Big data-based traditional Chinese medicine clinical curative effect informatization evaluation system and method

Abstract

The invention discloses a big data-based traditional Chinese medicine clinical curative effect informatization evaluation system and method, and belongs to the technical field of traditional Chinese medicine clinical curative effect informatization evaluation systems. The system comprises a patient information management module, a data mining module and an intelligent recommendation module; the signal end of the patient information management module, the signal end of the symptom and sign management module and the signal end of the treatment scheme management module are connected with the signal end of the treatment effect evaluation module. According to the invention, a data model is constructed by adopting a data warehouse technology through a data integration module, and cleaned and standardized data are integrated to form a unified data view; according to the method, the analysis difficulty is reduced by integrating the clinical curative effect information of the traditional Chinese medicine, after the treatment effect is evaluated, data are mined and analyzed, personalized treatment suggestions can be provided for patients, the treatment effect and the satisfaction degree of the patients can be improved, and modernization and personalized development of the traditional Chinese medicine can be promoted.

Description

Technical Field

[0001] The present invention relates to an evaluation system and method, specifically an information-based evaluation system and method for the clinical efficacy of traditional Chinese medicine based on big data, and belongs to the technical field of information-based evaluation systems for the clinical efficacy of traditional Chinese medicine. Background Art

[0002] With the rapid development of modern information technology, the field of traditional Chinese medicine has gradually begun to explore the possibility of information-based evaluation. The emergence of the information-based evaluation system for the clinical efficacy of traditional Chinese medicine is an inevitable trend in the modernization and internationalization of traditional Chinese medicine. It can not only improve the accuracy and efficiency of the evaluation of the clinical efficacy of traditional Chinese medicine, but also promote the spread and application of traditional Chinese medicine globally. The information-based evaluation system for the clinical efficacy of traditional Chinese medicine is a comprehensive evaluation platform integrating modern information technology and traditional Chinese medicine theory.

[0003] The information-based evaluation system for the clinical efficacy of traditional Chinese medicine can, based on big data and artificial intelligence technologies, discover the laws of traditional Chinese medicine diagnosis and treatment, and assist doctors in making more accurate clinical decisions. Through the analysis and mining of a large amount of clinical data, the system can discover the efficacy characteristics and advantages of traditional Chinese medicine in treating a certain disease, and provide personalized treatment plan suggestions for doctors. In addition, the system can also monitor the changes in the patient's condition in real time, adjust the treatment plan in a timely manner, and improve the treatment effect.

[0004] The existing information-based evaluation systems for the clinical efficacy of traditional Chinese medicine mainly rely on statistical methods in data analysis. Although this method can, to a certain extent, obtain the efficacy evaluation of traditional Chinese medicine treatment, the data analysis function within the system is poor, which further limits the wide application and in-depth development of the system in the field of traditional Chinese medicine. This is because the clinical data of traditional Chinese medicine is diverse and complex, including multiple aspects such as patient basic information, symptoms and signs, treatment plans, and treatment effects. The analysis is difficult after data integration. Therefore, an information-based evaluation system and method for the clinical efficacy of traditional Chinese medicine based on big data are proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide an information-based evaluation system and method for the clinical efficacy of traditional Chinese medicine based on big data to solve one of the problems raised in the above background art.

[0006] The present invention is implemented by the following technical solutions: An information-based evaluation system for the clinical efficacy of traditional Chinese medicine based on big data includes a patient information management module, a symptoms and signs management module, a treatment plan management module, a treatment effect evaluation module, a data mining module, and an intelligent recommendation module; The signal terminals of the patient information management module, the symptom and sign management module, and the treatment plan management module are all connected to the signal terminal of the treatment effect evaluation module. The signal terminal of the treatment effect evaluation module is connected to the signal terminal of the data mining module, and the signal terminal of the data mining module is connected to the signal terminal of the intelligent recommendation module; The patient information management module is used to manage the basic information of patients, including name, age, gender, and medical history; The symptom and sign management module is used to record and manage the symptom and sign information of patients, including the diagnosis results of inspection, auscultation, inquiry, and palpation; The treatment plan management module is used to record and manage the treatment plans formulated by doctors for patients, including traditional Chinese medicine prescriptions, acupuncture, and massage methods; The treatment effect evaluation module is used to evaluate the treatment effect of patients, including efficacy evaluation and adverse reaction monitoring, and provide feedback on the treatment effect to doctors; The data mining module is used to deeply mine the integrated data using data mining algorithms to discover potential disease laws and treatment effects; The intelligent recommendation module is used to convert the results of data mining into actual treatment plan recommendations and provide personalized treatment suggestions for patients.

