data.

[0084] In this embodiment, a patient-centered medical semantic graph network is constructed to achieve a deep understanding of the semantic relationships among multi-source medical data. In particular, it integrates semantic data converted from photos of outpatient materials, patient recovery status data collected by wearable devices, and doctor-patient interaction data, enriching the dimensions of medical data. This method can perform clinical logical reasoning based on semantic graph data, identify potential clinical abnormality patterns and predict risks, achieve timed recording and risk warning of patient recovery status through wearable devices, and support intelligent interaction for general medical consultations. It overcomes the limitations of manual ward rounds and information gaps in doctor-patient communication in traditional diagnosis and treatment, and overcomes the shortcomings of traditional systems in clinical reasoning and logical narrative construction. Therefore, it can generate accurate initial drafts of medical documents that conform to clinical logic, and supports feedback and corrections from medical staff to continuously optimize the semantic graph, effectively improving the quality of medical document generation and work efficiency.

[0085] In one feasible implementation, the steps of extracting clinical entities and identifying relationships between entities based on the standardized medical data, and constructing a patient-centered medical semantic graph network to obtain semantic graph data containing nodes and edges include: identifying and extracting clinical entity information from the standardized medical data to obtain clinical entity information data; matching and linking the clinical entity information data with standard medical terms in a preset medical knowledge base to obtain standardized clinical entity data; analyzing the temporal, causal, spatial, and therapeutic relationships between clinical entities in the standardized clinical entity data to obtain entity relationship data; and constructing a multi-level medical semantic graph network with the patient as the central node, the standardized clinical entity data as child nodes, and the entity relationship data as edges to obtain the semantic graph data.

[0086] In this embodiment, clinical entity information is identified and extracted from the standardized medical data to obtain clinical entity information data. The aim is to identify and extract clinically significant entities from preprocessed standardized medical data. Clinical entities may include, but are not limited to, patient symptoms, signs, diagnoses, treatment plans, test results, drug information, surgical records, etc. This can be achieved using Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER) models. These entities can be identified from unstructured text data (such as medical records and examination reports) through pre-trained medical domain models or methods such as rule matching and dictionary lookup. For structured data (such as fields in electronic medical records), the corresponding field content is directly extracted.

[0087] In this embodiment, the clinical entity information data is then matched and linked with standard medical terms in a preset medical knowledge base to obtain standardized clinical entity data. The purpose of this step is to map the identified original clinical entity information to a unified and standardized medical terminology system to eliminate heterogeneity, ambiguity, and synonym issues. The preset medical knowledge base may include standard medical terminology sets such as the International Classification of Diseases (ICD), the International Classification of Health and Disabilities (ICF), Medical Subject Headings (MeSH), and SNOMED CT. Matching and linking can be achieved through string similarity algorithms (such as edit distance and Jaccard similarity), semantic similarity calculations (such as word vector embedding), or rule-based mapping. For example, "fever" is matched with "elevated body temperature" in SNOMED CT.

[0088] Based on this, the temporal, causal, spatial, and therapeutic relationships among the clinical entities in the standardized clinical entity data are analyzed to obtain entity relationship data. This step identifies various logical and semantic associations between standardized clinical entities. Temporal relationships refer to the chronological order or duration of events occurring in an entity, such as "coughing occurred first, followed by fever," which can be established by analyzing timestamp information or event sequences in medical records. Causal relationships refer to one entity being the cause or result of another entity's occurrence, such as "infection leads to fever," which typically requires learning from a large amount of clinical data based on a pre-defined medical causal knowledge base or through machine learning models. Spatial relationships refer to the location of entities on a patient's body, such as "inflammation in the left lung," which can be identified through an anatomical knowledge base or descriptions in medical imaging reports. Therapeutic relationships refer to the association between treatment measures and diseases, symptoms, or test results, such as "aspirin is used to treat fever," which can be established based on clinical practice guidelines, drug instructions, or treatment protocol knowledge bases. Implementation methods can include rule-based pattern matching, machine learning (such as relation extraction models), or manual annotation and verification combined with medical expert knowledge.

[0089] In this embodiment, ultimately, a multi-layered medical semantic graph network is constructed with the patient as the central node, the standardized clinical entity data as child nodes, and the entity relationship data as edges, resulting in the semantic graph data. This step organizes all the previously extracted and analyzed information into a structured graph network. Having the patient as the central node means that each graph network is built around a specific patient, and all other clinical entities and relationships are associated with that patient. Standardized clinical entity data serves as child nodes, meaning that each standardized clinical entity (e.g., "hypertension," "diabetes," "headache") becomes a node in the graph. Entity relationship data serves as edges, meaning that identified temporal, causal, spatial, and treatment relationships serve as edges connecting these nodes, and each edge can have type and attributes (e.g., relationship strength, time interval). Multi-layered refers to the graph network containing information of different granularities; for example, a symptom node can be further expanded into a more detailed description, or different types of entities (e.g., diagnosis, treatment) can form different layers. The construction process typically involves graph database technology, storing and indexing nodes and edges for efficient querying and reasoning.

[0090] In this embodiment, through the above technical solution, this application can systematically identify, standardize, and associate various clinical entities from standardized medical data, thereby overcoming the challenge of constructing a unified semantic representation from heterogeneous and complex medical data. Constructing a multi-layered medical semantic graph network centered on the patient not only ensures the integrity and accuracy of the graph data but also provides a solid and structured knowledge foundation for subsequent clinical logical reasoning. This refined graph construction method enables the dynamic reasoning engine to more accurately identify potential clinical anomalies and predict risks, improving the intelligence and reliability of medical document generation and quality control.

[0091] In one feasible implementation, the step of matching and linking the clinical entity information data with standard medical terms in a preset medical knowledge base to obtain standardized clinical entity data includes: reading a standard medical terminology database from the preset medical knowledge base; the standard medical terminology database contains standard names and codes for symptom terms, sign terms, laboratory test terms, diagnostic terms, and treatment terminology; performing similarity matching between the medical terms in the clinical entity information data and the standard medical terminology database, identifying the closest standard terms, and obtaining matching result data; for clinical entities that are successfully matched, replacing the original expressions with the standard names and codes in the standard medical terminology database to obtain preliminary standardized data; for clinical entities that cannot be directly matched, performing semantic reasoning and classification based on contextual semantic analysis and medical background knowledge to generate supplementary standard terminology data; merging the preliminary standardized data and the supplementary standard terminology data to uniformly convert all clinical entities into the standardized expression form in the standard medical terminology database, thereby obtaining the standardized clinical entity data.

