Traditional Chinese medicine clinical closed-loop intelligent syndrome differentiation system and knowledge evolution method

Abstract

The invention provides a traditional Chinese medicine clinical closed-loop dialectical auxiliary system and a knowledge evolution method, and belongs to the technical field of medical information. The system can be realized in the form of software, intelligent equipment or a combination thereof, is used as an auxiliary decision-making tool of a clinician, and comprises: 1, a clinical information acquisition module, which is used for standardly acquiring four-diagnosis information of a patient, intelligent equipment data and diagnosis and treatment feedback, and ensuring the accuracy and integrity of the data; the auxiliary diagnosis module is used for analyzing multi-source clinical information based on a large language model, providing interpretable dialectical suggestions and helping doctors to make more accurate diagnosis; the knowledge evolution module is used for extracting, verifying and continuously optimizing clinical diagnosis and treatment experience, and real-time updating and effectiveness of the knowledge base are ensured. The dialectical knowledge base is used for storing and managing standardized dialectical specifications, diagnosis and treatment schemes and evolution records of the diagnosis and treatment schemes and supporting standardization and normalization of clinical practice. According to the method, a closed-loop mechanism of clinical practice-auxiliary diagnosis-knowledge accumulation-optimization application is established, intelligent auxiliary decision support is provided for traditional Chinese medicine clinical practice on the premise of ensuring medical safety, and the method has important application value. The specific security measures comprise data encryption, access control and patient privacy protection, and the compliance and security of the system are ensured.

Description

Technical Field

[0001] This invention belongs to the field of medical information technology, specifically to a TCM syndrome differentiation decision-making support system based on clinical practice and its knowledge evolution method, including but not limited to a software system, intelligent terminal devices, and their combined applications. This system can be widely used in TCM diagnosis and treatment, medical education, clinical research, and other fields. Background Art

[0002] Traditional Chinese Medicine diagnosis is based on the four diagnostic methods of "looking, listening, asking and feeling". However, the following problems exist in clinical practice:

[0003] 1. Clinical experience is difficult to accumulate effectively, and there is a lack of a systematic knowledge extraction mechanism;

[0004] 2. Dialectical analysis lacks feedback verification, making continuous optimization and improvement difficult;

[0005] 3. The knowledge base is updated lagging behind and cannot reflect the progress of clinical practice in a timely manner;

[0006] 4. Lack of intelligent syndrome differentiation support, making it difficult to fully utilize historical diagnosis and treatment experience;

[0007] 5. Existing systems generally use static knowledge bases and lack dynamic evolution capabilities;

[0008] 6. The disconnect between clinical practice and theoretical research affects the improvement of syndrome differentiation results.

[0009] Existing solutions often fail to effectively address the above problems, resulting in low efficiency of TCM diagnosis and affecting clinical results. Summary of the Invention

[0010] The purpose of this invention is to provide a closed-loop syndrome differentiation support system and knowledge evolution method for Traditional Chinese Medicine (TCM) clinical practice. By establishing a closed-loop mechanism of clinical practice-assisted diagnosis-knowledge accumulation-optimized application, this system provides auxiliary decision support for TCM clinical practice and enables the continuous optimization and evolution of TCM diagnostic and treatment knowledge. The innovation of this system lies in its dynamic knowledge evolution capability and intelligent decision-making support.

[0011] Technical Solution

[0012] The decision support system provided by the present invention adopts a closed-loop architecture and can be deployed independently or used in conjunction with intelligent medical equipment. It mainly includes:

[0013] 1. Clinical information collection module

[0014] οStandardized four-diagnosis information collection

[0015] οFeedback record of diagnosis and treatment effect

[0016] οReal-time collection of clinical experience

[0017] οData association analysis and processing

[0018] οSupport smart device data access

[0019] οMulti-source data standardization processing

[0020] 2. Auxiliary diagnosis module

[0021] οSemantic understanding based on large language models

[0022] οAssisted syndrome differentiation analysis

[0023] οGenerate syndrome differentiation suggestions

[0024] οAnalysis of the explainability of the reasoning process

[0025] οSmart device data analysis

[0026] οMultimodal data fusion processing

[0027] 3. Knowledge Evolution Module

[0028] οClinical experience extraction and verification

[0029] οDynamic optimization of knowledge rules

[0030] οContinuous improvement of syndrome differentiation plan

[0031] οKnowledge base updated in real time

[0032] 4. Syndrome Differentiation Knowledge Base

[0033] οStandard and specification management

[0034] οClinical case accumulation

[0035] οKnowledge graph construction

[0036] οEvolutionary record tracking

[0037] Beneficial effects

[0038] The present invention has the following beneficial effects:

[0039] 1. Achieve a closed knowledge loop: establish a benign interaction between clinical practice and theoretical knowledge to improve diagnosis and treatment efficiency.

[0040] 2. Continuous optimization capability: Dynamic optimization of knowledge is achieved through feedback verification to ensure real-time updating of the knowledge base.

[0041] 3. Decision support: Provide explainable dialectical suggestions to assist doctors in diagnosis and improve diagnostic accuracy.

[0042] 4. Experience inheritance and innovation: Promote the accumulation and development of TCM clinical experience and promote the advancement of TCM theory.

[0043] 5. Practical guidance: Optimized knowledge assists clinical application and improves patient treatment outcomes. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the system module;

[0045] Figure 2 This is a closed-loop system architecture diagram;

[0046] Figure 3 It is the process of collecting clinical information;

[0047] Figure 4 Schematic diagram of knowledge evolution mechanism;

[0048] Figure 5 It is the structure of dialectical knowledge base;

[0049] Figure 6 Deploy the architecture for the system. DETAILED DESCRIPTION

[0050] System Architecture

[0051] The present invention adopts a closed-loop architecture design:

[0052] 1. Data collection layer: standardized collection of clinical information

[0053] 2. Analysis and processing layer: intelligent dialectics and knowledge evolution

[0054] 3. Knowledge storage layer: dynamic knowledge base management

[0055] 4. Application service layer: clinical practice guidance

[0056] Key technology implementation

[0057] 1. Clinical knowledge evolution mechanism

[0058] Algorithm 1: Clinical Knowledge Evolution

[0059] Input: Clinical practice data D and feedback F

[0060] Output: Evolved knowledge base K

[0061] 1. Initialize knowledge base K

[0062] 2. While (new clinical data available):

[0063] 2.1 Collect clinical data and feedback

[0064] 2.2 Extract valuable experience

[0065] 2.3 Validate new knowledge

[0066] 2.4 Update knowledge base

[0067] 2.5 Apply to clinical practice

[0068] 3. Return evolved knowledge base K

[0069] 2. Intelligent syndrome differentiation process

[0070] Intelligent Pattern Differentiation Algorithm (Algorithm 2: Intelligent Pattern Differentiation)

[0071] Input: Clinical information C

[0072] Output: Diagnosis suggestion S with explanation E

[0073] 1. Parse clinical information

[0074] 2. Extract key features

[0075] 3. Match with knowledge base

[0076] 4. Generate diagnosis suggestion

[0077] 5. Provide reasoning explanation

[0078] 6. Return suggestion and explanation

[0079] 3. Knowledge Graph Update

[0080] Knowledge Graph Update Algorithm (Algorithm 3: Knowledge Graph Update)

[0081] Input: New clinical knowledge N

[0082] Output: Updated knowledge graph G

[0083] 1. Evaluate knowledge relevance

[0084] 2. Verify knowledge reliability

[0085] 3.Update graph structure

[0086] 4. Optimize relationships

[0087] 5. Record evolution history

[0088] 6. Return updated graph

[0089] Data model design

[0090] The system's data model mainly includes:

[0091] 1. Clinical Information Model

[0092] οBasic information of the patient

[0093] ο Four diagnostic data records

[0094] οFeedback on diagnosis and treatment effects

[0095] οClinical experience records

[0096] 2. Knowledge Evolution Model

[0097] οKnowledge rule definition

[0098] οVerification evaluation indicators

[0099] οOptimize update records

[0100] οEvolution process tracking

[0101] 3. Dialectical Standard Model

[0102] οStandard terminology system

[0103] ο Dialectical rules library

[0104] οScheme template library

[0105] οPractical Case Library

[0106] System deployment

[0107] 1. Basic environment requirements

[0108] οApplication Server

[0109] οDistributed storage system

[0110] οLarge language model service

[0111] οKnowledge graph database

[0112] οSmart device access gateway

[0113] οData conversion adapter

[0114] 2. Extended service configuration

[0115] οLoad balancing service

[0116] οMessage queue system

[0117] οCache service

[0118] οMonitoring and alarm services

[0119] οEquipment management services

[0120] οData synchronization service

[0121] 3. Security measures

[0122] οData encryption transmission and storage

[0123] οRole-based access control

[0124] οComplete operation log audit

[0125] οData backup and disaster recovery mechanism

[0126] οDesensitization of patient privacy information

[0127] οComply with medical data security regulations

[0128] οRegular security assessments and updates

[0129] Usage Restrictions

[0130] 1. System positioning:

[0131] οThis system is only used as an auxiliary tool for doctors' diagnosis and treatment

[0132] The final diagnostic decision rests with the clinician

[0133] οSystem suggestions are for reference only

[0134] 2. Usage requirements:

[0135] οMust be performed by a licensed physician

[0136] οRequires standardized training before use

[0137] οStrictly abide by the management regulations of medical institutions

[0138] 3. Data Usage:

[0139] οStrictly comply with patient data privacy protection regulations

[0140] οData collection requires informed consent from patients

[0141] ο Ensure that data is used ethically

[0142] Application Scenario

[0143] The present invention is applicable to:

[0144] 1. Traditional Chinese Medicine clinical syndrome differentiation auxiliary diagnosis

[0145] 2. Medical teaching and training

[0146] 3. Clinical experience inheritance

[0147] 4. Research on Traditional Chinese Medicine Theory

[0148] 5. Establishment of dialectical standards

[0149] 6. Telemedicine collaboration

[0150] 7. Construction of smart clinics

[0151] 8. Portable diagnostic equipment

[0152] 9. Mobile medical terminals

[0153] Implementation Method

[0154] The present invention can have the following embodiments:

[0155] 1. Software system form:

[0156] οIndependently deployed information systems

[0157] οHospital HIS system integration module

[0158] οMobile Application

[0159] 2. Smart device form:

[0160] οSmart clinic workstation

[0161] οPortable diagnostic terminal

[0162] οMobile medical equipment

[0163] οSmart wearable devices

[0164] 3. Hybrid application form:

[0165] οIntegrated hardware and software solutions

[0166] οCloud-edge-end collaborative system

[0167] οRemote diagnosis and treatment platform

[0168] o

[0169] Industrial Applicability

[0170] The present invention has significant industrial applicability:

[0171] 1. Advanced and reliable technical solutions:

[0172] οClosed-loop architecture design

[0173] οKnowledge evolution mechanism

[0174] οIntelligent syndrome differentiation support

[0175] 2. Implementation costs are controllable:

[0176] οModular deployment solution

[0177] οIncremental construction path

[0178] οOperation and maintenance cost optimization

[0179] 3. Significant application value:

[0180] οImprove diagnosis and treatment efficiency

[0181] οPromote experience inheritance

[0182] ο Promote theoretical development

[0183] in conclusion

[0184] This invention provides an intelligent decision-making assistance tool for the clinical practice of traditional Chinese medicine by establishing a clinical closed-loop syndrome differentiation assistance system. On the premise of ensuring medical safety, it realizes the continuous optimization and evolution of traditional Chinese medicine diagnosis and treatment knowledge, and has important application value and promotion prospects.