[0007] As a further preference of this technical solution: The data mining algorithm includes the following steps: Data integration: Integrate the medical records, diagnoses, treatments, and medication data of patients into a database; Data selection: In the evaluation of the clinical efficacy of traditional Chinese medicine, extract the feature data related to efficacy evaluation from the database. The feature data includes drug ingredients, dosage, and treatment cycle; Data transformation: Convert the feature data into numerical data and perform standardization processing on the data, that is, convert it into a unified format to eliminate the differences between data; Data mining: Conduct comprehensive classification processing on actual cases, use support vector technology to distinguish different syndromes or disease types, and use neural network algorithms to predict the development trend of diseases or evaluate treatment effects; Model construction: Use the extracted feature data to construct a traditional Chinese medicine efficacy evaluation model, evaluate the model using an independent test set, and optimize the model according to the evaluation results, including adjusting model parameters, adding features, and improving algorithms; Result interpretation: Analyze the results of data mining to reveal the internal laws and mechanisms of traditional Chinese medicine treatment, including the analysis of drug compatibility laws, syndrome diagnosis accuracy, and treatment effects.

[0008] As a further preference of this technical solution: The intelligent recommendation module uses an intelligent recommendation algorithm to recommend treatment plans; The intelligent recommendation algorithm includes the following steps: Data collection and integration: Collect the patient's personal information, including age, gender, living habits, and past medical history; integrate the patient's diagnosis and treatment data, including medical history records, examination and test results, and imaging data; integrate the data mining results, including potential disease patterns and treatment effect evaluations; Data analysis: Conduct in-depth analysis of the patient's personal information and diagnosis and treatment data, compare and analyze the effects of different treatment plans, and determine the treatment path, where the treatment path is the treatment plan with the best treatment effect; Knowledge base matching: Match the data mining results with the traditional Chinese medicine theory knowledge base, search for cases and successful experiences similar to the patient's situation, and combine traditional Chinese medicine theory and clinical practice to differentiate the patient's condition; Generate personalized treatment plan: Generate a personalized treatment plan based on the data mining and analysis results, combined with the patient's personal situation and traditional Chinese medicine theory knowledge. The plan includes treatment goals, methods, medication dosages, and treatment cycles; Plan recommendation: Recommend the generated personalized treatment plan to the patient and the doctor for their reference and decision-making.

[0009] As a further preference of this technical solution: The signal end of the intelligent recommendation module is connected to a data visualization analysis module, and the data visualization analysis module is used to display the complex data analysis results to the user in an intuitive manner through data analysis visualization techniques, including line charts, bar charts, and pie charts.

[0010] As a further preference of this technical solution: The signal end of the intelligent recommendation module is connected to a knowledge base, and the knowledge base is used to store patient cases, successful experiences of patient treatment, and patient treatment plans.

[0011] As a further preference of this technical solution: It further includes a data acquisition module, a data processing module, and a data integration module. The signal end of the data processing module is respectively connected to the signal ends of the data acquisition module and the data integration module; The data acquisition module is used to collect traditional Chinese medicine clinical data from data sources, and the data sources include hospital information systems, electronic medical record systems, and clinical research databases.

[0012] As a further optimization of this technical solution: The data processing module is used to convert data from different sources and in different formats into a unified format, eliminate the differences between the data, and then clean the data, including removing duplicate data, handling missing values, and correcting incorrect data. The signal terminals of the data integration module are respectively connected to the signal terminals of the patient information management module, the symptom and sign management module, and the treatment plan management module. The data integration module is used to construct a data model using data warehouse technology, integrate the cleaned and standardized data, form a unified data line graph, and realize the integration and storage of the data. The signal terminal of the intelligent recommendation module is connected to a knowledge base, and the knowledge base is used to store patient cases, successful experiences of patient treatment, and patient treatment plans.