[0092] In this embodiment, a standard medical terminology database is retrieved from a pre-defined medical knowledge base. This database is professionally constructed and maintained, containing standard naming and coding for symptom terms, sign terms, laboratory test terms, diagnostic terms, and treatment terminology. For example, the database may adopt standard systems such as the International Classification of Diseases (ICD), Systematic Nomenclature Medicine (SNOMED CT), or Medical Subject Headings (MeSH), ensuring the standardization and uniqueness of medical terminology and providing an authoritative reference for subsequent standardization processes.

[0093] In this embodiment, the medical terms in the clinical entity information data are matched with the standard medical terminology database to identify the closest standard term, thereby obtaining the matching result data. This matching process can employ various techniques. For example, string-based similarity algorithms (such as Levenshtein distance and Jaccard similarity) can be used for fuzzy matching to handle spelling errors or subtle variations; or semantic similarity calculation methods based on word vectors (such as Word2Vec and BERT) can be used to capture the deep semantic relationships between medical terms, effectively identifying terms with similar meanings even if their expressions differ. Through these methods, even if there are subtle differences between the original expression and the standard terminology, the most suitable standard correspondence can be effectively identified.

[0094] In this embodiment, for successfully matched clinical entities, the original descriptions are replaced with standard names and codes from a standard medical terminology database to obtain preliminary standardized data. This step ensures that identified clinical entities can be uniformly represented using standardized medical terminology, eliminating potential inconsistencies in the original data and laying a unified foundation for subsequent data processing.

[0095] Furthermore, for clinical entities that cannot be directly matched, this application performs semantic reasoning and classification based on contextual semantic analysis and medical background knowledge to generate supplementary standard terminology data. Specifically, the system analyzes the contextual information of the unmatched entity in the original medical data, such as the words before and after it, and the sentences or paragraphs it belongs to, to infer its potential meaning. Simultaneously, it combines pre-defined medical background knowledge, such as common symptoms of diseases, diagnostic criteria, or treatment pathways, to achieve a deeper semantic understanding of the entity. For example, if the word "chest tightness" does not directly match, but the context mentions "abnormal electrocardiogram" and "difficulty breathing," the system can infer that it may refer to "cardiovascular symptoms." By using machine learning models (such as Transformer-based sequence labeling models) or expert rule systems, these entities are classified or mapped to the closest standard terms, thereby compensating for the shortcomings of simple matching and effectively handling non-standard, ambiguous, or newly emerging medical terms.

[0096] In this embodiment, the preliminary standardized data and the supplementary standardized terminology data are finally merged to convert all clinical entities into standardized expressions in the standard medical terminology database, resulting in the standardized clinical entity data. This merging process ensures that standardized entities, whether obtained through direct matching or semantic reasoning, can be integrated into a unified and standardized dataset, providing complete and high-quality input for the subsequent construction of the medical semantic graph network.

[0097] In this embodiment, through the above technical solution, when matching and linking clinical entity information data with standard medical terms in a preset medical knowledge base, this application can not only efficiently process directly matched clinical entities, but more importantly, for clinical entities that cannot be directly matched, it can perform in-depth semantic reasoning and classification based on contextual semantic analysis and medical background knowledge. This effectively solves the problems of diversity, non-standardization, and the emergence of new terms in clinical terminology, improving the accuracy and coverage of clinical entity standardization. Ultimately, all clinical entities can be uniformly converted into standardized expressions in the standard medical terminology database, thus providing high-quality, highly consistent basic data for the subsequent construction of the medical semantic graph network, greatly improving the reliability of the semantic graph network and the accuracy of clinical logical reasoning, thereby ensuring the professionalism and rigor of medical document generation and quality control.

[0098] In one feasible implementation, the step of analyzing the temporal, causal, spatial, and therapeutic relationships among clinical entities in the standardized clinical entity data to obtain entity relationship data includes: analyzing the chronological order of different clinical entities in the standardized clinical entity data to establish temporal relationship chain data; based on a preset medical causal knowledge base, analyzing the causal relationship between symptom entities and sign entities, the indicative relationship between sign entities and laboratory indicator entities, the supporting relationship between laboratory indicator entities and diagnostic entities, and the treatment plan relationship between diagnostic entities and treatment measure entities in the standardized clinical entity data to establish causal inference chain data; based on clinical diagnosis and treatment guidelines, analyzing the synergistic or conflicting relationships among different treatment measure entities in the standardized clinical entity data to establish treatment association network data; and integrating the temporal relationship chain data, causal inference chain data, and treatment association network data to obtain the complete entity relationship data.

[0099] In this embodiment, the system analyzes the chronological order of different clinical entities in the standardized clinical entity data to establish a temporal relationship chain, aiming to identify the temporal sequence of clinical events. This is crucial for understanding disease progression and evaluating treatment effectiveness. For example, by analyzing the timestamp information of each clinical entity in the medical data, such as consultation time, examination time, and medication time, these entities can be accurately sorted. The system can utilize time series analysis algorithms, such as rule-based time event sorting or timestamp association analysis, to construct the temporal relationship chain. For instance, if the timestamp of the "fever" entity is earlier than that of the "blood routine examination" entity, the system will establish a temporal relationship that "fever" precedes "blood routine examination," thus clearly showing the order of events.

[0100] In this embodiment, based on a pre-set medical causal knowledge base, the system analyzes the causal relationships between symptom entities and sign entities, the indicative relationships between sign entities and laboratory indicator entities, the supporting relationships between laboratory indicator entities and diagnostic entities, and the treatment plan relationships between diagnostic entities and treatment measure entities in the standardized clinical entity data. This establishes causal reasoning chain data, aiming to reveal the deep logical connections between clinical events, which forms the basis for clinical diagnosis and treatment decisions. The pre-set medical causal knowledge base can be a structured database containing various causal rules defined by medical experts, such as "a certain bacterial infection causes fever," "fever indicates an inflammatory response," "elevated C-reactive protein supports a bacterial infection diagnosis," and "a diagnosis of pneumonia recommends antibiotic treatment." The system extracts potential causal phrases or patterns from the standardized clinical entity data using Natural Language Processing (NLP) technology and matches them with rules in the knowledge base. Upon successful matching, the system establishes corresponding causal reasoning chains; for example, connecting "symptom entities" and "sign entities" through a "causal" relationship, thereby constructing an association with a clear causal orientation.

[0101] In this embodiment, based on clinical practice guidelines, the system analyzes the synergistic or conflicting relationships between different treatment entities in the standardized clinical entity data to establish a treatment association network. This aims to ensure the rationality and safety of treatment plans, avoid potential drug interactions or treatment conflicts, and identify treatment potential with synergistic effects. The clinical practice guidelines can be stored in the form of rule sets, guidelines, or ontology. The system compares the treatment entities in the standardized clinical entity data with these guidelines. For example, if a guideline states that "the simultaneous use of drug A and drug B may lead to serious side effects," the system will establish a "conflict" relationship between drug A and drug B. Conversely, if a guideline recommends "postoperative physical therapy," the system will establish a "synergistic" relationship between the surgical entity and the physical therapy entity. This can be achieved through a rule engine or graph matching algorithm to ensure the scientific validity and safety of the treatment plan.

