Name traditional Chinese medicine inheritance analysis method and device, electronic equipment and storage medium

Abstract

The invention provides a famous traditional Chinese medicine inheritance analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly obtaining a plurality of student medical records including inquiry data and diagnosis evolution data, classifying all the student medical records into to-be-guided medical records and unqualified medical records based on a static rule base, and outputting an unqualified reason of each unqualified medical record; for each to-be-guided medical record, inputting the inquiry data of the to-be-guided medical record into a pre-trained dynamic simulation model to obtain simulation diagnosis evolution data corresponding to the to-be-guided medical record; and finally, evaluating the diagnosis extraction data of the to-be-guided medical record by using the simulated diagnosis extraction data. According to the invention, the static rule base is established based on the corresponding relationship among the general principle, symptoms and signs in the field of traditional Chinese medicine, and the inherent differentiation and medication thinking of famous traditional Chinese medicine, and the dynamic simulation model is obtained by training a plurality of famous traditional Chinese medicine clinical medical records. The static rule base and the dynamic simulation model are combined to realize evaluation and auditing of student medical records.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method, device, electronic device and storage medium for analyzing the inheritance of famous Chinese medicine practitioners. Background Art

[0002] In the current TCM industry, there are relatively few senior and famous TCM doctors, while young and less experienced TCM doctors account for a large proportion. The growth of young TCM doctors depends largely on the clinical guidance of famous TCM doctors. However, in the existing master-apprentice teaching model, the mentors are usually the clinical backbones or experts of the hospital. In addition to clinical work, they also need to undertake scientific research, teaching and other tasks, resulting in extremely limited guidance time allocated to trainees. This means that trainees often cannot get timely help and guidance from teachers when they encounter problems in clinical practice. Summary of the invention

[0003] The purpose of the present invention is to provide a famous Chinese medicine inheritance analysis method, device, electronic equipment and storage medium to improve the problems existing in the prior art.

[0004] The embodiments of the present invention can be implemented as follows:

[0005] In a first aspect, the present invention provides a method for analyzing the inheritance of famous Chinese medicine practitioners, comprising:

[0006] Obtaining multiple student medical records; the student medical records include consultation data and diagnosis and prescription data;

[0007] Based on the static rule base, all student medical records are classified into medical records to be guided and unqualified medical records, and the reasons for the failure of each unqualified medical record are output; the static rule base is established based on the general principles in the field of traditional Chinese medicine, the correspondence between symptoms and signs, and the inherent differentiation and medication thinking of famous Chinese medicine practitioners;

[0008] For each medical record to be guided, the consultation data of the medical record to be guided is input into a pre-trained dynamic simulation model to obtain simulated diagnosis and prescription data corresponding to the medical record to be guided; the dynamic simulation model is trained based on several clinical medical records of famous Chinese medicine practitioners;

[0009] The simulated diagnosis prescription data is used to evaluate the diagnosis prescription data of the medical record to be guided, so as to obtain an evaluation result of the medical record to be guided.

[0010] Optionally, the medical consultation data includes medical history description, symptom description and tongue and pulse condition records; the diagnosis and prescription data includes diagnosis conclusion and treatment prescription; the static rule base includes description rule set, single syndrome rule set, compound syndrome rule set and prescription rule set;

[0011] The steps of classifying all student medical records into medical records to be guided and unqualified medical records based on the static rule base, and outputting the unqualified reasons for each unqualified medical record, include:

[0012] For each of the student medical records, based on the description rule set, a description verification is performed on the medical history description and the symptom description in the student medical record;

[0013] If the description verification fails, the student's medical record is judged to be an unqualified medical record with unreasonable medical history and symptom descriptions;

[0014] If the description verification passes, then based on the single syndrome type rule set and the compound syndrome type rule set, the syndrome type verification is performed on the symptom description, tongue and pulse condition records in the student's medical record and the syndrome type conclusion in the diagnosis conclusion;

[0015] If the syndrome type verification fails, the student's medical record is judged to be an unqualified medical record due to incorrect syndrome differentiation;

[0016] If the syndrome type verification is passed, the diagnosis conclusion and the treatment prescription are verified based on the prescription rule set;

[0017] If the prescription verification fails, the student's medical record is determined to be an unqualified medical record with incorrect prescriptions;

[0018] If the prescription verification passes, the student's medical record is determined to be a preliminarily qualified medical record awaiting guidance.