[0013] As a further optimization of this technical solution: When the treatment effect evaluation module encounters ambiguous curative effect evaluation or adverse reaction monitoring results, it adopts a combination of online and offline methods for evaluation, and then inputs the evaluation results into the system to update the data. The evaluation methods include: Repeated evaluation: Repeatedly evaluate patients suspected of having poor curative effects or adverse reactions. Multidisciplinary consultation: Invite experts from relevant disciplines for consultation to jointly discuss the patient's treatment plan and evaluation results. Prolong the observation period: For patients with uncertain curative effect evaluation results, prolong the observation period. Communicate fully with the patient: Fully communicate the evaluation results and treatment plans with the patient and their families, and explain the uncertainty of the evaluation results.

[0014] As a further optimization of this technical solution: It also includes a data security module, and the data security module is used to protect the privacy and data security of patients using access control technology.

[0015] A method for information-based evaluation of the clinical efficacy of traditional Chinese medicine based on big data includes the following steps: Step 1: Collect clinical data of traditional Chinese medicine from data sources, convert data from different sources and in different formats into a unified format, eliminate the differences between the data, and then clean the data. Step 2: Construct a data model using data warehouse technology, integrate the cleaned and standardized data, and form a unified data line graph. Step 3: Evaluate the treatment effect of the patient, and then use data mining algorithms to mine valuable information and patterns from the integrated data, including disease patterns and treatment effects. Step 4: Based on the results of data mining and analysis, the intelligent recommendation module provides personalized treatment plan recommendations for patients. Step 5: Recommend the generated personalized treatment plan to patients and doctors for their reference and decision-making.

[0016] Advantages of the present invention: 1. The present invention collects traditional Chinese medicine clinical data from data sources through a data collection module. The data processing module standardizes data from different sources and in different formats, and then cleans the data. The data integration module constructs a data model using data warehouse technology, integrates the cleaned and standardized data to form a unified data view, evaluates the treatment effect of patients through a treatment effect evaluation module, and then through data mining and analysis, can reveal the internal laws and mechanisms of traditional Chinese medicine treatment. Finally, the intelligent recommendation module can transform the results of data mining into actual treatment plan recommendations, providing personalized treatment suggestions for patients; 2. The present invention integrates traditional Chinese medicine clinical efficacy information, reduces the difficulty of analysis, and after evaluating the treatment effect, mines and analyzes the data, which can provide personalized treatment suggestions for patients, not only helps to improve the treatment effect and patient satisfaction, but also promotes the modernization and personalized development of traditional Chinese medicine. Brief Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic structural diagram of an information-based evaluation system for the clinical efficacy of traditional Chinese medicine based on big data according to the present invention; Figure 2 It is a schematic flow diagram of an information-based evaluation method for the clinical efficacy of traditional Chinese medicine based on big data according to the present invention. Detailed Embodiments