[0102] In this embodiment, the temporal relationship chain data, causal reasoning chain data, and treatment association network data are integrated to obtain complete entity relationship data. The aim is to unify relationship information from different dimensions, forming a comprehensive and multi-dimensional representation of entity relationships, providing a rich and accurate foundation for subsequent semantic graph construction and clinical reasoning. The integration process can treat these different types of relationships as different types of edges in the semantic graph. For example, temporal relationships can be represented as edges with temporal attributes, causal relationships as edges with causal type labels, and treatment relationships as edges with cooperative or conflict labels. Seamless data integration is achieved by structurally storing this relationship data in the same graph database or constructing a unified graph structure in memory.

[0103] In this embodiment, by establishing temporal relationship chain data, the sequence and trajectory of clinical events can be clearly displayed; by establishing causal reasoning chain data, the inherent logical connections between symptom entities, sign entities, test indicator entities, diagnostic entities, and treatment measure entities can be revealed, providing a basis for understanding disease mechanisms and treatment effects; by establishing treatment association network data, the synergy or conflict between different treatment measure entities can be effectively identified, ensuring the rationality and safety of treatment plans. The integration of these multi-dimensional relationships enables the constructed medical semantic graph network to not only contain rich clinical entity information but also possess powerful logical reasoning capabilities, more accurately reflecting the patient's true clinical state and disease progression patterns. This improves the accuracy and reliability of subsequent dynamic reasoning engines in performing clinical logical reasoning, thereby more effectively identifying potential clinical abnormal patterns and predicting risks, laying a solid foundation for generating high-quality, logically rigorous initial drafts of medical documents, and improving the effectiveness of medical document quality control.

[0104] In one feasible implementation, the steps of performing clinical logical reasoning through a dynamic reasoning engine based on the semantic graph data and the patient's individual characteristic data to identify potential clinical abnormal patterns and predict risks, and obtain clinical reasoning result data include: traversing all nodes and edges in the semantic graph data to identify whether there are contradictory clinical information, obtaining contradiction detection result data; analyzing whether there are logical breaks or missing information links in the clinical development path of the semantic graph data, obtaining integrity analysis result data; matching whether there are high-risk combination patterns in the semantic graph data based on a preset medical risk knowledge base, wherein the high-risk combination patterns include specific combinations of multiple symptom entities and sign entities, obtaining risk pattern recognition result data; predicting possible future clinical events based on the current clinical state of the semantic graph data, combined with a preset disease development law knowledge base and the patient's individual characteristic data, and calculating the potential risk level according to preset risk assessment standards, obtaining predictive analysis result data; and comprehensively analyzing the contradiction detection result data, integrity analysis result data, risk pattern recognition result data, and predictive analysis result data to generate the clinical reasoning result data containing specific suggestions and basis.

[0105] In this embodiment, when traversing all nodes and edges in the semantic graph data to identify whether there is contradictory clinical information and obtain contradiction detection result data, this step aims to ensure the internal consistency of the patient's clinical information and avoid subsequent reasoning based on erroneous or conflicting information. Specifically, the system can preset a series of clinical conflict rules, such as certain diagnoses being logically mutually exclusive, or the simultaneous occurrence of a specific drug and certain symptoms being unreasonable. Then, by traversing all nodes (representing clinical entities) and edges (representing relationships between entities) in the medical semantic graph network, the system checks whether these preset rules are violated one by one. Once it finds that there are entities or relationship combinations in the semantic graph that simultaneously satisfy the conflict rules—for example, contradictory diagnostic records within the same time period, or a certain drug appearing simultaneously with a contraindication—it is marked as contradictory information, and corresponding contradiction detection result data is generated.

[0106] In this embodiment, when analyzing the clinical development path in the semantic graph data to determine if there are logical breaks or missing information, and obtaining completeness analysis results, this step is used to assess the continuity and completeness of the patient's treatment process, identifying potentially overlooked key treatment steps or data. Specifically, the system can construct standardized treatment process templates based on preset disease treatment path models, clinical guidelines, or expert experience. Subsequently, the actual patient treatment process reflected in the medical semantic graph network is compared with these standard templates. For example, for a specific disease, if the standard treatment path requires a key examination after diagnosis, but the semantic graph lacks a record of this examination, it will be identified as a missing information step. Similarly, if a patient's treatment record is suddenly interrupted after a certain point in time, lacking subsequent follow-up or treatment records, it may be identified as a logical break, and corresponding completeness analysis results will be generated.

[0107] In this embodiment, based on a preset medical risk knowledge base, the system matches the semantic graph data to determine if there are high-risk combination patterns. These high-risk combination patterns include specific combinations of multiple symptom entities and sign entities. When obtaining risk pattern recognition result data, this step aims to proactively identify specific high-risk situations that patients may face, enabling timely intervention. The preset medical risk knowledge base stores a large amount of knowledge related to disease risks. For example, specific drug combinations may lead to serious adverse reactions, or specific combinations of certain symptoms and signs may indicate the occurrence of a critical illness. The system utilizes this knowledge base to perform pattern matching on clinical entities (such as symptom entities, sign entities, diagnostic entities, treatment entities, etc.) and their interrelationships in the medical semantic graph network. Once a combination of entities in the semantic graph matches a high-risk combination pattern defined in the knowledge base—for example, a specific combination of symptoms and signs such as "chest pain," "dyspnea," and "ST segment elevation on ECG"—it is identified as a high-risk pattern, and risk pattern recognition result data is generated.

[0108] In this embodiment, based on the current clinical state of the semantic graph data, combined with a preset disease development pattern knowledge base and the patient's individual characteristic data, the system predicts potential future clinical events and calculates the potential risk level according to preset risk assessment criteria to obtain predictive analysis results. This step aims to provide forward-looking risk warnings and event predictions to assist clinical decision-making. The preset disease development pattern knowledge base contains information such as typical evolution paths of different diseases at different stages, possible complications, and treatment effects. The system first extracts the patient's current clinical state information from the medical semantic graph network, including the current diagnosis, symptoms, signs, test results, and treatment plan. Subsequently, it compares and analyzes these current clinical states with the disease development pattern knowledge base, and combines individual characteristic data such as the patient's age, gender, past medical history, and genetic background, using machine learning models or expert systems to infer and predict possible clinical events in the future, such as disease deterioration, complications, and poor treatment effects. Simultaneously, according to preset risk assessment criteria, the probability of occurrence and potential severity of each predicted event are calculated to obtain predictive analysis results.