[0019] Optionally, the diagnosis prescription data includes a diagnosis conclusion and a treatment prescription; the simulated diagnosis prescription data includes a simulated diagnosis and a simulated prescription;

[0020] The step of evaluating the diagnosis prescription data of the medical record to be guided by using the simulated diagnosis prescription data comprises:

[0021] Based on the simulated diagnosis, evaluating the accuracy of the diagnostic conclusion;

[0022] A similarity evaluation is performed on the simulation prescription and the treatment prescription.

[0023] Optionally, the step of evaluating the similarity between the simulated prescription and the treatment prescription includes:

[0024] Obtaining the number of simulated medications in the simulated prescription;

[0025] Matching each Chinese medicine in the treatment prescription with the Chinese medicine in the simulation prescription respectively to obtain the number of common Chinese medicines between the treatment prescription and the simulation prescription and each common Chinese medicine;

[0026] From all the shared Chinese medicines, find each target shared Chinese medicine whose dosage in the treatment prescription and the simulation prescription is consistent;

[0027] Calculating the ratio of the number of the common Chinese medicines to the number of the simulated medicines to obtain the prescription similarity between the treatment prescription and the simulated prescription;

[0028] The ratio of the number of all target shared Chinese medicines to the number of shared Chinese medicines is calculated to obtain the dosage similarity between the treatment prescription and the simulation prescription.

[0029] Optionally, the evaluation results of the medical records to be guided include prescription similarity and dosage similarity; the method further includes:

[0030] Use the current moment as the review time for each student's medical record;

[0031] A qualified label is set for each medical record to be guided and each unqualified medical record; the qualified label is preliminarily qualified or unqualified;

[0032] Each unqualified medical record and its review time, qualified label and reason for unqualified are saved in the student medical record database, and each medical record to be guided and its review time, qualified label, prescription similarity and dosage similarity are saved in the student medical record database.

[0033] Optionally, the student medical record also includes the student's name; the method further includes:

[0034] receiving a personal growth analysis request, and obtaining an analysis period and a name of a student to be analyzed from the personal growth analysis request;

[0035] Based on the name of the student to be analyzed, the analysis and evaluation results of all target student medical records whose review time is within the analysis period and belongs to the student to be analyzed are searched from the student medical record database; wherein the analysis and evaluation results of the preliminarily qualified target student medical records include the review time, qualified label, prescription similarity and dosage similarity; the analysis and evaluation results of the unqualified target student medical records include the review time and qualified label; the qualified label is qualified or unqualified;

[0036] Generate a qualified line graph based on the review time and qualified labels of all target students' medical records; the qualified line graph reflects the changes in the qualification of the medical records of the students to be analyzed over time;

[0037] Based on the analysis and evaluation results including the review time and prescription similarity corresponding to all target student medical records of prescription similarity, a prescription similarity growth curve is generated;

[0038] Based on the analysis and evaluation results including the review time and dosage similarity corresponding to all target student medical records of dosage similarity, a dosage similarity growth curve is generated.

[0039] Optionally, the student medical record also includes the time spent with the teacher; the method further includes:

[0040] Obtaining a qualified line graph, a similarity growth curve of prescriptions, and a similarity growth curve of dosages of representative students in the previous learning cycle whose apprenticeship time belongs to each learning year;

[0041] The qualified line graphs of the representative students of each study year are integrated to obtain a comparative line graph; the broken lines of different colors in the comparative line graph reflect the qualified line graphs of the representative students of each study year;

[0042] Integrate the square root similarity growth curves of representative students of different study years to obtain a square root comparison graph; curves of different colors in the square root comparison graph reflect the square root similarity growth curves of representative students of different study years;

[0043] The dose similarity growth curves of representative students of different study years are integrated to obtain a dose comparison diagram; curves of different colors in the dose comparison diagram reflect the dose similarity growth curves of representative students of different study years.

