Traditional Chinese medicine intelligent health diagnosis system and method based on multi-source data fusion

Abstract

The invention provides a traditional Chinese medicine intelligent health diagnosis system and method based on multi-source data fusion, and relates to the technical field of health diagnosis. The system comprises a database construction module which is used for collecting condition data and historical disease data of a patient and constructing a traditional Chinese medicine holographic health database. And the multi-dimensional classification module is used for establishing a multi-source data classification standard and adding category fields to the multi-source data in the traditional Chinese medicine holographic health database from multiple dimensions to obtain associated field data. The importance classification module is used for constructing an importance degree judgment standard and judging the importance degree of different associated field data to obtain importance labels, and the exclusive customization module is used for classifying health states according to different importance labels to obtain current health data and formulating exclusive treatment strategies. According to the method, the data are classified and associated from multiple dimensions, the organization and utilization efficiency of the data is improved, and the importance degree judgment method can provide a basis for priority processing and analysis of the data.

Description

Technical Field

[0001] The present invention relates to the field of health diagnosis technology, and in particular to a traditional Chinese medicine intelligent health diagnosis system and method based on multi-source data fusion. Background Art

[0002] With the continuous advancement of science and technology and the growing demand for personalized and precision medicine in the healthcare field, intelligent health diagnosis systems for Traditional Chinese Medicine (TCM) have gradually become a research hotspot. TCM data comes from a wide range of sources, including clinical cases, literature records, and patient feedback. The data types are diverse, covering structured data (such as laboratory test results), semi-structured data (such as medical records), and unstructured data (such as tongue images and pulse signals). Data from different sources are not uniform in format and lack standardized terminology and coding systems, making data integration difficult and hindering effective classification and analysis.

[0003] The importance assessment of Traditional Chinese Medicine (TCM) data lacks unified standards and methods, making it difficult to determine which data are most valuable for diagnosis and treatment. Data importance assessment often relies on expert experience, is highly subjective, and lacks objective quantitative indicators and automated assessment tools. Furthermore, due to inadequate data classification and importance assessment, a large amount of TCM data is not fully utilized, and its potential value is not effectively tapped. Furthermore, data organization and management methods are relatively primitive, making it difficult to support complex query and analysis needs, limiting the application of TCM data in clinical decision-making, scientific research, and health management. There is a lack of comprehensive assessment of patient health status and the development of personalized treatment strategies. Dynamically adjusting treatment plans based on a patient's specific health status and data characteristics is difficult, impacting treatment effectiveness and patient satisfaction.

[0004] In response to the above problems, the present invention proposes a traditional Chinese medicine intelligent health diagnosis system based on multi-source data fusion. Summary of the Invention

[0005] The present invention provides a TCM intelligent health diagnosis system and method based on multi-source data fusion, which is used to solve the defects of the existing technology that TCM data is complex, difficult to classify, and difficult to effectively evaluate the importance of different data.

[0006] In one aspect, the present invention provides a TCM intelligent health diagnosis system based on multi-source data fusion, comprising:

[0007] The database construction module is used to collect the patient's condition data and historical disease data, and use the multi-source holographic database construction method to build a traditional Chinese medicine holographic health database.

[0008] The multidimensional classification module is used to establish multi-source data classification standards, add category fields to the multi-source data in the TCM holographic health database from multiple dimensions to obtain associated field data. The multiple dimensions include etiology and pathogenesis, dialectical system, constitution and prevention of disease, data attributes and time and space attributes.

[0009] The importance grading module is used to construct importance judgment criteria, judge the importance of different related field data based on health characteristics, and obtain importance labels. Health characteristics include physiological indicator characteristics, physical function characteristics, psychological and adaptation characteristics, and lifestyle characteristics.

[0010] The exclusive customized module classifies health status according to different importance tags to obtain current health data and formulate exclusive treatment strategies.

[0011] According to the TCM intelligent health diagnosis system based on multi-source data fusion provided by the present invention, the steps of constructing a TCM holographic health database include:

[0012] The needs and goals of building a database are determined based on the usage objectives, purpose and user groups, and the basic information, symptom data and other health data of the patients are collected based on clinical cases, literature and patient feedback to form status data.