[0109] In this embodiment, when comprehensively analyzing the contradiction detection results, integrity analysis results, risk pattern recognition results, and predictive analysis results to generate the clinical reasoning results data containing specific recommendations and justifications, this step aims to integrate all reasoning results to form comprehensive and actionable clinical decision support information. The system will summarize, prioritize, and perform correlation analysis on the aforementioned detection and prediction results. For example, if contradiction detection finds information conflicts and risk pattern recognition finds high risk, the comprehensive analysis will provide more urgent recommendations. The final generated clinical reasoning results data not only includes the identified problems but also provides specific clinical recommendations (such as recommendations for follow-up examinations, medication adjustments, and close observation) and supporting evidence for these recommendations (such as "based on the combination of symptom A and sign B found in the semantic graph data, combined with the risk knowledge base, a high risk of disease C is indicated"), thereby providing medical staff with comprehensive, accurate, and interpretable clinical decision support.

[0110] In this embodiment, by comprehensively traversing all nodes and edges in the semantic graph data, this application can systematically identify contradictions in clinical information, effectively avoiding misjudgments caused by data inconsistencies. Simultaneously, by analyzing logical gaps and missing information in the clinical development path, the integrity and continuity of the diagnosis and treatment process are ensured, compensating for the shortcomings of traditional methods that may miss key information. Furthermore, combined with a pre-set medical risk knowledge base, this application can accurately match and identify high-risk combination patterns, achieving early warning of potential critical situations. In addition, by integrating a disease development pattern knowledge base and patient individual characteristic data, this application can prospectively predict future clinical events and their potential risks, providing valuable decision support information for medical personnel. Finally, by synthesizing these multi-dimensional analysis results, clinical reasoning result data containing specific suggestions and evidence is generated, greatly improving the comprehensiveness, accuracy, and practicality of clinical reasoning, thereby enhancing the intelligence level and security of the intelligent generation and quality control method for medical documents.

[0111] In one feasible implementation, the step of matching the semantic graph data for high-risk combination patterns based on a preset medical risk knowledge base, where the high-risk combination patterns include specific combinations of multiple symptom entities and sign entities, to obtain risk pattern recognition result data includes: reading predefined risk combination rules from the preset medical risk knowledge base, where the risk combination rules define the risk level and clinical significance when multiple clinical entities appear simultaneously; matching all clinical entities in the semantic graph data one by one with the risk combination rules, identifying entity combinations that meet the rule requirements, and obtaining entity combination matching results; for each identified entity combination in the entity combination matching results, calculating its risk score and determining its risk level, and obtaining risk level assessment data, where the risk score is based on the number, importance, and interrelationship strength of the entities in the combination; and generating content containing high-risk warning information and processing suggestions based on the risk level assessment data, as the risk pattern recognition result data.

[0112] In this embodiment, predefined risk combination rules are read from a preset medical risk knowledge base. These rules define the risk level and clinical significance when multiple clinical entities appear simultaneously. The preset medical risk knowledge base is a database specifically storing medical expertise, with the predefined risk combination rules at its core. These rules describe, in a structured form, the potential high-risk pattern represented when multiple specific clinical entities (e.g., symptoms, signs, test results, etc.) appear simultaneously in a patient's semantic graph data. Each rule not only clarifies the combination of entities constituting a high risk but also specifies the corresponding risk level (e.g., low, medium, high risk, or a specific risk score range) and its specific clinical significance (e.g., indicating an early warning of a disease, risk of complications, adverse drug reactions, etc.). The implementation of these rules can include, but is not limited to, using ontology or rule engine-based logical expressions, such as "if (symptom A AND symptom B AND test C) coexist, the risk level is high, and the clinical significance is an early manifestation of disease X."

[0113] In this embodiment, all clinical entities in the semantic graph data are matched one by one with the risk combination rules to identify entity combinations that meet the rule requirements, thus obtaining entity combination matching results. This step aims to systematically compare the patient's current clinical state (represented by nodes in the semantic graph data, i.e., clinical entities) with risk patterns defined in a preset medical risk knowledge base. One-by-one matching means that the system traverses all clinical entity combinations existing in the semantic graph data and attempts to compare these combinations with preset risk combination rules. The matching process can employ graph pattern matching algorithms, such as subgraph isomorphism detection, or rule engine-based reasoning mechanisms to determine whether the set of entities in the semantic graph satisfies the conditions of a certain risk combination rule. The entity combination matching result is a list of all successfully matched risk patterns, with each match clearly indicating which risk rule was triggered and the specific clinical entity combination that triggered the rule.

[0114] In this embodiment, for each identified entity combination in the entity combination matching results, its risk score is calculated and its risk level is determined, resulting in risk level assessment data. The risk score is based on the number, importance, and strength of the relationships between entities in the combination. After identifying potential risky entity combinations, they need to be quantitatively assessed. The risk score is a numerical value used to accurately measure the risk level of a specific entity combination. The calculation of this score is multi-dimensional: first, the number of clinical entities included in the combination is considered; generally, the more entities, the stronger the risk indication. Second, the importance of each entity is considered; for example, certain key symptoms or test indicators themselves have high risk indication significance, and their weight in the risk score calculation should be higher. Finally, the strength of the relationships between entities is considered; for example, causal relationships or strong associations may indicate higher risk than weak associations. The risk level is a classification of the risk score; for example, the score can be divided into discrete levels such as "low," "medium," and "high" to facilitate clinical decision-making and risk communication. The risk level assessment data is a detailed record containing the risk score and corresponding risk level for each identified entity combination.

[0115] In this embodiment, based on the risk level assessment data, content including high-risk warning information and treatment suggestions is generated as the risk pattern recognition result data. This step transforms the quantified risk assessment results into information that medical staff can understand and act upon. The high-risk warning information clearly indicates the specific high-risk pattern currently present in the patient, the involved clinical entities, and their corresponding risk levels. The treatment suggestions are targeted intervention measures or further examination recommendations extracted from a pre-set medical risk knowledge base or clinical guidelines based on these high-risk patterns, such as "It is recommended to immediately perform a certain examination," "Consider adjusting the treatment plan," and "Closely monitor a certain indicator." This information collectively constitutes the risk pattern recognition result data, aiming to provide medical staff with timely and accurate risk alerts and decision support.

