Traditional Chinese medicine physiotherapy management system based on big data and artificial intelligence

Abstract

The invention discloses a traditional Chinese medicine physiotherapy management system based on big data and artificial intelligence, and relates to the technical field of physiotherapy management, and the system comprises a data input module, a data management module, a retrieval module, a database, a dialectical analysis module, and a medication evaluation module. Wherein the data input module is used for collecting patient data; the data management module can perform symptom feature extraction on the information received by the data input module; syndrome differentiation and treatment reference examples are stored in the database, and each syndrome differentiation and treatment reference example corresponds to a group of reference features and at least one group of reference treatment schemes; the retrieval module performs retrieval according to all symptom characteristics of the patient to select a conforming syndrome differentiation and treatment reference example; the dialectical analysis module performs dialectical analysis based on patient data and traditional Chinese medicine knowledge; the technical effects of improving the efficiency of screening information related to traditional Chinese medicine syndrome differentiation treatment by doctors and reducing the screening time and workload are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of physical therapy management, and in particular to a traditional Chinese medicine physical therapy management system based on big data and artificial intelligence. Background Art

[0002] Traditional Chinese medicine has long occupied an important position in the field of medical care with its unique theoretical system and treatment methods. Traditional Chinese medicine is highly dependent on the individual experience of doctors. Through the combination of the four diagnostic methods of "looking, listening, asking and touching", combined with the patient's constitution, cause, nature of the disease, location of the disease and other factors, a detailed dialectical analysis is carried out to formulate a personalized treatment plan. However, with the rapid development of modern medical technology and the sharp increase of medical data, traditional Chinese medicine is facing unprecedented challenges.

[0003] In recent years, although some medical institutions have begun to try to introduce information technology into the field of TCM therapy, such as using electronic medical record systems to record basic patient information, symptom descriptions, and examination reports, most of these systems only stay at the level of simple data entry and storage, lacking in-depth mining and intelligent analysis of data. In practical applications, doctors still need to spend a lot of time and energy to manually filter out information related to TCM syndrome differentiation and treatment from massive amounts of data. This process is not only inefficient, but also easily affected by human factors, making it difficult to ensure the accuracy of syndrome differentiation results. In addition, the field of TCM therapy has accumulated a large number of clinical cases and knowledge of syndrome differentiation and treatment, but existing technologies often fail to effectively integrate these valuable resources and cannot provide doctors with instant and accurate syndrome differentiation analysis and treatment recommendations. Therefore, how to make full use of big data and artificial intelligence technology to achieve deep mining and intelligent analysis of TCM therapy data and improve the accuracy and efficiency of syndrome differentiation and treatment has become a key issue that needs to be urgently addressed in the current field of TCM therapy. Summary of the invention

[0004] The present application provides a TCM physiotherapy management system based on big data and artificial intelligence, thereby solving the technical problems in the prior art that doctors need to spend a lot of time and energy manually screening out information related to TCM syndrome differentiation and treatment from massive data, which is inefficient and easily affected by human factors; and achieves the technical effect of improving the efficiency of doctors in screening information related to TCM syndrome differentiation and treatment, and reducing the time and workload of screening.

[0005] The present application provides a TCM physiotherapy management system based on big data and artificial intelligence, comprising a data input module, a data management module, a retrieval module, a database, a syndrome differentiation analysis module and a medication evaluation module; wherein the data input module is used to collect patient data, and the patient data at least includes the patient's basic information, medical history, symptoms and examination report; the patient data is stored in the database and a unique patient ID is generated; the data management module can extract symptom features from the information received by the data input module; the data management module selects and extracts symptom features related to TCM syndrome differentiation from the patient data, and these symptoms The characteristics include at least symptoms, causes, location, nature and course of the disease; the database stores reference cases for syndrome differentiation and treatment, each of which corresponds to a set of reference characteristics and at least one set of reference treatment plans; the retrieval module retrieves and selects matching reference cases for syndrome differentiation and treatment based on all symptom characteristics of the patient, and sorts the selected reference cases for syndrome differentiation and treatment from high to low according to the degree of relevance; the syndrome differentiation analysis module performs dialectical analysis based on patient data and traditional Chinese medicine knowledge, and the dialectical analysis module is connected to the retrieval module, and generates all possible syndrome differentiation results and diagnoses for reference based on the syndrome differentiation and treatment reference cases retrieved by the retrieval module.