[0013] The patient's historical disease data is determined based on the patient's past medical history, family medical history and previous treatment plans.

[0014] Use data conversion tools to convert condition data and historical disease data from different sources into a standard format, and perform terminology standardization and data cleaning to obtain multi-source data.

[0015] The database structure is designed according to the needs, goals and characteristics of traditional Chinese medicine theory, and the database management system is selected according to the type of multi-source data and application requirements. The multi-source data is stored in the database to obtain a traditional Chinese medicine holographic health database.

[0016] According to a TCM intelligent health diagnosis system based on multi-source data fusion provided by the present invention, the step of obtaining associated field data includes:

[0017] Corresponding definition rules are determined from multiple dimensions, multiple classification tables are created according to different definition rules, and the association relationships between different classification tables are analyzed to obtain multi-information association data.

[0018] According to different definition rules, each data in the multi-source data is classified from multiple dimensions to obtain the corresponding category fields.

[0019] The multi-information related data is added according to the multiple category fields to obtain related field data.

[0020] According to a TCM intelligent health diagnosis system based on multi-source data fusion provided by the present invention, the steps of obtaining multi-information correlation data include:

[0021] Integrate data from different classification tables into a transaction database, where each transaction represents multiple related data records.

[0022] The minimum support threshold is set according to the frequent pattern growth algorithm, and transactions that meet the minimum support are found from the transaction database as frequent itemsets.

[0023] Generate association rules based on multiple frequent item sets, calculate the confidence of each association rule, and judge whether each confidence is greater than the preset confidence threshold. If so, it is regarded as a valid association rule, otherwise it is deleted.

[0024] According to the effective association rules, the association relationships between different classification tables are analyzed to obtain multi-information association data.

[0025] According to a TCM intelligent health diagnosis system based on multi-source data fusion provided by the present invention, the steps of classifying and obtaining corresponding category fields include:

[0026] By tracing the root cause of the disease from the etiology and pathogenesis, clarifying the diagnostic path and syndrome type from the dialectical system, identifying individual physical characteristics from the physical category, standardizing the data source and mode from the data attributes, and incorporating time and geographical factors from the spatiotemporal attributes, the corresponding classification value is determined.

[0027] Assign classification values ​​to each data in multi-source data, and define classification rules based on logical judgment of data features.

[0028] Each data is classified according to the classification rules, and symptom keywords, texture features and sound spectrum features are extracted from it. The traditional Chinese medicine terms are unified and the numerical values ​​are standardized.

[0029] Add initial fields corresponding to each dimension to each data record, match classification rules according to data content and features, and thus determine the categories on each dimension. Add the obtained multiple categories to the corresponding initial fields to obtain the corresponding category fields.

[0030] According to a TCM intelligent health diagnosis system based on multi-source data fusion provided by the present invention, the steps of establishing importance judgment criteria include:

[0031] Collect the health characteristics of the patients and calculate the weight of each feature in the health characteristics.

[0032] Get the feature score of each feature and calculate the importance of each data with the corresponding weight.

[0033] Set the importance judgment threshold range corresponding to each importance level. The importance levels are divided into level one, level two, and level three.

[0034] According to the importance level of each data, the importance of each data is judged, and the judgment result is used as the importance label of the data.

[0035] According to a TCM intelligent health diagnosis system based on multi-source data fusion provided by the present invention, the weight calculation process is:

[0036] Calculate the objective weight of each feature, the formula is expressed as:

[0037]

[0038] Where θ i represents the objective weight of the i-th feature, Y i represents the i-th eigenvalue, ∑Y i represents the sum of all data values ​​under the i-th feature, It represents the proportion of the i-th feature to all the data of this feature.

[0039] Calculate the subjective weight of each feature. The process includes:

[0040] Establish a pairwise comparison matrix Q, normalize each column of the matrix Q to obtain the matrix B, sum the normalized matrix B by row to obtain the vector C and normalize it to obtain the weight vector. The formula is expressed as:

[0041]

[0042] Where n represents the number of features, w i represents the subjective weight of the i-th feature, c i Represents the i-th element in the vector C obtained by summing the rows of the normalized matrix B, c k is the kth element in vector C.