[0116] In this embodiment, through the above technical solution, this application can read structured risk combination rules from a pre-defined medical risk knowledge base. These rules clearly define the risk level and clinical significance when multiple clinical entities appear simultaneously, thus providing a precise basis for identifying high-risk patterns. By matching clinical entities in semantic graph data with these rules one by one, entity combinations that conform to predefined high-risk patterns can be systematically identified, avoiding subjective judgment or information omissions. Furthermore, by comprehensively considering the number of entities, their importance, and the strength of their interrelationships to calculate risk scores and determine risk levels, risk assessment becomes more refined and quantifiable, accurately distinguishing different levels of risk and providing more instructive risk warnings for medical staff. Finally, content containing specific high-risk warning information and handling suggestions is generated, which not only improves the accuracy and efficiency of risk identification but also provides timely and clear intervention directions for clinical decision-making, enhancing the risk management capabilities of intelligent medical document generation and quality control methods.

[0117] In one feasible implementation, the steps of predicting potential future clinical events based on the current clinical state of the semantic graph data, combined with a preset disease development pattern knowledge base and the patient's individual characteristic data, and calculating the potential risk level according to preset risk assessment criteria to obtain predictive analysis result data include: acquiring natural development trajectory data of a specific disease or clinical state based on the preset disease development pattern knowledge base; comparing and analyzing the current clinical state of the semantic graph data with the natural development trajectory data to identify the current state's position in the natural development trajectory and obtain state position information; predicting potential clinical events within a preset time period based on the state position information and the patient's individual characteristic data to obtain event prediction data; calculating the probability of occurrence and potential risk level of each predicted clinical event in the event prediction data according to preset risk assessment criteria to obtain risk assessment data; and generating a prediction report containing prevention recommendations and intervention timing based on the risk assessment data as the predictive analysis result data.

[0118] In this embodiment, the pre-defined disease development pattern knowledge base is a structured database that stores a large amount of typical pathways, time-series information, and characteristic descriptions of different clinical states for various diseases from their occurrence, development, and outcome. This data can be derived from medical literature, clinical guidelines, expert consensus, or large-scale clinical data mining. Natural development trajectory data refers to the typical patterns of disease or specific clinical state evolution over time under conditions of no external intervention or specific treatment. Acquiring this data aims to provide a medical benchmark for subsequent patient state comparisons, ensuring the accuracy and rationality of predictions. For example, for diabetic patients, this knowledge base might contain a typical timeline from impaired glucose tolerance to the onset of complications and the clinical manifestations at each stage.

[0119] In this embodiment, the current semantic graph data comprehensively reflects the patient's real-time clinical entities and the relationships between them, thus characterizing the patient's current clinical state. Comparative analysis involves matching the patient's current clinical manifestations (such as symptoms, signs, test results, diagnoses, etc.) with natural progression trajectory data in a pre-defined disease development knowledge base to determine which stage of the disease or which specific clinical state the patient is currently in. State location information is the result of the comparative analysis; it quantitatively or qualitatively describes the relative position of the patient's current state within the natural progression of the disease. For example, this can be achieved by calculating the similarity between the patient's current symptom set and the typical symptom sets at each stage of the disease, or by using a time-series matching algorithm to align the patient's clinical event sequence with the typical progression sequence of the disease.

[0120] In this embodiment, the state location information provides the patient's precise coordinates on the disease progression trajectory, while the patient's individual characteristic data (e.g., age, gender, genotype, past medical history, comorbidities, lifestyle habits, medication use, etc.) reflects the patient's specific factors. Combining these two types of information, the system can utilize advanced predictive models, such as deep learning models based on Markov chains, Hidden Markov Models, Recurrent Neural Networks (RNNs), or Long Short-Term Memory Networks (LSTMs), to infer clinical events the patient may experience within a specific future time window (e.g., in the next few hours, days, weeks, or months). These clinical events may include disease deterioration, complication occurrence, treatment response, recovery progress, or specific adverse events. The event prediction data is a collection of these predictions, typically including the event type, the predicted time window, and the initial probability of occurrence.

[0121] In this embodiment, the preset risk assessment criteria are a set of rules or models that define the risk levels, assessment indicators, and calculation methods for different clinical events. For each predicted clinical event identified in the event prediction data, the system calculates its precise probability of occurrence (e.g., expressed as a percentage) and potential risk level (e.g., classified as low, medium, or high risk) based on these criteria, comprehensively considering the nature of the event itself, the individual characteristics of the patient, and the likelihood of the event occurring. Risk assessment data is structured information containing these quantitative calculation results, providing a clear quantitative basis for subsequent prevention and intervention measures. This can be achieved by integrating multiple risk scoring models (such as the Framingham risk score for cardiovascular disease, the SOFA score for infection, etc.) or a machine learning-based risk classifier.

[0122] In this embodiment, the prediction report is the final output presented to healthcare professionals. It transforms complex risk assessment data into easily understandable and actionable clinical recommendations. Prevention recommendations refer to specific, actionable preventative measures provided by the system for predicted high-risk events, such as adjusting medication dosages, increasing the frequency of vital sign monitoring, recommending specific examinations, and guiding patients on lifestyle interventions. Intervention timing refers to the optimal time to take these preventative or treatment measures to minimize or mitigate adverse events or to seize the best treatment window. This prediction report aims to provide timely and personalized support for clinical decision-making, helping healthcare professionals proactively manage patient risks and optimize treatment processes.

[0123] In this embodiment, through the above-described technical solution, this application can accurately predict potential future clinical events based on the patient's current clinical state, combined with the natural progression of the disease and the patient's individual characteristics. Specifically, by acquiring natural progression trajectory data of a specific disease or clinical state and comparing the patient's current semantic graph data with these trajectories, the patient's position in the disease progression can be accurately identified. Based on this, combined with the patient's individual characteristics, it is possible to more accurately predict clinical events that may occur within a preset time period, and calculate their probability of occurrence and potential risk level according to preset risk assessment criteria. Finally, a prediction report containing specific prevention recommendations and intervention timing is generated, providing timely and personalized clinical decision support for medical staff. This effectively solves the problems of insufficient generalization and difficulty in providing targeted recommendations in traditional prediction methods, improving the accuracy and practicality of clinical risk prediction, thereby optimizing the quality of medical documentation and clinical quality control.

[0124] In one feasible implementation, the steps of generating a draft medical document that conforms to clinical logic based on the clinical reasoning result data and the standardized medical data include: selecting a corresponding document template from a preset document template library according to the type of medical document, wherein the document template defines the structural framework and required content items of the document; organizing the key findings and conclusions in the clinical reasoning result data into a coherent narrative text according to the clinical logical order to obtain narrative text data; filling the specific numerical and time information in the standardized medical data into the corresponding positions in the narrative text data to generate basic information content data; adding explanations and transitional statements to the basic information content data in conjunction with the clinical development context in the semantic graph data to form logically coherent complete text data; and combining and arranging the basic information content data and the complete text data according to the structural framework of the document template to generate the draft medical document that conforms to the standard format.