[0006] Furthermore, the patient's basic information includes at least name, gender, and age information.

[0007] Furthermore, the data input module includes a text transmission unit, a voice transmission unit, an image transmission unit and a video transmission unit; the patient data is uploaded in the form of text through the text transmission unit, and the patient's voice, image or video information is transmitted respectively through the voice transmission unit, the image transmission unit or the video transmission unit.

[0008] Furthermore, the reference treatment plan includes prescription drugs and their usage methods; the reference characteristics of the syndrome differentiation and treatment reference case at least include symptoms, causes, location, nature, and course of the disease; and the selected syndrome differentiation and treatment reference case contains all symptom characteristics of the corresponding patient.

[0009] Furthermore, among a group of reference features in the syndrome differentiation and treatment reference case, the reference features that are the same as the patient's symptom features are marked as relevant features, and the remaining reference features are marked as irrelevant features; the fewer irrelevant features in the syndrome differentiation and treatment reference case, the higher the correlation between the syndrome differentiation and treatment reference case and the patient, and the more irrelevant features in the syndrome differentiation and treatment reference case, the lower the correlation between the syndrome differentiation and treatment reference case and the patient.

[0010] Furthermore, the medication evaluation module includes an output unit and a verification unit; the output unit outputs all possible syndrome differentiation results and diagnoses generated by the syndrome differentiation analysis module and displays them to the doctor, and the doctor provides verification feedback through the verification unit.

[0011] Furthermore, the doctor selects the accurate one among all possible differentiation results and diagnoses according to the actual situation of the patient and his own clinical experience, and feeds back the verification result to the verification unit. After obtaining the information, the verification unit stores the patient data and the corresponding case in the database.

[0012] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0013] Patient data is collected through various transmission units in the data input module and stored in the database to generate a unique ID; the data management module extracts symptom characteristics and matches them with reference examples of syndrome differentiation and treatment in the database; the retrieval module selects matching reference examples by sorting by relevance; the syndrome differentiation analysis module uses machine learning and data mining technology to perform dialectical analysis to generate possible syndrome differentiation results and diagnoses; the medication evaluation module outputs the diagnosis results for verification and feedback by doctors to form a closed loop; this effectively solves the technical problem in the prior art that doctors need to spend a lot of time and energy to manually screen out information related to Chinese medicine syndrome differentiation and treatment from massive data, which is inefficient and easily affected by human factors; and thus achieves the technical effect of improving the efficiency of doctors in screening information related to Chinese medicine syndrome differentiation and treatment and reducing the time and workload of screening. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a schematic diagram of a traditional Chinese medicine physiotherapy management system based on big data and artificial intelligence of the present invention;

[0015] Figure 2 This is a schematic diagram of the medication evaluation module of the traditional Chinese medicine physiotherapy management system based on big data and artificial intelligence of the present invention. DETAILED DESCRIPTION

[0016] To facilitate the understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0017] It should be noted that the terms “vertical”, “horizontal”, “up”, “down”, “left”, “right” and similar expressions used in this document are only for illustrative purposes and do not represent the only implementation method.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0019] Example: Figure 1 and Figure 2 As shown, the TCM physiotherapy management system based on big data and artificial intelligence of the present application includes a data input module, a data management module, a retrieval module, a database, a syndrome differentiation analysis module and a medication evaluation module.

[0020] Among them, the data input module is used to collect patient data, and the patient data at least includes the patient's basic information, medical history, symptoms and examination report.

[0021] The data input module includes a text transmission unit, a voice transmission unit, an image transmission unit and a video transmission unit.