[0044] In a second aspect, the present invention provides a famous Chinese medicine inheritance analysis device, comprising:

[0045] An acquisition module, used to acquire multiple student medical records; the student medical records include consultation data and diagnosis and prescription data;

[0046] The static analysis module is used to classify all student medical records into medical records to be guided and unqualified medical records based on a static rule base, and output the unqualified reason for each unqualified medical record; the static rule base is established based on the general principles in the field of traditional Chinese medicine, the correspondence between symptoms and signs, and the inherent differentiation and medication thinking of famous Chinese medicine practitioners;

[0047] A simulation diagnosis module is used for inputting the consultation data of each medical case to be guided into a pre-trained dynamic simulation model to obtain the simulation diagnosis prescription data corresponding to the medical case to be guided; the dynamic simulation model is trained based on a number of clinical medical records of famous Chinese medicine practitioners;

[0048] The comparison and evaluation module is used to evaluate the diagnosis and prescription data of the medical record to be guided by using the simulated diagnosis and prescription data to obtain the evaluation result of the medical record to be guided.

[0049] In a third aspect, the present invention provides an electronic device comprising: a memory and a processor, wherein the memory stores a software program, and when the electronic device is running, the processor executes the software program to implement the famous Chinese medicine inheritance analysis method as described in the first aspect above.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the famous Chinese medicine inheritance analysis method described in the first aspect.

[0051] Compared with the prior art, the embodiment of the present invention provides a famous Chinese medicine inheritance analysis method, device, electronic device and storage medium. First, multiple student medical records including consultation data and diagnosis prescription data are obtained, and all student medical records are classified into medical records to be guided and unqualified medical records based on the static rule base, and the unqualified reasons for each unqualified medical record are output; for each medical record to be guided, the consultation data of the medical record to be guided is input into a pre-trained dynamic simulation model to obtain the simulated diagnosis prescription data corresponding to the medical record to be guided; finally, the simulated diagnosis prescription data is used to evaluate the diagnosis prescription data of the medical record to be guided, and the evaluation result of the medical record to be guided is obtained. In the present invention, the static rule base is established based on the general principles in the field of traditional Chinese medicine, the corresponding relationship between symptoms and signs, and the inherent syndrome differentiation and medication thinking of famous Chinese medicine practitioners, and the dynamic simulation model is obtained by training based on several famous Chinese medicine clinical medical records. The combination of the static rule base and the dynamic simulation model realizes the evaluation and review of student medical records. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 One of the flow charts of a famous Chinese medicine inheritance analysis method provided in an embodiment of the present invention.

[0054] Figure 2 The second flowchart of a famous Chinese medicine inheritance analysis method provided in an embodiment of the present invention.

[0055] Figure 3 A qualified line graph is provided for an embodiment of the present invention.

[0056] Figure 4 A square root similarity growth curve provided by an embodiment of the present invention.

[0057] Figure 5 A dose similarity growth curve provided by an embodiment of the present invention.

[0058] Figure 6 A schematic diagram of the structure of a famous Chinese medicine inheritance analysis device provided in an embodiment of the present invention.

[0059] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0061] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0063] It should be noted that, in the absence of conflict, the features in the embodiments of the present invention may be combined with each other.

[0064] The embodiment of the present invention provides a famous Chinese medicine inheritance analysis method, which can be applied to electronic devices, such as smart phones, smart tablets, personal computers, servers and other devices with computing capabilities. A famous Chinese medicine inheritance analysis system can be deployed in the electronic device, and the famous Chinese medicine inheritance analysis system is used to implement the famous Chinese medicine inheritance analysis method. The method uses a static rule base and a dynamic simulation model in a mixed way. It not only analyzes the eligibility of student medical records based on the essence of traditional Chinese medicine diagnosis, but also uses machine learning to achieve dynamic optimization of diagnosis and prescription logic. Finally, it comprehensively evaluates the syndrome differentiation, medication and dosage of student medical records, and supports personalized growth analysis of individual students, aiming to significantly improve students' learning outcomes and personalized development. The following is a detailed explanation through examples and the accompanying drawings.

[0065] Please refer to Figure 1 , Figure 1A schematic flow chart of a famous Chinese medicine inheritance analysis method provided in an embodiment of the present invention, wherein the famous Chinese medicine inheritance analysis includes the following steps S101 to S104.

[0066] S101. Obtain multiple student medical records.

[0067] In this embodiment, multiple student medical records may belong to the same student (e.g., multiple medical records compiled by a student during a week / two weeks / one month of internship), or may belong to different students (e.g., medical records compiled by multiple students after they consulted a patient), which is not limited in this embodiment of the present invention. Here, students refer to TCM students or young TCM practitioners.