[0125] In this embodiment, a corresponding document template is selected from a preset document template library based on the type of medical document. The document template defines the document's structural framework and required content items. The document template is a predefined data structure or file used to guide the generation of medical documents. Its core function is to provide a unified structural framework and required content items for different types of medical documents (e.g., admission records, discharge summaries, progress notes, surgical records, etc.). The structural framework may include hierarchical relationships such as titles, chapters, paragraphs, and lists, while the required content items specify the key information points that the document must include, such as basic patient information, chief complaint, present illness, past medical history, physical examination, auxiliary examination results, diagnosis, treatment plan, and condition assessment. The selection of a document template can be implemented by having medical staff specify the document type through a user interface, with the system automatically matching the corresponding template; or the system can intelligently recommend suitable templates based on the current clinical scenario (e.g., patient admission, discharge). These templates are typically stored in a parsable format (e.g., XML, JSON, or a specific markup language) so that the program can recognize their structural and content requirements.

[0126] Based on this, the key findings and conclusions from the clinical reasoning results data are organized into a coherent narrative text according to clinical logical order, resulting in narrative text data. The clinical reasoning results data may contain a series of discrete, structured reasoning conclusions, such as "the patient has a risk of hypertension" or "a kidney function test is recommended." Transforming these discrete conclusions into narrative text aims to make them more suitable for the reading habits and clinical thinking processes of healthcare professionals. The organization method can be based on pre-defined narrative rules or Natural Language Generation (NLG) models, mapping entities, attributes, and relationships in the reasoning results to natural language expressions. For example, "patient-exists-risk of hypertension" can be transformed into "the patient currently has a risk of hypertension." The organization according to clinical logical order means that the text generation will follow the chronological or causal sequence of disease occurrence and development, diagnostic process, and treatment intervention, ensuring the fluency and rationality of the narrative.

[0127] In this embodiment, the specific numerical values ​​and time information from the standardized medical data are then filled into the corresponding positions in the narrative text data to generate basic information content data. Standardized medical data includes various objective indicators of the patient, such as vital signs (body temperature, blood pressure, heart rate), test results (complete blood count, biochemical indicators), medication dosage, and medication time. This data is an indispensable component of medical documents, providing specific and quantifiable factual evidence for the narrative text. The filling process typically involves reserving placeholders in the narrative text or using semantic matching technology to precisely insert the corresponding standardized medical data (such as body temperature, blood pressure, medication time, etc.) into the text. This filling ensures the accuracy and detail of the document content.

[0128] In this embodiment, to further enhance the logical coherence and readability of the document, explanatory and transitional statements are added to the basic information content data, based on the clinical development context in the semantic graph data, forming a logically coherent and complete text data. The semantic graph data represents the patient's clinical entities and their interrelationships in the form of nodes and edges, clearly demonstrating the clinical development context of the disease, the diagnostic process, and the treatment effect. By analyzing the temporal, causal, and treatment relationships in the semantic graph, the system can intelligently generate transitional statements connecting different clinical events (such as "Due to..., the patient experienced...", "Subsequently, after... treatment, the condition improved"), as well as statements explaining certain clinical phenomena. For example, when the semantic graph shows a strong causal relationship between a symptom and a diagnosis, the system can automatically add the explanation "Given the patient's [symptom entity], the preliminary diagnosis is [diagnosis entity]". This makes the generated text not merely a list of information, but possesses clear clinical logic and narrative depth.

[0129] In this embodiment, the basic information content data and the complete text data are combined and arranged according to the structural framework of the document template to generate the initial draft data of the medical document that conforms to the standardized format. The predefined structural framework of the document template (such as chapter titles, paragraph order, list format, etc.) ensures the standardization of the final document format. The combination and arrangement process involves placing the text content generated in the previous steps, which contains specific information and logical coherence, into the corresponding chapters and positions according to the template. For example, the patient's chief complaint and present medical history are filled into the "Medical History" chapter, and the diagnosis and treatment plan are filled into the "Diagnosis and Treatment" chapter. This step ensures that the generated initial draft of the medical document is not only accurate in content and logically clear, but also fully conforms to the requirements of medical institutions or industry standards in form, providing medical staff with a standardized document that can be directly reviewed and modified.

[0130] In this embodiment, through the above technical solution, this application can efficiently transform discrete clinical reasoning results data and standardized medical data into a clear, logically rigorous, and narratively coherent draft of medical documents. Guided by a preset document template, the standardization of document format is ensured, avoiding errors and inconsistencies that may occur with manual typesetting. Key findings and conclusions from clinical reasoning results are organized into narrative text and filled with specific numerical and time information from standardized medical data, ensuring the completeness and accuracy of the document content. More importantly, by combining the clinical development context from semantic graph data and adding explanatory and transitional statements, the document is not merely a simple accumulation of data, but rather presents a clear outline of the patient's clinical development and treatment logic, greatly improving the readability and clinical usability of medical documents, reducing the burden of manual writing and organization for medical staff, and providing a high-quality benchmark for subsequent quality control, thereby improving the efficiency and quality of medical document generation.

[0131] In one feasible implementation, the step of comparing and verifying the initial draft data of the medical document with the semantic graph data to obtain verified medical document data includes: extracting all mentioned clinical entities and relationship descriptions from the initial draft data of the medical document to obtain document content entity data; comparing the document content entity data with the nodes and edges in the semantic graph data one by one to check for inconsistencies in expression or missing information, and obtaining comparison result data; marking the specific location and problem type for inconsistencies found in the comparison result data, and obtaining problem labeling data, wherein the problem types include entity expression errors, relationship description errors, information omissions, and logical contradictions; automatically correcting or providing correction suggestions for inconsistencies in the problem labeling data based on the semantic graph data to ensure that the document content is completely consistent with the underlying semantic representation, and obtaining corrected document data; generating a consistency verification report based on the corrected document data, recording all verification processes and results, and obtaining the verified medical document data.

[0132] In this embodiment, all mentioned clinical entities and relational descriptions are extracted from the initial draft data of medical documents to obtain document content entity data. This step aims to transform unstructured initial draft data of medical documents into structured semantic information that can be understood and compared by machines. Specifically, Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER) and Relation Extraction (RE), can be used to parse the text information in the document. The NER model can identify various clinical entities in the document, such as diseases, symptoms, drugs, test results, treatment plans, etc. The RE model further analyzes the semantic relationships between these entities, such as "diagnosed as," "taken," "caused," etc. Through these techniques, the text information in the document is parsed into a series of structured entity-relation triples, forming document content entity data, laying the foundation for subsequent accurate comparison.