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0020] Embodiment Please refer to Figure 1, the present invention provides a technical solution: an information-based evaluation system for the clinical efficacy of traditional Chinese medicine based on big data, including a patient information management module, a symptom and sign management module, a treatment plan management module, a treatment effect evaluation module, a data mining module, and an intelligent recommendation module; The signal terminals of the patient information management module, the symptom and sign management module, and the treatment plan management module are all connected to the signal terminal of the treatment effect evaluation module. The signal terminal of the treatment effect evaluation module is connected to the signal terminal of the data mining module, and the signal terminal of the data mining module is connected to the signal terminal of the intelligent recommendation module; The patient information management module is used to manage the basic information of patients, including name, age, gender, and medical history, so that doctors can understand the basic conditions and medical history of patients and provide a basis for diagnosis and treatment; the patient information management module introduces a data verification mechanism to verify patient information to ensure the accuracy and integrity of the information. At the same time, a patient information update mechanism is designed to support the real-time update and modification of patient information; The symptom and sign management module is used to record and manage the symptom and sign information of patients, including the diagnosis results of inspection, auscultation and olfaction, interrogation, and palpation, so as to provide doctors with detailed disease information and help accurately judge the condition and formulate treatment plans; Specifically, it includes: constructing a symptom and sign database to store the symptom and sign information of patients, including the diagnosis results of inspection, auscultation and olfaction, interrogation, and palpation; introducing data standardization technology to standardize the symptom and sign information from different sources to ensure the consistency and comparability of the information; designing a symptom and sign update mechanism to support the real-time update and modification of symptom and sign information; The treatment plan management module is used to record and manage the treatment plans formulated by doctors for patients, including traditional Chinese medicine prescriptions, acupuncture, and massage methods, so as to facilitate doctors to track the treatment effects and adjust the treatment plans in a timely manner; The treatment plan management module is used to construct a treatment plan database to store the treatment plans formulated by doctors for patients, including traditional Chinese medicine prescriptions, acupuncture, and massage methods, and introduce data association technology to associate the treatment plans with patient information to ensure the accuracy and integrity of the treatment plans; The treatment effect evaluation module is used to evaluate the treatment effects of patients, including efficacy evaluation and adverse reaction monitoring, and provide feedback on the treatment effects to doctors, which helps to optimize the treatment plans. The treatment effect evaluation module is equipped with a treatment effect evaluation index library to store efficacy evaluation and adverse reaction monitoring indicators. Through data mining technology, the treatment effect data is deeply mined to discover potential efficacy laws and adverse reaction patterns, and a treatment effect evaluation report generation mechanism is established to generate an intuitive evaluation report based on the evaluation results to provide a scientific basis for optimizing the treatment plans; When the treatment effect evaluation module encounters ambiguous results in efficacy evaluation or adverse reaction monitoring, it adopts a combination of online and offline methods for evaluation, and then inputs the evaluation results into the system to update the data; The evaluation methods include: Repeated evaluation: Repeatedly evaluate patients with suspected poor efficacy or adverse reactions; Multidisciplinary consultation: Invite experts from relevant disciplines for consultation to jointly discuss the treatment plan and evaluation results of the patient; Prolong the observation period: For patients with uncertain efficacy evaluation results, prolong the observation period; Communicate fully with the patient: Fully communicate the evaluation results and treatment plan with the patient and their family members, and explain the uncertainty of the evaluation results; The data mining module is used to deeply mine the integrated data using data mining algorithms to discover potential disease patterns and treatment effects; Furthermore, online evaluation can utilize the Internet and intelligent devices to achieve remote and real-time data collection and analysis. This can not only reduce the time and cost for patients to travel to and from the hospital, but also improve the efficiency and accuracy of data collection. Through offline evaluation, it relies on professional medical equipment and direct observation by medical staff. Through offline evaluation, more detailed and accurate condition information can be obtained, providing strong supplement and verification for online evaluation. By combining online and offline evaluation, the advantages of both can be fully utilized to form a complementary effect. This comprehensive evaluation method can not only improve the comprehensiveness and accuracy of evaluation, but also provide more personalized and precise treatment suggestions for patients.