[0043] Calculate the comprehensive weight of each feature, the formula is expressed as:

[0044] J i =σ*θ i +(1-σ)*w i

[0045] Where, J i It represents the comprehensive weight of the i-th feature, σ is the adjustment coefficient, and its value range is (0, 1).

[0046] According to a TCM intelligent health diagnosis system based on multi-source data fusion provided by the present invention, the step of obtaining a feature score for each feature includes:

[0047] The specific manifestations of each feature are extracted from the health features, and the initial scores are obtained by labeling them according to the importance and relevance of the health features.

[0048] And build a scoring model based on the decision tree, input specific performance, and output the initial score of the current data.

[0049] Specific performance scores are obtained based on the initial scores, and the physiological indicator characteristic scores, physical function characteristic scores, psychological and adaptation characteristic scores, and lifestyle habit characteristic scores are calculated.

[0050] According to the TCM intelligent health diagnosis system based on multi-source data fusion provided by the present invention, the steps of formulating a dedicated treatment strategy include:

[0051] A health status classification standard is formulated based on importance labels and traditional Chinese medicine theory, and all data with importance labels are integrated to obtain a health dataset.

[0052] The patient's health status is classified according to the health status classification standard and combined with the importance label to obtain the current health status.

[0053] Analyze the current health status to identify the main health problems and risk factors, thereby determining the treatment goals. Provide patients with personalized treatment plans based on lifestyle, rehabilitation and psychotherapy, Traditional Chinese Medicine conditioning and health education, monitor the patient's health indicators and symptom changes, and adjust the personalized treatment plan at preset time intervals to obtain exclusive treatment strategies.

[0054] On the other hand, the present invention also provides a TCM intelligent health diagnosis method based on multi-source data fusion, comprising:

[0055] The patient's condition data and historical disease data are collected, and a multi-source holographic database construction method is used to build a traditional Chinese medicine holographic health database.

[0056] Establish a multi-source data classification standard, add category fields to the multi-source data in the TCM holographic health database from multiple dimensions to obtain associated field data. The multiple dimensions include etiology and pathogenesis, dialectical system, constitution and prevention of disease, data attributes and time and space attributes.

[0057] Construct an importance judgment standard, judge the importance of different related field data according to health characteristics, and obtain importance labels. Health characteristics include physiological indicator characteristics, physical function characteristics, psychological and adaptation characteristics, and lifestyle characteristics.

[0058] Classify health status according to different importance labels to obtain current health data and formulate exclusive treatment strategies.

[0059] The intelligent health diagnosis system and method of traditional Chinese medicine based on multi-source data fusion provided by the present invention solves the problem of diverse sources and different formats of traditional Chinese medicine data through a multi-source holographic database construction method, realizes data standardization and integration, and improves data quality and availability. The use of a multi-dimensional classification method overcomes the defects of traditional Chinese medicine data being complex and difficult to classify, classifies data from multiple dimensions and establishes associations, improves the organization and utilization efficiency of data, and the importance discrimination method solves the problem of how to effectively evaluate the importance of different data, providing a basis for the priority processing and analysis of data. The personalized treatment strategy formulation method formulates an exclusive treatment strategy based on the patient's health status and importance label, solving the problem of lack of personalization in traditional Chinese medicine diagnosis and treatment. The application of the holistic approach helps to promote the development of traditional Chinese medicine in a modern and intelligent direction, and enhances the competitiveness and influence of traditional Chinese medicine in the medical field. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 This is one of the flow charts of the TCM intelligent health diagnosis system and method based on multi-source data fusion provided by an embodiment of the present invention;

[0062] Figure 2 This is the second flow chart of the TCM intelligent health diagnosis system and method based on multi-source data fusion provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0064] The following combination Figure 1-Figure 2 The present invention describes the TCM intelligent health diagnosis system and method based on multi-source data fusion.

[0065] like Figure 1 As shown, the embodiment of the present invention provides a TCM intelligent health diagnosis system and method based on multi-source data fusion, and the execution subject can be a TCM intelligent health diagnosis system based on multi-source data fusion, including:

[0066] The database construction module is used to collect the patient's condition data and historical disease data, and use the multi-source holographic database construction method to build a traditional Chinese medicine holographic health database.