[0133] Subsequently, the entity data of the document content is compared one by one with the nodes and edges in the semantic graph data to check for inconsistencies in expression or missing information, thus obtaining the comparison results. The core of this step lies in performing fine-grained information verification. Each entity and relation in the document content entity data is matched with the corresponding node and edge in the semantic graph data. During the comparison process, not only is the complete consistency of entity names and relation types checked, but also various expression forms such as synonyms, near-synonyms, and abbreviations are considered. For example, through word vector similarity calculation, ontology mapping, and other techniques, it is determined whether "hypertension" in the document and "primary hypertension" in the semantic graph refer to the same concept. At the same time, it also checks whether key information in the semantic graph is reflected in the document, and whether information in the document has a corresponding counterpart in the semantic graph, thereby discovering inconsistencies in expression or missing information.

[0134] Furthermore, for inconsistencies found in the comparison results data, the specific location and problem type are marked, resulting in problem-marked data. These problem types include entity description errors, relationship description errors, information omissions, and logical contradictions. This step further refines and categorizes the comparison results. Once an inconsistency is found, the system precisely locates the specific text fragment in the initial draft data and classifies it into a specific problem type based on predefined rules or machine learning models. For example, if the entity name in the document does not match the standard name in the semantic graph, it is marked as "entity description error"; if the relationship between entities described in the document does not match the relationship in the semantic graph, it is marked as "relationship description error"; if an important node or edge in the semantic graph is not reflected in the document, it is marked as "information omission"; if the description in the document conflicts with the clinical logic in the semantic graph (e.g., a mismatch between diagnosis and treatment plan), it is marked as "logical contradiction". This refined marking facilitates subsequent corrections and quality control.

[0135] Building upon this foundation, and based on semantic graph data, the system automatically corrects inconsistencies in the labeled data or provides correction suggestions, ensuring complete consistency between the document content and the underlying semantic representation, resulting in corrected document data. This step is crucial for achieving intelligent quality control. For labeled issues, the system prioritizes automatic correction. For example, for incorrect entity descriptions, non-standard expressions in the document can be directly replaced with standard terms from the semantic graph; for missing information, relevant content can be automatically supplemented based on information from the semantic graph. For complex issues that cannot be automatically corrected or require manual confirmation (such as logical contradictions), the system provides detailed correction suggestions based on the semantic graph data, including suggested modifications, the basis for the modifications, and potential impacts, for medical staff to refer to. In this way, the initial draft data of medical documents is ensured to maintain a high degree of consistency with the underlying medical semantic graph data at the semantic level.

[0136] In this embodiment, a consistency verification report is generated based on the corrected document data, recording all verification processes and results to obtain the verified medical document data. This report not only includes the final verified medical document data but also records the entire verification process, including all problems found, problem types, details of automatic corrections, provided correction suggestions, and the final correction results. This report provides medical staff with a transparent quality control record, helps trace the document generation and correction process, and serves as proof of document quality.

[0137] In this embodiment, unstructured text in the initial draft data is transformed into structured document content entity data, enabling the machine to perform accurate semantic understanding and comparison. Secondly, through detailed comparison with patient-centered medical semantic graph data, not only can simple inconsistencies or missing information be detected, but deeper logical contradictions can also be identified, thereby improving the depth and breadth of quality control. Furthermore, for the problems discovered, the system can automatically correct them or provide intelligent correction suggestions based on the semantic graph data, greatly reducing the burden of manual review and modification by medical staff and improving the efficiency and accuracy of document generation. The final consistency verification report provides traceable evidence of the quality of medical documents, ensuring that the content of the medical documents is completely consistent with the underlying semantic representation of the patient's actual clinical situation, thereby effectively reducing medical risks and ensuring medical quality.

[0138] In the embodiments of this application, the intelligent generation and quality control method for medical documents acquires multi-source medical data, constructs a medical semantic graph network, performs clinical logical reasoning, generates and verifies medical documents, and updates them based on feedback. It can automatically construct a patient-specific medical knowledge network, identify deep semantic relationships between clinical entities, generate logically rigorous professional descriptions, and improve the quality of medical documents and medical safety.

[0139] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the intelligent generation and quality control method of medical documents in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0140] This application also provides a medical document intelligent generation and quality control system, see reference. Figure 2 The intelligent medical document generation and quality control system includes: a memory 10, a processor 20, and an intelligent medical document generation and quality control program stored on the memory 10 and executable on the processor 20. The intelligent medical document generation and quality control program is configured to implement the steps of the intelligent medical document generation and quality control method.

[0141] The intelligent medical document generation and quality control system provided in this application, employing the intelligent medical document generation and quality control method described in the above embodiments, can improve the quality of medical documents and medical safety. Compared with the prior art, the beneficial effects of the intelligent medical document generation and quality control system provided in this application are the same as those of the intelligent medical document generation and quality control method provided in the above embodiments, and other technical features of the intelligent medical document generation and quality control system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0142] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0143] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A method for intelligent generation and quality control of medical documents, characterized in that, The method includes: Acquire multi-source medical data and preprocess it to obtain standardized medical data and individual patient characteristic data; Based on the standardized medical data, clinical entities are extracted and the relationships between entities are identified. A patient-centered medical semantic graph network is constructed to obtain semantic graph data containing nodes and edges; the nodes represent clinical entities and the edges represent clinical relationships between entities. Based on the semantic graph data and the patient's individual characteristic data, a dynamic reasoning engine is used to perform clinical logical reasoning, identify potential clinical abnormal patterns and predict risks, and obtain clinical reasoning result data. Based on the clinical reasoning results data and the standardized medical data, a draft of medical documents that conforms to clinical logic is generated. The initial draft data of the medical document is compared and verified with the semantic graph data to obtain verified medical document data; The verified medical document data is displayed to medical staff, and the medical staff's modification operations on the medical document data are received to obtain modification feedback data. Based on the modification feedback data, the semantic graph data is updated and adjusted to obtain the updated semantic graph data. Based on the semantic graph data and the individual patient characteristic data, the steps of performing clinical logical reasoning through a dynamic reasoning engine to identify potential clinical abnormal patterns and predict risks, and obtaining clinical reasoning result data include: Traverse all nodes and edges in the semantic graph data to identify whether there are contradictory clinical information and obtain contradiction detection result data; The semantic graph data is analyzed to determine whether there are logical gaps or missing information in the clinical development path, and the completeness analysis results are obtained. Based on a pre-set medical risk knowledge base, the semantic graph data is matched to see if there are high-risk combination patterns. The high-risk combination patterns include specific combinations of multiple symptom entities and sign entities to obtain risk pattern recognition result data. Based on the current clinical status of the semantic graph data, combined with the preset disease development pattern knowledge base and the patient's individual characteristic data, the clinical events that may occur in the future are predicted, and the degree of potential risk is calculated according to the preset risk assessment criteria to obtain the predictive analysis results data. The contradiction detection results, integrity analysis results, risk pattern recognition results, and predictive analysis results are comprehensively analyzed to generate the clinical reasoning results data, which include specific recommendations and evidence. The steps of comparing and verifying the initial draft data of the medical document with the semantic graph data to obtain the verified medical document data include: Extract all mentioned clinical entities and relationship descriptions from the initial draft data of the medical document to obtain the document content entity data; The document content entity data is compared one by one with the nodes and edges in the semantic graph data to check for inconsistencies in expression or missing information, and the comparison result data is obtained. For any inconsistencies found in the comparison results data, the specific location and problem type are marked to obtain problem marking data. The problem types include entity description errors, relationship description errors, information omissions, and logical contradictions. Based on the semantic graph data, inconsistencies in the problem-marked data are automatically corrected or correction suggestions are provided to ensure that the document content is completely consistent with the underlying semantic representation, resulting in corrected document data. Based on the corrected document data, a consistency verification report is generated, recording all verification processes and results, to obtain the verified medical document data.