[0021] The results of data mining algorithms can provide important references for clinical decision-making in traditional Chinese medicine. By mining and analyzing clinical data, key information such as which treatment plans are more effective and which drugs are safer can be discovered, thus helping doctors make more informed decisions. In addition, data mining algorithms can also be used to optimize resource allocation, such as reasonably allocating medical resources and improving the efficiency of drug use, thereby improving the overall quality and benefits of traditional Chinese medicine services; Among them, the data mining algorithm includes the following steps: Data integration: Integrate the patient's medical records, diagnoses, treatments, and medication data into the database; Data selection: In the evaluation of the clinical efficacy of traditional Chinese medicine, extract the feature data related to efficacy evaluation from the database. The feature data includes drug ingredients, dosage, and treatment cycle; Data transformation: Convert the feature data into numerical data and perform standardization processing on the data, that is, convert it into a unified format to eliminate the differences between data; Data mining: Comprehensive classification of actual cases, specifically: first collect multi-dimensional data such as the patient's medical history, symptoms, constitution, treatment history, etc., and then use the pre-processed data to recursively select the optimal features to build a decision tree. Each internal node represents a test on a feature, each branch represents a test result, and each leaf node represents a category. For new actual cases, start from the root node through the decision tree and select the corresponding branch according to the feature value until you reach the leaf node to obtain the classification result of the case; use support vector technology to distinguish different syndromes or disease types, specifically: extract features that can reflect the differences between different syndromes or disease types from the patient's medical history, symptoms, constitution and other information, and then use the features The support vector model is trained by extracting features from new cases and inputting them into the trained support vector model. The support vector model will judge the category to which it belongs, i.e., syndrome or disease type, based on the feature value. The neural network algorithm is used to predict the development trend of the disease or evaluate the treatment effect. Specifically, the patient's medical history, symptoms, treatment history, laboratory test results and other multi-dimensional time series data are collected. According to the development trend of the disease and the treatment effect, a recurrent neural network is constructed. The neural network is trained using historical time series data so that it can learn the potential laws and patterns in the data. For new cases or treatment plans, their features are extracted and input into the trained neural network model. The model will make predictions or evaluations based on the learned laws. Model building: Using the extracted feature data, the model is evaluated using an independent test set. The evaluation indicators include: Accuracy: The ratio of correctly predicted samples to the total number of samples, which reflects the overall performance of the model; Recall rate: the ratio of the number of samples correctly predicted as positive samples to the total number of samples that are actually positive. It reflects the model's ability to identify positive samples. F1 value: The harmonic mean of precision and recall, used to comprehensively evaluate the performance of the model. The higher the F1 value, the better the performance of the model. Then, the model is optimized based on the evaluation results, including adjusting model parameters, adding features, and improving algorithms; Result interpretation: Analyze the results of data mining to reveal the inherent laws and mechanisms of TCM treatment, including the analysis of drug compatibility rules, syndrome diagnosis accuracy, and treatment effects; Through data mining and analysis, we can reveal the inherent laws and mechanisms of TCM treatment, and provide strong support for the modernization and international development of TCM. Through data mining algorithms, a large amount of traditional Chinese medicine clinical data can be deeply mined to discover the internal relationships in aspects such as drug compatibility rules, syndrome diagnosis accuracy, and treatment effects. These rules are of great significance for understanding the essence of traditional Chinese medicine treatment, optimizing treatment plans, and improving treatment effects. In practical applications, by mining drug compatibility rules, it can be found which drug combinations can produce better treatment effects, thus providing a scientific basis for clinical medication.

[0022] The intelligent recommendation module is used to convert the results of data mining into actual treatment plan recommendations, providing personalized treatment suggestions for patients. The intelligent recommendation module uses intelligent recommendation algorithms to recommend treatment plans. The intelligent recommendation module can generate personalized treatment plans based on the patient's personal data, historical treatment effects, and potential disease rules obtained from data mining. Such personalized treatment plans can more accurately target the specific situation of the patient, thereby improving the accuracy and effectiveness of treatment; by comprehensively considering the patient's age, gender, living habits, and past medical history, the intelligent recommendation module can provide treatment suggestions that are more in line with the patient's actual needs. Based on the results of data mining and analysis and combined with traditional Chinese medicine theory knowledge, the intelligent recommendation module provides treatment plan suggestions for doctors. This approach helps to enhance the scientificity and objectivity of medical decision-making, reduce the interference of human factors, and doctors can rely more on the data and analysis results provided by the system to make more accurate decisions. Among them, the intelligent recommendation algorithm includes the following steps: Data collection and integration: Collect the patient's personal information, including age, gender, living habits, and past medical history; integrate the patient's diagnosis and treatment data, including medical history records, examination and test results, and imaging materials; integrate the results of data mining, including potential disease rules and treatment effect evaluations. Data analysis: Deeply analyze the patient's personal information and diagnosis and treatment data, compare and analyze the effects of different treatment plans, and determine the treatment path, where the treatment path is the treatment plan with the best treatment effect. Knowledge base matching: Match the results of data mining with the traditional Chinese medicine theory knowledge base, search for cases and successful experiences similar to the patient's situation, and syndrome-differentiate the patient's condition in combination with traditional Chinese medicine theory and clinical practice. Personalized treatment plan generation: Generate a personalized treatment plan according to the results of data mining and analysis, combined with the patient's personal situation and traditional Chinese medicine theory knowledge. The plan includes treatment goals, methods, medication dosages, and treatment cycles. Plan recommendation: Recommend the generated personalized treatment plan to patients and doctors for their reference and decision-making. The intelligent recommendation module can convert the results of data mining into actual treatment plan recommendations, providing personalized treatment suggestions for patients. This not only helps improve the treatment effect and patient satisfaction, but also promotes the modernization and personalized development of traditional Chinese medicine.