[0067] The needs and objectives of building a database are determined based on the intended use, purpose, and user group. Based on clinical cases, literature, and patient feedback, basic patient information, symptom data, and other health data are collected to form status data. During the needs analysis phase, it is necessary to combine different scenarios, such as clinical research and health management, to clarify the granularity and dimensions of data collection. For example, for chronic disease management, the focus should be on collecting long-term physiological indicators, while for sub-health conditioning, the collection of lifestyle data should be strengthened.

[0068] Basic information collection: including the patient's name, gender, age, contact information, social attributes and other basic information.

[0069] Symptom data collection: Detailed records of the patient's symptoms, such as tongue, pulse, complexion, pain location, and pain nature, can be obtained through the use of professional testing equipment and tools, such as tongue monitors and pulse monitors, to improve the accuracy and objectivity of data collection.

[0070] Collection of other health status data: such as the patient's eating habits, sleeping conditions, emotional state, lifestyle, etc. These factors are also important for TCM diagnosis and treatment.

[0071] Determine the patient's historical medical data based on their medical history, family history, and previous treatment plans. Create a historical data timeline, annotating key nodes such as major disease events, surgery times, and medication cycles. This supports time-series comparative analysis with current data, such as tracking the correlation between a patient's pulse changes and disease progression over the past three years.

[0072] Use data conversion tools to convert condition data and historical disease data from different sources into a standard format, and perform terminology standardization and data cleaning to obtain multi-source data.

[0073] Terminology standardization: Establish a unified TCM terminology database and standardize the terms in the data to avoid data confusion caused by terminology differences.

[0074] Data cleaning: Improve data quality by removing duplicate data, filling in missing data, correcting erroneous data, etc.

[0075] The database structure is designed according to the needs, goals and characteristics of traditional Chinese medicine theory, and the database management system is selected according to the type of multi-source data and application requirements. The multi-source data is stored in the database to obtain a traditional Chinese medicine holographic health database.

[0076] The multidimensional classification module is used to establish multi-source data classification standards, add category fields to the multi-source data in the TCM holographic health database from multiple dimensions to obtain associated field data. The multiple dimensions include etiology and pathogenesis, dialectical system, constitution and prevention of disease, data attributes and time and space attributes.

[0077] like Figure 2 As shown, the steps of obtaining the associated field data include:

[0078] Corresponding definition rules are determined from multiple dimensions. Multiple classification tables are created based on different definition rules, and the relationships between different classification tables are analyzed to obtain multi-information related data. The definition rules integrate Traditional Chinese Medicine theory and data characteristics. They can include: in the etiology and pathogenesis dimension, setting the logical rule of "history of exposure to rain + white and greasy tongue coating = six exogenous pathogens - dampness"; in the spatiotemporal attribute dimension, setting the compound rule of "collection season = long summer + region = Guangdong = humid and hot area - susceptibility to dampness."

[0079] Integrate data from different classification tables into a transaction database, where each transaction represents multiple related data records.

[0080] A minimum support threshold is set based on the frequent pattern growth algorithm, and transactions that meet this minimum support are identified from the transaction database as frequent itemsets. Algorithm parameters are optimized based on the characteristics of Traditional Chinese Medicine (TCM) data. For low-frequency but important causes of "epidemic," the support threshold is lowered to ensure effective identification. A pruning strategy is also used to eliminate meaningless itemsets.

[0081] Association rules are generated based on multiple frequent item sets, and the confidence level of each association rule is calculated. Each confidence level is determined to be greater than a preset confidence threshold. If it is, it is considered a valid association rule; otherwise, it is deleted. A knowledge graph from the field of Traditional Chinese Medicine (TCM) is introduced to perform a secondary verification of the association rules, automatically filtering out rules that violate TCM theory, such as "cold syndrome-red tongue," to ensure that the mining results conform to clinical logic.

[0082] According to the effective association rules, the association between different classification tables is analyzed to obtain multi-information association data. Multi-information association data can be the association between etiology and pathogenesis and dialectical system, the association between constitution and preventive treatment, etc.

[0083] According to different definition rules, each data in the multi-source data is classified from multiple dimensions to obtain the corresponding category fields.