[0068] Optionally, a student medical record may include student information, patient basic information, interview data obtained through the four diagnostic methods of traditional Chinese medicine (observation, auscultation, inquiry, palpation), and diagnosis and prescription data. The interview data includes medical history description, symptom description, and tongue and pulse records, and the diagnosis and prescription data includes diagnosis conclusions and treatment prescriptions. S102. Based on the static rule base, all student medical records are classified into medical records to be guided and unqualified medical records, and the reasons for the unqualified medical records are output.

[0069] In this embodiment, the static rule base is established based on the general principles in the field of traditional Chinese medicine, the corresponding relationship between symptoms and signs, and the inherent dialectical thinking and medication thinking of famous Chinese medicine practitioners. Using the static rule base, the correctness and rationality analysis of each student medical record can be realized at the basic level, so as to distinguish whether a student medical record is an unqualified medical record or a preliminarily qualified medical record to be guided.

[0070] It is understandable that unqualified medical records have errors at the basic level, such as unreasonable description of medical history and symptoms, incorrect diagnosis, incorrect prescription (there are principle errors in medication), etc. Therefore, there is no need to use dynamic simulation models for further analysis and evaluation of unqualified medical records.

[0071] S103. For each medical record to be guided, the consultation data of the medical record to be guided is input into a pre-trained dynamic simulation model to obtain simulated diagnosis and prescription data corresponding to the medical record to be guided.

[0072] In this embodiment, the dynamic simulation model is trained based on a number of clinical medical records of famous Chinese medicine practitioners. Optionally, the simulated diagnosis and prescription data include simulated diagnosis and simulated prescription.

[0073] S104. Evaluate the diagnostic prescription data of the medical record to be guided using the simulated diagnostic prescription data to obtain an evaluation result of the medical record to be guided.

[0074] In this embodiment, the simulated diagnosis and prescription data are used to evaluate the diagnosis and prescription data of the medical case to be guided, and the evaluation result of the medical case to be guided is obtained. The evaluation result reflects the accuracy of the student's syndrome differentiation and the degree of conformity between the student's treatment prescription and the simulated prescription.

[0075] The famous Chinese medicine inheritance analysis method provided by the embodiment of the present invention first obtains multiple student medical records including consultation data and diagnosis and prescription data, classifies all student medical records into medical records to be guided and unqualified medical records based on the static rule base, and outputs the unqualified reasons for each unqualified medical record; for each medical record to be guided, inputs the consultation data of the medical record to be guided into a pre-trained dynamic simulation model to obtain the simulated diagnosis and prescription data corresponding to the medical record to be guided; finally, the simulated diagnosis and prescription data are used to evaluate the diagnosis and prescription data of the medical record to be guided. In the present invention, the static rule base is established based on the general principles in the field of traditional Chinese medicine, the correspondence between symptoms and signs, and the inherent syndrome differentiation and medication thinking of famous Chinese medicine practitioners, and the dynamic simulation model is obtained by training based on several famous Chinese medicine clinical medical records. The combination of the static rule base and the dynamic simulation model realizes the evaluation and review of student medical records.

[0076] The correctness and rationality of student medical records at the basic level mainly focus on the following four aspects:

[0077] 1. Reasonableness of medical history and symptoms: whether the medical history description and symptom description are accurate and consistent with the common description of disease symptoms in traditional Chinese medicine, and whether there are any contradictions;

[0078] 2. The consistency of symptoms, tongue and pulse with syndrome types: In the field of traditional Chinese medicine, different syndrome types have different significant symptoms, tongue and pulse patterns;

[0079] 3. Basic accuracy of disease diagnosis: In the field of traditional Chinese medicine, some diseases have some typical symptom combinations;

[0080] 4. Basic compliance with medication principles: Traditional Chinese medicine has different principles for prescribing and using medication for different syndromes, and it is necessary to verify whether the prescribed medication complies with the medication principles for the diagnostic syndrome.

[0081] Optionally, the static rule base can be established based on the universal principles in the field of traditional Chinese medicine, the correspondence between symptoms and signs, and the inherent syndrome differentiation and medication thinking extracted from several famous Chinese medicine clinical medical records. The static rule base can include a description rule set, a single syndrome rule set, a compound syndrome rule set, and a prescription rule set. The rule set includes several static rules, and the storage method can be a list (List), a dictionary (Dictionary), a tree structure (such as a decision tree), a relational database, etc., which is not limited by the embodiment of the present invention. Taking a student medical record as an example, the analysis process of the student medical record in the above step S102 is introduced below.