2. The intelligent generation and quality control method for medical documents as described in claim 1, characterized in that, Based on the standardized medical data, the steps of extracting clinical entities and identifying relationships between entities, constructing a patient-centered medical semantic graph network, and obtaining semantic graph data containing nodes and edges include: Clinical entity information is identified and extracted from the standardized medical data to obtain clinical entity information data; The clinical entity information data is matched and linked with standard medical terms in a preset medical knowledge base to obtain standardized clinical entity data. Analyze the temporal, causal, spatial, and therapeutic relationships among the clinical entities in the standardized clinical entity data to obtain entity relationship data; Using the patient as the central node, the standardized clinical entity data as child nodes, and the entity relationship data as edges, a multi-layered medical semantic graph network is constructed to obtain the semantic graph data.

3. The intelligent generation and quality control method for medical documents as described in claim 2, characterized in that, The steps of matching and linking the clinical entity information data with standard medical terms in a preset medical knowledge base to obtain standardized clinical entity data include: The standard medical terminology database is retrieved from a pre-set medical knowledge base; the standard medical terminology database contains standard names and codes for symptom terms, sign terms, laboratory test terms, diagnostic terms, and treatment terminology. The medical terms in the clinical entity information data are matched with the standard medical terminology database to identify the closest standard terms and obtain the matching result data. For successfully matched clinical entities, the original expressions are replaced with standard names and codes from the standard medical terminology database to obtain preliminary standardized data. For clinical entities that cannot be directly matched, semantic reasoning and classification are performed based on contextual semantic analysis and medical background knowledge to generate supplementary standard terminology data; The preliminary standardized data and the supplementary standard terminology data are merged and processed to convert all clinical entities into standardized expressions in the standard medical terminology database, thus obtaining the standardized clinical entity data.

4. The intelligent generation and quality control method for medical documents as described in claim 2, characterized in that, The steps for analyzing the temporal, causal, spatial, and therapeutic relationships among clinical entities in the standardized clinical entity data to obtain entity relationship data include: Analyze the chronological order of different clinical entities in the standardized clinical entity data to establish a temporal relationship chain data; Based on a pre-set medical causal knowledge base, the causal relationship between symptom entities and sign entities, the indicative relationship between sign entities and test indicator entities, the supporting relationship between test indicator entities and diagnostic entities, and the treatment plan relationship between diagnostic entities and treatment measure entities are analyzed in the standardized clinical entity data to establish causal reasoning chain data. Based on clinical diagnosis and treatment guidelines, the collaborative or conflicting relationships between different treatment measures entities in the standardized clinical entity data are analyzed to establish treatment association network data. The time relationship chain data, causal reasoning chain data, and treatment association network data are integrated to obtain the complete entity relationship data.

5. The intelligent generation and quality control method for medical documents as described in claim 1, characterized in that, Based on a pre-defined medical risk knowledge base, the steps of matching the semantic graph data to determine whether there are high-risk combination patterns, where the high-risk combination patterns include specific combinations of multiple symptom entities and sign entities, and obtaining risk pattern recognition result data include: The system reads predefined risk combination rules from a pre-defined medical risk knowledge base. These risk combination rules define the risk level and clinical significance when multiple clinical entities occur simultaneously. All clinical entities in the semantic graph data are matched one by one with the risk combination rules to identify entity combinations that meet the rule requirements and obtain entity combination matching results. For each identified entity combination in the entity combination matching results, its risk score is calculated and the risk level is determined to obtain risk level assessment data. The risk score is based on the number of entities, their importance and the strength of their interrelationships in the combination. Based on the risk level assessment data, content including high-risk early warning information and handling suggestions is generated as the risk pattern recognition result data.

6. The intelligent generation and quality control method for medical documents as described in claim 1, characterized in that, Based on the current clinical status of the semantic graph data, combined with a pre-set disease development pattern knowledge base and the patient's individual characteristic data, the steps for predicting potential future clinical events and calculating the degree of potential risk according to pre-set risk assessment criteria to obtain predictive analysis results include: Based on a pre-defined knowledge base of disease development patterns, data on the natural development trajectory of a specific disease or clinical condition is obtained. The clinical status of the current semantic graph data is compared and analyzed with the natural development trajectory data to identify the position of the current status in the natural development trajectory and obtain the status position information; Based on the status location information and combined with the patient's individual characteristic data, predict the clinical events that may occur within a preset time period in the future to obtain event prediction data; For each predicted clinical event in the event prediction data, its probability of occurrence and potential risk level are calculated according to the preset risk assessment criteria to obtain risk assessment data; Based on the risk assessment data, a predictive report containing prevention recommendations and intervention timing is generated as the predictive analysis result data.

7. The intelligent generation and quality control method for medical documents as described in claim 1, characterized in that, The steps for generating initial draft data of medical documents that conform to clinical logic based on the clinical reasoning results data and the standardized medical data include: Select the appropriate document template from the preset document template library according to the type of medical document. The document template defines the structural framework and required content items of the document. The key findings and conclusions in the clinical reasoning results data are organized into a coherent narrative text according to the clinical logical order to obtain narrative text data. The specific numerical values ​​and time information from the standardized medical data are filled into the corresponding positions in the narrative text data to generate basic information content data; By combining the clinical development context in the semantic graph data, explanatory and transitional statements are added to the basic information content data to form logically coherent complete text data. According to the structural framework of the document template, the basic information content data and the complete text data are combined and arranged to generate the first draft data of the medical document that conforms to the standard format.

8. A medical document intelligent generation and quality control system, characterized in that, The intelligent medical document generation and quality control system includes: a memory, a processor, and an intelligent medical document generation and quality control program stored in the memory and executable on the processor, wherein the intelligent medical document generation and quality control program is configured to implement the steps of the intelligent medical document generation and quality control method as described in any one of claims 1 to 7.

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