[0023] In this embodiment, specifically: the signal end of the intelligent recommendation module is connected to a data visualization analysis module, which is used to display complex data analysis results to users in an intuitive way through data analysis visualization technology, including line charts, bar charts, and pie charts. By intuitively presenting the data analysis results, the user experience can be improved.

[0024] In this embodiment, specifically: the signal end of the intelligent recommendation module is connected to a knowledge base, which is used to store patient cases, successful experience of patient treatment, and patient treatment plans.

[0025] In this embodiment, specifically: it further includes a data acquisition module, a data processing module, and a data integration module. The signal end of the data processing module is respectively connected to the signal ends of the data acquisition module and the data integration module; The data acquisition module is used to collect traditional Chinese medicine clinical data from data sources, and the data sources include hospital information systems, electronic medical record systems, and clinical research databases to ensure the comprehensiveness and accuracy of the data, providing a reliable data basis for subsequent data processing and analysis; The data acquisition module introduces a data verification mechanism to preliminarily verify the collected data to ensure the accuracy and reliability of the data. The specific steps are as follows: Define verification rules: According to the characteristics and requirements of the traditional Chinese medicine clinical informatization system, define data verification rules, which include data type verification, data range verification, and data format verification.

[0026] Write verification algorithms: According to the defined verification rules, write corresponding verification algorithms. The verification algorithms perform simple logical judgments on the data. When dealing with age data, write an algorithm to determine whether it is a positive integer and whether it is within a reasonable age range; Implement data verification: During the data acquisition process, when new data is collected, the data verification mechanism will be triggered. At this time, the system will call the corresponding verification algorithm to perform preliminary inspection and verification on the collected data. If the data does not conform to the verification rules, the system will immediately issue a warning or error prompt to facilitate timely discovery and handling of problems; Record verification results: Establish a log recording mechanism to track and record the process and results of data verification. Whenever the data verification is completed, the system will record the verification results in the log, so that when problems occur, the cause of the problem can be conveniently traced and located; Feedback and correction: If the data verification fails, the system provides feedback to the user or administrator so that the user or administrator can understand the specific situation of the problem and make corrections.

[0027] In this embodiment, specifically: The data processing module is used to convert data from different sources and in different formats into a unified format, eliminate the differences between the data, and then clean the data, including removing duplicate data, handling missing values, and correcting incorrect data, so as to improve the data quality and reduce the errors in the data analysis process. The data processing module adopts a data deduplication algorithm to identify and remove duplicate data, avoiding data redundancy. The data processing module adopts a missing value handling strategy, including filling missing values with the mean, median, or mode, or inferring missing values based on context information. The data processing module identifies and corrects incorrect data through techniques such as rule matching and machine learning to ensure the accuracy of the data.

[0028] In this embodiment, specifically: The signal terminals of the data integration module are respectively connected to the signal terminals of the patient information management module, the symptom and sign management module, and the treatment plan management module. The data integration module is used to construct a data model using data warehouse technology, integrate the cleaned and standardized data, form a unified data line graph, realize the integration and storage of the data, and then realize the centralized management and efficient access of the data. The data integration module adopts a data association algorithm, including association based on primary keys and foreign keys, or association based on similarity, to associate and integrate data from different sources.

[0029] In this embodiment, specifically: It further includes a data security module, which is used to protect the privacy and data security of patients using access control technology.