[0084] The steps for classifying and obtaining the corresponding category fields include:

[0085] By tracing the root cause of the disease from the etiology and pathogenesis, clarifying the diagnostic path and syndrome type from the dialectical system, identifying individual physical characteristics from the physical category, standardizing the data source and mode from the data attributes, and incorporating time and geographical factors from the spatiotemporal attributes, the corresponding classification value is determined.

[0086] Each piece of data in the multi-source data is assigned a classification value, and classification rules are defined based on the logical judgment of the data features. The logical judgment of the data features may include: red tongue edge + flank pain → liver depression and qi stagnation syndrome.

[0087] Each data point is classified according to classification rules, and symptom keywords, texture features, and sound spectrum features are extracted. Traditional Chinese medicine terminology is unified and numerical values ​​are standardized. Natural language processing (NLP) technology is introduced to parse the medical consultation text, and symptom vectors are extracted using the BERT model. Computer vision (CV) technology is used to analyze tongue images, automatically measuring texture features such as tongue coating area percentage and crack length. Symptom keywords are extracted, such as "dry mouth" and "night sweats" from "dry mouth in the morning and night sweats."

[0088] Texture features can include tongue coating thickness, crack area, etc. As shown in Table 1: Tongue Image Checklist:

[0089]

[0090] Table 2: Moss image checklist:

[0091]

[0092] Add initial fields corresponding to each dimension to each data record, match classification rules according to data content and features, and thus determine the categories on each dimension. Add the obtained multiple categories to the corresponding initial fields to obtain the corresponding category fields.

[0093] The multi-information associated data is added according to multiple category fields to obtain associated field data. The adding process may include combining "cause category = internal injury seven emotions - anger + syndrome category = liver depression and qi stagnation syndrome + season = spring" into "spring - emotional disorder - liver depression syndrome".

[0094] The importance grading module is used to construct importance judgment criteria, judge the importance of different related field data based on health characteristics, and obtain importance labels. Health characteristics include physiological indicator characteristics, physical function characteristics, psychological and adaptation characteristics, and lifestyle characteristics.

[0095] The steps to establish importance criteria include:

[0096] Collect the health characteristics of the patients and calculate the weight of each feature in the health characteristics.

[0097] The weight calculation process is:

[0098] Calculate the objective weight of each feature, the formula is expressed as:

[0099]

[0100] Where θ i represents the objective weight of the i-th feature, Y i represents the i-th eigenvalue, ∑Y i represents the sum of all data values ​​under the i-th feature, It represents the proportion of the i-th feature to all the data of this feature.

[0101] Calculate the subjective weight of each feature. The process includes:

[0102] Establish the pairwise comparison matrix Q, the formula is expressed as:

[0103] Q=(a ij ) n*n

[0104] Normalize each column of matrix A to obtain matrix B = (b ij ) n*n , the formula is:

[0105]

[0106] Sum the normalized matrix B by row to get the vector C = (c1, c2, ..., c n ) T , the formula is:

[0107]

[0108] Normalize the vector C to get the weight vector W = (w1, w2, ..., w n ) T , the formula is:

[0109]

[0110] Where n represents the number of features, w i represents the subjective weight of the i-th feature, a ij Indicates the importance of feature i relative to feature j, c i represents the i-th element in the vector C obtained by summing the rows of the normalized matrix B, b ij Represents the elements in matrix B obtained after normalizing the columns of matrix A, a kj is an element in the pairwise comparison matrix A, indicating the importance of feature k relative to feature j, c k is the kth element in vector C, which is obtained by summing the rows of the normalized matrix B.

[0111] Calculate the comprehensive weight of each feature, the formula is expressed as:

[0112] Ji =σ*θ i +(1-σ)*w i

[0113] Where, J i It represents the comprehensive weight of the i-th feature, σ is the adjustment coefficient, and its value range is (0, 1).

[0114] Get the feature score of each feature and calculate the importance of each data with the corresponding weight.

[0115] The steps to obtain the feature score for each feature include:

[0116] The specific manifestations of each feature are extracted from the health features, and the initial scores are obtained by labeling them according to the importance and relevance of the health features.

[0117] And build a scoring model based on the decision tree, input specific performance, and output the initial score of the current data.