[0022] Doctors can upload relevant information to patient data in text form through a text transmission unit, and can also transmit the patient's voice, image or video information through a voice transmission unit, an image transmission unit or a video transmission unit respectively.

[0023] The patient data is stored in the database and a unique patient ID is generated.

[0024] It should be noted that the patient's basic information includes at least name, gender, and age.

[0025] The data management module can extract symptom features from the information received by the data input module.

[0026] It should be noted that the data management module selects and extracts symptom characteristics related to TCM syndrome differentiation from the patient data, and these symptom characteristics at least include symptoms, causes, location, nature and course of disease.

[0027] Specifically, the data management module first receives the patient data from the data input module, which includes basic information, medical records, symptoms, and examination reports; pre-processes the patient data, including data cleaning and format unification, to ensure the accuracy and consistency of the data; selects symptom features directly related to TCM syndrome differentiation from the pre-processed data. These features usually include symptoms (such as cough, fever), causes (such as exogenous wind and cold, internal injury diet), disease location (such as lungs, stomach), disease nature (such as cold, heat), disease state (such as acute, chronic), etc.; uses natural language processing (NLP), image recognition and other technologies to extract specific symptom features from text, image and other data. For example, extract symptoms such as "cough, sputum" from the medical record text, and identify the color and texture of the tongue coating from the tongue image; standardize the extracted symptom features to ensure that data from different sources and different formats can be uniformly represented and compared; store the standardized symptom features in the database and associate them with the patient's unique ID.

[0028] For example, taking patient Zhang San as an example, the specific process of the data management module selecting and extracting symptom characteristics related to TCM syndrome differentiation is as follows:

[0029] Receive Zhang San's basic information (name, gender, age), medical record text (describing symptoms such as cough, sputum, chest tightness, dry mouth, dry stool, etc.) and tongue image; clean the medical record text, remove irrelevant information, and retain key symptom descriptions; pre-process the tongue image, such as denoising, contrast enhancement, etc.; select symptom features such as "cough, sputum, chest tightness, dry mouth, dry stool" from the medical record text; select features such as yellow and greasy tongue coating and red tongue from the tongue image (assuming that they are obtained through image recognition technology); use NLP technology to extract specific symptom descriptions from the medical record text; use image recognition technology to extract features such as the color and texture of the tongue coating from the tongue image; standardize the extracted symptom features, such as standardizing "cough" to "cough" in Chinese medicine terminology, and standardizing the yellow and greasy tongue coating to "yellow, greasy tongue coating"; store the standardized symptom features in the database and associate them with Zhang San's unique ID.

[0030] Furthermore, a large number of syndrome differentiation and treatment reference cases collected and processed in advance are stored in the database, and each syndrome differentiation and treatment reference case corresponds to a group of reference features and at least one group of reference treatment plans.

[0031] It should be noted that the reference treatment plan includes prescription drugs and their usage.

[0032] Specifically, the reference features of the above-mentioned reference examples of syndrome differentiation and treatment at least include symptoms, causes, location of disease, nature of disease, and course of disease.

[0033] It should be noted that the symptoms include at least cold, cough, insomnia, and palpitations; the disease sites include at least the heart, liver, gallbladder, spleen, stomach, intestines, lungs, kidneys, bladder, uterus, Chong and Ren meridians, sperm chamber, and surface; the cause, nature, and course of the disease include at least visceral fullness, imbalance, heat, false heat, blood heat, fluid deficiency, poison, pus, accumulation, cold, heat, qi deficiency, blood deficiency, yin deficiency, yang deficiency, insolubility, irritability, coma, blood stasis, collateral obstruction, bleeding, qi stagnation, cough and asthma, qi reversal, yang hyperactivity, wind, wind movement, dryness, dampness, water, phlegm, food, and worms.

[0034] The search module searches and selects reference cases of syndrome differentiation and treatment that meet the requirements based on all the symptom characteristics of the patient, and sorts the selected reference cases of syndrome differentiation and treatment from high to low according to the degree of relevance.