[0030] Please refer to Figure 2 , a method for information-based evaluation of the clinical efficacy of traditional Chinese medicine based on big data, including the following steps: Step 1: Collect clinical data of traditional Chinese medicine from data sources, convert data from different sources and in different formats into a unified format, eliminate the differences between the data, and then clean the data. Step 2: Construct a data model using data warehouse technology, integrate the cleaned and standardized data, and form a unified data line graph. Step 3: Evaluate the treatment effect of the patient, and then use a data mining algorithm to mine valuable information and rules from the integrated data, including disease patterns and treatment effects. Step 4: Based on the results of data mining and analysis, the intelligent recommendation module provides personalized treatment plan recommendations for patients. Step 5: Recommend the generated personalized treatment plan to patients and doctors for their reference and decision-making.

[0031] Working principle or structural principle: During operation, the traditional Chinese medicine clinical data is collected from the data source through the data collection module. The data processing module standardizes the data from different sources and in different formats, and then cleans the data. The data integration module constructs a data model using data warehouse technology, integrates the cleaned and standardized data to form a unified data view. The treatment effect evaluation module evaluates the treatment effect of the patient, and then uses data mining algorithms to mine valuable information and rules from the integrated data. Based on the results of data mining and analysis, the intelligent recommendation module provides personalized treatment plan recommendations for patients, and recommends the generated personalized treatment plan to patients and doctors for their reference and decision-making.

[0032] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A big data-based information-based evaluation system for clinical efficacy of traditional Chinese medicine, characterized in that: It includes patient information management module, symptom and sign management module, treatment plan management module, treatment effect evaluation module, data mining module and intelligent recommendation module; The signal end of the patient information management module, the signal end of the symptom and sign management module, and the signal end of the treatment plan management module are all connected to the signal end of the treatment effect evaluation module, the signal end of the treatment effect evaluation module is connected to the signal end of the data mining module, and the signal end of the data mining module is connected to the signal end of the intelligent recommendation module; The patient information management module is used to manage the patient's basic information, including name, age, gender, and medical history; The symptom and sign management module is used to record and manage the patient's symptom and sign information, including the diagnosis results of observation, auscultation, questioning and palpation; The treatment plan management module is used to record and manage the treatment plan formulated by the doctor for the patient, including Chinese medicine prescriptions, acupuncture, and massage methods; The treatment effect evaluation module is used to evaluate the treatment effect of the patient, including efficacy evaluation and adverse reaction monitoring, and provide feedback on the treatment effect to the doctor; The data mining module is used to use data mining algorithms to conduct in-depth mining on the integrated data to discover potential disease patterns and treatment effects; The intelligent recommendation module is used to convert the results of data mining into actual treatment plan recommendations and provide personalized treatment suggestions for patients.

2. According to the big data-based information-based evaluation system for clinical efficacy of traditional Chinese medicine in claim 1, it is characterized in that: The data mining algorithm comprises the following steps: Data integration: Integrate patients’ medical records, diagnosis, treatment, and medication data into the database; Data selection: In the evaluation of clinical efficacy of traditional Chinese medicine, characteristic data related to efficacy evaluation are extracted from the database, including drug ingredients, dosage, and treatment cycle; Data transformation: convert feature data into numerical data and perform data standardization, that is, convert it into a unified format to eliminate differences between data; Data mining: Comprehensively classify actual cases, use support vector technology to distinguish different syndromes or disease types, and use neural network algorithms to predict the development trend of the disease or evaluate the treatment effect; Model construction: Use the extracted feature data to build a TCM efficacy evaluation model, use an independent test set to evaluate the model, and optimize the model based on the evaluation results, including adjusting model parameters, adding features, and improving algorithms; Result interpretation: The results of data mining were analyzed to reveal the inherent laws and mechanisms of TCM treatment, including the analysis of drug compatibility rules, syndrome diagnosis accuracy, and treatment effects.