[0118] Based on the initial score, the specific performance score is obtained, and the physiological indicator characteristic score, physical function characteristic score, psychological and adaptation characteristic score, and lifestyle characteristic score are calculated. The formula is expressed as follows:

[0119] D E =F*CNN(β)

[0120]

[0121] Where D E represents the initial score, D P represents the physiological index characteristic score, M u represents the physical function characteristic score, M m represents the psychological and adaptive characteristics score, M l represents the score of lifestyle characteristics, P represents the actual physiological index value, and P min Indicates the minimum normal value of physiological indicators, P max Indicates the maximum normal value of physiological indicators, U represents the actual body function level, U min Indicates the lowest normal level of body function, U max Indicates the highest normal level of physical function, M indicates the actual psychological and adaptation level, M min Indicates the lowest normal level of psychology and adaptation, M max Indicates the highest normal level of psychology and adaptation, L indicates actual living habits, L min Indicates the worst living habits, L max represents the best living habits, F represents the index coefficient, and CNN(β) represents the specific performance score.

[0122] The calculation formula for importance is:

[0123] L=ΣJ i *M i

[0124] In the formula, L is the importance, J i Represents the weight of each feature of health characteristics, M i The feature score representing the health feature, i=1, 2, 3, 4.

[0125] Set the importance judgment threshold range corresponding to each importance level. The importance levels are divided into level one, level two, and level three.

[0126] According to the importance level of each data, the importance of each data is judged, and the judgment result is used as the importance label of the data.

[0127] The exclusive customized module classifies health status according to different importance tags to obtain current health data and formulate exclusive treatment strategies.

[0128] Steps in developing a personalized treatment strategy include:

[0129] A health status classification standard is formulated based on importance labels and traditional Chinese medicine theory, and all data with importance labels are integrated to obtain a health dataset.

[0130] The patient's health status is classified according to the health status classification standard and combined with the importance label to obtain the current health status.

[0131] Analyze the current health status to identify the main health problems and risk factors, thereby determining the treatment goals. Provide patients with personalized treatment plans based on lifestyle, rehabilitation and psychotherapy, Traditional Chinese Medicine conditioning and health education, monitor the patient's health indicators and symptom changes, and adjust the personalized treatment plan at preset time intervals to obtain exclusive treatment strategies.

[0132] If the current health status is analyzed and it is found that the physical health status is Yang deficiency, then refer to the exclusive recovery table for adjustment.

[0133] Table 3: Dedicated recovery table:

[0134]

[0135]

[0136]

[0137] Lifestyle can include:

[0138] Dietary adjustment: Develop a personalized diet plan based on the patient's physiological indicators and lifestyle habits, such as a low-salt, low-fat diet and increased dietary fiber intake.

[0139] Exercise guidance: Develop appropriate exercise plans based on your physical condition, such as aerobic exercise, strength training, and flexibility exercises.

[0140] Quit smoking and limit alcohol consumption: Provide guidance and support on quitting smoking and limiting alcohol consumption to help patients give up bad habits.

[0141] Rehabilitation and psychotherapy may include:

[0142] Rehabilitation training: Develop rehabilitation training plans for physical dysfunction, such as physical therapy and occupational therapy.

[0143] Psychotherapy: Based on the results of psychological assessment, provide psychotherapy or psychological counseling, such as cognitive behavioral therapy and relaxation training.

[0144] Traditional Chinese Medicine treatments can include:

[0145] According to the results of TCM syndrome differentiation, TCM treatment methods such as Chinese herbal decoctions, acupuncture, and massage are used for conditioning.

[0146] Health education can include:

[0147] Provide health education to patients, explain disease management knowledge, lifestyle improvement methods, etc., to improve patients' self-management ability.

[0148] Based on the same general inventive concept, the present invention also protects a TCM intelligent health diagnosis method based on multi-source data fusion, comprising:

[0149] The patient's condition data and historical disease data are collected, and a multi-source holographic database construction method is used to build a traditional Chinese medicine holographic health database.

[0150] Establish a multi-source data classification standard, add category fields to the multi-source data in the TCM holographic health database from multiple dimensions to obtain associated field data. The multiple dimensions include etiology and pathogenesis, dialectical system, constitution and prevention of disease, data attributes and time and space attributes.

[0151] Construct an importance judgment standard, judge the importance of different related field data according to health characteristics, and obtain importance labels. Health characteristics include physiological indicator characteristics, physical function characteristics, psychological and adaptation characteristics, and lifestyle characteristics.