[0035] It should be noted that the selected reference cases for syndrome differentiation and treatment include all symptom characteristics of the corresponding patients.

[0036] The syndrome differentiation analysis module uses machine learning and data mining technology to perform dialectical analysis based on patient data and traditional Chinese medicine knowledge. The syndrome differentiation analysis module is connected to the retrieval module. According to the syndrome differentiation and treatment reference cases retrieved by the retrieval module, all possible syndrome differentiation results and diagnoses are generated for reference.

[0037] Among them, the reference features in a group of reference features in the reference case of syndrome differentiation and treatment that are the same as the patient's symptom features are marked as relevant features, and the remaining reference features are marked as irrelevant features.

[0038] It should be noted that the fewer irrelevant features in the reference case of syndrome differentiation and treatment, the higher the correlation between the reference case of syndrome differentiation and treatment and the patient, and the more irrelevant features in the reference case of syndrome differentiation and treatment, the lower the correlation between the reference case of syndrome differentiation and treatment and the patient.

[0039] The medication evaluation module includes an output unit and a verification unit.

[0040] The output unit outputs all possible syndrome differentiation results and diagnoses generated by the syndrome differentiation analysis module and displays them to the doctor, who then provides verification feedback through the verification unit.

[0041] Specifically, the doctor selects the accurate one among all possible differentiation results and diagnoses according to the actual situation of the patient and his own clinical experience, and feeds the verification result back to the verification unit. After obtaining the information, the verification unit stores the patient data and the corresponding case in the database.

[0042] For example: Suppose patient Zhang San, male, 30 years old, recently developed symptoms such as cough, sputum, chest tightness, dry mouth, and dry stool.

[0043] First, the doctor diagnoses the patient's symptoms through "observation, smell, questioning and palpation" and other means; the doctor fills in Zhang San's basic information and symptom description through the text transmission unit, and uploads the tongue image through the image transmission unit; the data management module extracts Zhang San's symptom characteristics, including cough, sputum, chest tightness, dry mouth, dry stool, etc.; the retrieval module retrieves the reference examples of syndrome differentiation and treatment that meet the requirements in the database based on Zhang San's symptom characteristics, such as "lung heat cough", "phlegm and dampness blocking the lungs", etc.; the syndrome differentiation analysis module conducts a dialectical analysis based on the retrieved reference examples and Zhang San's specific symptoms. Assuming that the symptom characteristics in the "lung heat cough" reference example are highly consistent with Zhang San's symptom characteristics, the system generates a syndrome differentiation result of "lung heat cough"; the medication evaluation module recommends drugs that clear heat and resolve phlegm, moisten the lungs and relieve cough, such as Fritillaria cirrhosa, Platycodon grandiflorum, licorice, etc., based on the reference treatment plan for "lung heat cough", and outputs the results for verification and feedback from the doctor.

[0044] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0045] 1. Through the integrated data input module (including text, voice, image and video transmission units), the system can comprehensively and quickly collect multi-dimensional data of patients, improving the efficiency and accuracy of data collection;

[0046] 2. The data management module can automatically extract symptom characteristics, reducing the time and workload of manual screening;

[0047] 3. The search module can quickly retrieve relevant reference cases of syndrome differentiation and treatment in a huge database based on the patient's symptom characteristics, and sort them by relevance to provide doctors with valuable reference information;

[0048] 4. The syndrome differentiation analysis module uses machine learning and data mining technology, combined with traditional Chinese medicine knowledge, to automatically generate all possible syndrome differentiation results and diagnoses, thus improving the intelligent level of diagnosis;

[0049] 5. The system can recommend personalized treatment plans for patients based on the results of syndrome differentiation analysis and the reference treatment plans stored in the database, including prescription drugs and their usage methods, thus improving the pertinence and effectiveness of treatment;

[0050] 6. The medication evaluation module can continuously optimize and improve the reference cases and treatment plans in the database through doctors' verification feedback, so as to achieve continuous learning and progress of the system; it will help improve the accuracy and efficiency of the system in handling similar cases in the future;