3. According to the big data-based information-based evaluation system for clinical efficacy of traditional Chinese medicine in claim 1, it is characterized in that: The intelligent recommendation module uses an intelligent recommendation algorithm to recommend treatment plans; The intelligent recommendation algorithm includes the following steps: Data collection and integration: Collect patients’ personal information, including age, gender, lifestyle, and past medical history; integrate patients’ diagnosis and treatment data, including medical history records, examination and test results, and imaging data; integrate data mining results, including potential disease patterns and treatment effect evaluation; Data analysis: Conduct in-depth analysis of the patient's personal information and diagnosis and treatment data, compare and analyze the effects of different treatment plans, and determine the treatment path, which is the treatment plan with the best treatment effect; Knowledge base matching: Match the data mining results with the TCM theory knowledge base to find cases and successful experiences similar to the patient's condition, and combine TCM theory and clinical practice to differentiate the patient's condition; Personalized treatment plan generation: Based on the results of data mining and analysis, combined with the patient's personal situation and theoretical knowledge of traditional Chinese medicine, a personalized treatment plan is generated, which includes the treatment goals, methods, dosage, and treatment cycle; Plan recommendation: Recommend the generated personalized treatment plan to patients and doctors for their reference and decision-making.

4. According to the big data-based information-based evaluation system for clinical efficacy of traditional Chinese medicine in claim 1, it is characterized in that: The signal end of the intelligent recommendation module is connected to a data visualization analysis module, which is used to display complex data analysis results to users in an intuitive manner through data analysis visualization technology, including line graphs, bar graphs, and pie charts.

5. According to the big data-based information-based evaluation system for clinical efficacy of traditional Chinese medicine in claim 4, it is characterized in that: The signal end of the intelligent recommendation module is connected to a knowledge base, which is used to store patient cases, successful experiences in patient treatment and patient treatment plans.

6. The TCM clinical efficacy information evaluation system based on big data according to claim 1 is characterized in that: It also includes a data acquisition module, a data processing module and a data integration module, wherein the signal end of the data processing module is connected to the signal end of the data acquisition module and the signal end of the data integration module respectively; The data acquisition module is used to collect clinical data of traditional Chinese medicine from data sources, and the data sources include hospital information systems, electronic medical record systems, and clinical research databases.

7. The TCM clinical efficacy information evaluation system based on big data according to claim 6 is characterized in that: The data processing module is used to convert data from different sources and in different formats into a unified format, eliminate differences between the data, and then clean the data, including removing duplicate data, processing missing values, and correcting erroneous data. The signal end of the data integration module is respectively connected to the signal end of the patient information management module, the signal end of the symptom and sign management module, and the signal end of the treatment plan management module. The data integration module is used to use data warehouse technology to build a data model, integrate the cleaned and standardized data, form a unified data line chart, and realize data integration and storage.

8. The TCM clinical efficacy information evaluation system based on big data according to claim 1 is characterized in that: When the therapeutic effect evaluation or adverse reaction monitoring results are ambiguous, the treatment effect evaluation module uses a combination of online and offline methods to conduct evaluation, and then inputs the evaluation results into the system to update the data; The assessment methods include: Repeat evaluation: Repeat evaluation of patients suspected of poor efficacy or adverse reactions; Multidisciplinary consultation: Invite experts from related disciplines to conduct consultations to discuss the patient's treatment plan and evaluation results; Extended observation period: For patients with uncertain efficacy evaluation results, the observation period is extended; Communicate fully with patients: Fully communicate the assessment results and treatment plans with patients and their families, and explain the uncertainty of the assessment results.

9. The big data-based information-based evaluation system for clinical efficacy of traditional Chinese medicine according to claim 1, characterized in that: It also includes a data security module, which is used to protect the privacy and data security of patients by using access control technology.

10. A method for evaluating the clinical efficacy of traditional Chinese medicine based on big data, applied to the system for evaluating the clinical efficacy of traditional Chinese medicine based on big data as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect TCM clinical data from data sources, convert data from different sources and formats into a unified format, eliminate differences between data, and then clean the data; Step 2: Use data warehouse technology to build a data model, integrate the cleaned and standardized data, and form a unified data line chart; Step 3: Evaluate the treatment effect of the patient, and then use data mining algorithms to mine valuable information and patterns from the integrated data, including disease patterns and treatment effects; Step 4: Based on the results of data mining and analysis, the intelligent recommendation module provides personalized treatment plan recommendations for patients; Step 5: Recommend the generated personalized treatment plan to patients and doctors for their reference and decision-making.

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