[0152] Classify health status according to different importance labels to obtain current health data and formulate exclusive treatment strategies.

[0153] The intelligent TCM health diagnosis system and method based on multi-source data fusion provided in this embodiment constructs a TCM holographic health database and classifies the multi-source data in the database from multiple dimensions such as etiology and pathogenesis, dialectical system, constitution and prevention of disease, data attributes and spatiotemporal attributes, providing a more comprehensive and accurate information basis for TCM diagnosis, which helps to improve the accuracy and reliability of diagnosis. In addition, an importance judgment standard is constructed to calculate the weight and feature score of each feature in the health feature, thereby determining the importance of different associated field data and obtaining importance labels. This solves the problem of how to effectively evaluate the importance of different data, provides a basis for data priority processing and analysis, enables TCM data to be more effectively utilized, and provides strong support for TCM research, education and clinical practice. At the same time, the formulation of personalized treatment strategies can better meet the individual needs of patients, improve treatment effects and patient satisfaction. It helps to promote the development of TCM in a modern and intelligent direction and enhance the competitiveness and influence of TCM in the medical field.

[0154] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0155] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A TCM intelligent health diagnosis system based on multi-source data fusion, characterized by: include: The database construction module is used to collect the patient's condition data and historical disease data, and use the multi-source holographic database construction method to build a traditional Chinese medicine holographic health database; A multi-dimensional classification module is used to establish a multi-source data classification standard, and to add category fields to the multi-source data in the TCM holographic health database from multiple dimensions to obtain associated field data. The multiple dimensions include etiology and pathogenesis, dialectical system, constitution and preventive treatment, data attributes, and spatiotemporal attributes; The importance grading module is used to build importance judgment criteria, determine the importance of different related field data based on health characteristics, and obtain importance labels. Health characteristics include physiological indicator characteristics, physical function characteristics, psychological and adaptation characteristics, and lifestyle characteristics; The exclusive customized module classifies health status according to different importance tags to obtain current health data and formulate exclusive treatment strategies.

2. The TCM intelligent health diagnosis system based on multi-source data fusion according to claim 1 is characterized in that: The steps of constructing the TCM holographic health database include: Determine the needs and goals of building a database based on usage objectives, purposes, and user groups, and collect basic information, symptom data, and other health data of patients based on clinical cases, literature, and patient feedback to form the aforementioned condition data; Determining the patient's historical disease data based on the patient's past medical history, family medical history, and previous treatment plans; Using a data conversion tool to convert the condition data and the historical disease data from different sources into a standard format, and performing terminology standardization and data cleaning to obtain the multi-source data; The database structure is designed according to the requirements, goals and characteristics of traditional Chinese medicine theory, and a database management system is selected according to the type and application requirements of the multi-source data. The multi-source data is stored in the database to obtain the traditional Chinese medicine holographic health database.

3. The intelligent health diagnosis system of traditional Chinese medicine based on multi-source data fusion according to claim 1 is characterized in that: The steps of obtaining the associated field data include: Determine the corresponding definition rules from multiple dimensions, create multiple classification tables based on different definition rules, and analyze the association relationships between different classification tables to obtain multi-information association data; Classify each data in the multi-source data from multiple dimensions according to different definition rules to obtain corresponding category fields; The associated field data is obtained by adding the multi-information associated data according to a plurality of category fields.

4. The intelligent TCM health diagnosis system based on multi-source data fusion according to claim 3 is characterized in that: The steps of obtaining the multi-information association data include: Integrate data from different classification tables into a transaction database, where each transaction represents multiple related data records; Setting a minimum support threshold according to a frequent pattern growth algorithm, and finding transactions that meet the minimum support from the transaction database as frequent itemsets; Generate association rules based on multiple frequent item sets, calculate the confidence of each association rule, and determine whether each confidence is greater than the preset confidence threshold. If so, it is considered a valid association rule, otherwise it is deleted; According to the effective association rules, the association relationships between different classification tables are analyzed to obtain the multi-information association data.