[0051] 7. Systematic and intelligent TCM physiotherapy management helps reduce human errors and improve medical quality and safety; through automation and intelligent means, the system can significantly shorten the time of diagnosis and treatment and improve the efficiency of medical services;

[0052] 8. The system provides a new way and platform for the inheritance and development of TCM knowledge through the application of big data and artificial intelligence technology;

[0053] 9. By collecting and organizing a large number of TCM cases and treatment plans, the system helps to accumulate and disseminate TCM knowledge and promote the further development of TCM.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A traditional Chinese medicine physiotherapy management system based on big data and artificial intelligence, characterized in that: It includes data input module, data management module, retrieval module, database, syndrome differentiation analysis module and medication evaluation module; The data input module is used to collect patient data, which includes at least the patient's basic information, medical history, symptoms and examination report; The patient data is stored in the database and a unique patient ID is generated; The data management module can extract symptom features from the information received by the data input module; The data management module selects and extracts symptom characteristics related to TCM syndrome differentiation from the patient data, and these symptom characteristics at least include symptoms, causes, disease location, disease nature, and disease progress; The database internally stores reference cases of syndrome differentiation and treatment, each of which corresponds to a set of reference features and at least one set of reference treatment plans; The search module searches and selects reference cases of syndrome differentiation and treatment that meet the requirements according to all symptom characteristics of the patient, and sorts the selected reference cases of syndrome differentiation and treatment from high to low according to the relevance; The syndrome differentiation and analysis module performs a syndrome differentiation and analysis based on patient data and TCM knowledge. The syndrome differentiation and analysis module is connected to the retrieval module and generates all possible syndrome differentiation and diagnosis results according to the syndrome differentiation and treatment reference examples retrieved by the retrieval module.

2. The TCM physiotherapy management system based on big data and artificial intelligence as claimed in claim 1, characterized in that: The patient's basic information includes at least name, gender, and age information.

3. The TCM physiotherapy management system based on big data and artificial intelligence as claimed in claim 1, characterized in that: The data input module includes a text transmission unit, a voice transmission unit, an image transmission unit and a video transmission unit; The patient data is uploaded in text form through the text transmission unit, and the patient's voice, image or video information is transmitted respectively through the voice transmission unit, the image transmission unit or the video transmission unit.

4. The TCM physiotherapy management system based on big data and artificial intelligence as claimed in claim 1, characterized in that: Reference treatment options include prescribed medications and how to use them; The reference characteristics of the reference cases of syndrome differentiation and treatment at least include symptoms, causes, location, nature and course of disease; The selected reference cases for syndrome differentiation and treatment contain all symptom characteristics of the corresponding patients.

5. The TCM physiotherapy management system based on big data and artificial intelligence as claimed in claim 4, characterized in that: The reference features in the syndrome differentiation and treatment reference case that are the same as the patient's symptom features are marked as relevant features, and the remaining reference features are marked as irrelevant features; The fewer irrelevant features in the syndrome differentiation and treatment reference case, the higher the correlation between the syndrome differentiation and treatment reference case and the patient; the more irrelevant features in the syndrome differentiation and treatment reference case, the lower the correlation between the syndrome differentiation and treatment reference case and the patient.

6. The TCM physiotherapy management system based on big data and artificial intelligence as claimed in claim 1, characterized in that: The medication assessment module includes an output unit and a verification unit; The output unit outputs all possible syndrome differentiation results and diagnoses generated by the syndrome differentiation analysis module and displays them to the doctor, who then provides verification feedback through the verification unit.

7. The TCM physiotherapy management system based on big data and artificial intelligence as claimed in claim 6, characterized in that: The doctor selects the accurate one among all possible differentiation results and diagnoses according to the actual situation of the patient and his own clinical experience, and feeds back the verification result to the verification unit. After obtaining the information, the verification unit stores the patient data and the corresponding case in the database.

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