Claims

1. A closed-loop intelligent syndrome differentiation system for traditional Chinese medicine, characterized by: include: οClinical information collection module, used to standardize the collection and storage of patients' four-diagnosis information and treatment feedback, ensuring the accuracy and completeness of the data; ο Intelligent syndrome differentiation module, which is used to analyze clinical information based on a large language model and generate syndrome differentiation suggestions, providing an explainable reasoning process; οKnowledge evolution module, used to extract, verify and continuously optimize clinical diagnosis and treatment experience to ensure real-time updating of the knowledge base; ο Syndrome differentiation knowledge base, used to store and manage standardized syndrome differentiation norms, diagnosis and treatment plans and their evolution records, to support the standardization and normalization of clinical practice.

2. The system according to claim 1, wherein: The clinical information acquisition module includes: o Four diagnostic information collection unit, used to collect the patient's four diagnostic data; οClinical feedback collection unit, used to record diagnosis and treatment effects and patient feedback; οData normalization unit, used to ensure the standardization and integrity of data; o Data association analysis unit, used to establish associations between clinical information and support the integration of multi-source data.

3. The system according to claim 1, wherein: The intelligent syndrome differentiation module includes: ο Semantic understanding unit, used to parse the semantic content of clinical information; ο Dialectical reasoning unit, used to perform intelligent dialectics based on a large language model and provide explainable analysis results; ο Plan generation unit, used to provide syndrome differentiation basis and recommended plans to support clinical decision-making; οInterpretability analysis unit, used to explain the dialectical reasoning process and basis and enhance the transparency of the system.

4. The system according to claim 1, wherein: The knowledge evolution module includes: οExperience extraction unit, used to extract effective diagnosis and treatment experience from clinical practice and form standardized knowledge items; οKnowledge verification unit, used to verify the reliability and universality of newly added knowledge and ensure the validity of knowledge; οRule optimization unit, used to continuously optimize dialectical rules and solutions to improve the intelligence level of the system; οKnowledge update unit, used to dynamically update the dialectical knowledge base to ensure the timeliness of knowledge.

5. The system according to claim 1, wherein: The dialectical knowledge base includes: ο Standard specification library, which stores syndrome differentiation standards and diagnosis and treatment specifications to support clinical application; οClinical case library, which stores actual diagnosis and treatment records and effect feedback to promote experience inheritance; οKnowledge graph library, which stores the relational network of dialectical knowledge and supports intelligent analysis; οEvolution record library, which stores the update and optimization history of the knowledge base to ensure the traceability of knowledge.

6. A method for evolving clinical knowledge of traditional Chinese medicine based on the system according to any one of claims 1 to 5, characterized in that: The following steps are involved: (1) Collect clinical information and diagnosis and treatment feedback to ensure data accuracy; (2) Conduct intelligent dialectical analysis and solution generation, and provide explainable suggestions; (3) Extract and verify clinical diagnosis and treatment experience to form standardized knowledge items; (4) Optimize and update the dialectical knowledge base to ensure the timeliness of knowledge; (5) Apply optimized knowledge to guide clinical practice and improve diagnosis and treatment effects.

7. The method according to claim 6, characterized in that The step (3) specifically includes: (1) Analyze clinical diagnosis and treatment effects and patient feedback to extract effective diagnosis and treatment experience and rules; (2) Verify the reliability and scope of application of experience and form standardized knowledge items.

8. The method according to claim 6, characterized in that The step (4) specifically includes: (1) Evaluate the relevance of new knowledge to existing knowledge and ensure the integration of knowledge; (2) Integrate and update the knowledge graph structure and optimize the dialectical rules and solution library; (3) Record the knowledge evolution process to ensure the traceability of knowledge.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for evolving clinical knowledge of traditional Chinese medicine described in any one of claims 6 to 8 are implemented.

10. A computer device comprising a processor and a memory, characterized in that: The memory stores a computer program, and when the processor executes the computer program, the steps of the method for evolving clinical knowledge of traditional Chinese medicine described in any one of claims 6 to 8 are implemented.

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