5. The intelligent health diagnosis system of traditional Chinese medicine based on multi-source data fusion according to claim 3 is characterized in that: The steps for classifying and obtaining the corresponding category fields include: According to tracing the root cause of the disease from the etiology and pathogenesis, clarifying the diagnostic path and syndrome type from the dialectical system, identifying individual physical characteristics from the physical constitution and preventive treatment, standardizing the data source and mode from the data attributes, and incorporating time and geographical factors from the spatiotemporal attributes, the corresponding classification value is determined; Assigning each data in the multi-source data according to the classification value, and defining classification rules based on logical judgment of data features; Classify each data according to the classification rules, extract symptom keywords, texture features and sound spectrum features, and unify the TCM terms and standardize the numerical values; Add initial fields corresponding to each dimension to each data record, match the classification rules according to the data content and characteristics, thereby determining the categories on each dimension, and add the obtained multiple categories to the corresponding initial fields to obtain corresponding category fields.

6. The intelligent TCM health diagnosis system based on multi-source data fusion according to claim 1 is characterized in that: The steps of establishing the importance judgment criteria include: Collecting the health characteristics of the patient and calculating the weight of each of the health characteristics; Get the feature score of each feature and calculate the importance of each data with the corresponding weight; Setting the importance judgment threshold range corresponding to each importance level, wherein the importance levels are divided into first-level importance, second-level importance, and third-level importance; The importance of each data is determined according to the importance level of each data, and the determination result is used as the importance label of the data.

7. The intelligent TCM health diagnosis system based on multi-source data fusion according to claim 6 is characterized in that: The weight calculation process is: Calculate the objective weight of each feature; Calculate the subjective weight of each feature. The process includes: Establish a pairwise comparison matrix Q, normalize each column of the matrix Q to obtain the matrix B, sum the normalized matrix B by row to obtain the vector C and normalize it to obtain the weight vector; Calculate the comprehensive weight of each feature, the formula is expressed as: J i =σ*θ i +(1-σ)*w i Where, J i represents the comprehensive weight of the i-th feature, σ is the adjustment coefficient, the value range is (0, 1), w i represents the subjective weight of the i-th feature.

8. The intelligent TCM health diagnosis system based on multi-source data fusion according to claim 6 is characterized in that: The steps to obtain the feature score for each feature include: Extracting specific manifestations of each feature from the health features, and labeling the health features according to their importance and relevance to obtain an initial score; And build a scoring model based on a decision tree, input the specific performance, and output an initial score for the current data; A specific performance score is obtained based on the initial score, and a physiological indicator characteristic score, a physical function characteristic score, a psychological and adaptation characteristic score, and a lifestyle characteristic score are calculated.

9. The intelligent TCM health diagnosis system based on multi-source data fusion according to claim 1 is characterized in that: Steps in developing this customized treatment strategy include: Formulate a health status classification standard based on the importance label and traditional Chinese medicine theory, and integrate all data with the importance label to obtain a health data set; Classifying the patient's health status according to the health status classification standard and in combination with the importance label to obtain the current health status; The current health status is analyzed to obtain the main health problems and risk factors, so as to determine the treatment goals, provide the patient with a personalized treatment plan based on lifestyle, rehabilitation and psychotherapy, traditional Chinese medicine conditioning and health education, monitor the patient's health indicators and symptom changes, and adjust the personalized treatment plan at preset time intervals to obtain the exclusive treatment strategy.

10. A TCM intelligent health diagnosis method based on multi-source data fusion, using a TCM intelligent health diagnosis system based on multi-source data fusion as claimed in any one of claims 1 to 9, characterized in that: The diagnostic method comprises: Collect the patient's condition data and historical disease data, and use the multi-source holographic database construction method to build a traditional Chinese medicine holographic health database; Establishing a multi-source data classification standard, adding category fields to the multi-source data in the TCM holographic health database from multiple dimensions to obtain associated field data, the multiple dimensions including etiology and pathogenesis, dialectical system, constitution and preventive treatment, data attributes, and spatiotemporal attributes; Construct an importance judgment standard to determine the importance of different related field data based on health characteristics and obtain importance labels. Health characteristics include physiological indicator characteristics, physical function characteristics, psychological and adaptation characteristics, and lifestyle characteristics. Classify health status according to different importance labels to obtain current health data and formulate exclusive treatment strategies.

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