[0082] exist Figure 1 Based on Figure 2 In the above step S102, based on the static rule base, the process of classifying all student medical records into medical records to be guided and unqualified medical records, and outputting the unqualified reasons for each unqualified medical record may include the following sub-steps S1021 to S1027.

[0083] S1021. For each student medical record, based on the description rule set, perform description verification on the medical history description and symptom description in the student medical record.

[0084] In this embodiment, the description rule set includes several static rules that can be used to detect whether there are any contradictions between the medical history description and the symptom description in the student's medical record. Optionally, the static rules in the description rule set can be matched one by one using the keywords in the medical history description and the keywords in the symptom description. If there is a matching static rule, it means that the description verification has passed. Otherwise, if no static rule is matched, it means that the description verification has failed, and the medical history description and the symptom description in the medical record are contradictory.

[0085] S1022. If the description verification fails, the student medical record is determined to be an unqualified medical record with unreasonable medical history and symptom descriptions.

[0086] In this embodiment, if the description verification fails, it can be determined that the student medical record is an unqualified medical record, and the reason for the unqualified medical record is that the medical history and symptom descriptions are unreasonable.

[0087] S1023. If the description verification passes, then based on the single syndrome type rule set and the compound syndrome type rule set, the syndrome type verification is performed on the symptom description, tongue and pulse condition records in the student's medical record, and the syndrome type conclusion in the diagnosis conclusion.

[0088] In this embodiment, each static rule in the single syndrome rule set can reflect the correspondence between symptoms, tongue patterns, pulse patterns and a single syndrome, while each static rule in the compound syndrome rule set can reflect the correspondence between symptoms, tongue patterns, pulse patterns and a compound syndrome (compound syndrome).

[0089] Syndrome type is the core of TCM diagnosis, which reflects the pathological state of the disease, including the cause, pathogenesis, location and nature of the disease. The following are some common TCM syndrome types:

[0090] 1. Exterior symptoms: such as wind-cold exterior symptoms and wind-heat exterior symptoms, mainly manifested as fever, aversion to cold, headache, cough, etc.;

[0091] 2. Internal syndrome: such as cold invasion, dampness obstruction, qi stagnation and blood stasis, etc., manifested as abnormal visceral function, such as abdominal pain, diarrhea, vomiting, constipation, etc.;

[0092] 3. Deficiency syndrome: such as qi deficiency, blood deficiency, yin deficiency, yang deficiency, etc., manifested as insufficient vital energy, such as fatigue, palpitations, dizziness, pale complexion, etc.;

[0093] 4. Excessive symptoms: such as internal accumulation of dampness and heat, internal retention of phlegm and fluid, liver depression and qi stagnation, etc., manifested by excessive evil qi, such as bitter taste in the mouth, dry throat, chest tightness, abdominal distension, etc.;

[0094] 5. Cold syndrome: such as invasion of cold pathogens, yang deficiency and cold stagnation, etc., manifested by invasion of cold pathogens or insufficient yang, such as aversion to cold, cold limbs, diarrhea, etc.;

[0095] 6. Heat syndrome: such as excessive heat evil, Yin deficiency and excessive fire, etc., manifested by invasion of heat evil or insufficient Yin fluid, such as fever, thirst, constipation, yellow urine, etc.;

[0096] 7. Dampness syndrome: such as internal accumulation of dampness, dampness and turbidity blocking the middle, etc., manifested as invasion of dampness or obstruction of dampness, such as heaviness of limbs, joint pain, abdominal distension, etc.;

[0097] 8. Phlegm syndrome: such as phlegm-dampness obstruction, phlegm-heat accumulation, etc., manifested by abnormal phlegm production and excretion, such as cough, excessive phlegm, chest tightness, etc.;

[0098] 9. Stasis syndrome: such as blood stasis, qi stagnation and blood stasis, which manifests as poor circulation of qi and blood, such as pain, lumps, purpura, etc.;

[0099] 10. Depression: such as liver depression and qi stagnation, qi stagnation and blood stasis, etc., manifested as depression, chest tightness, and flank pain.

[0100] Among them, wind-cold exterior syndrome, wind-heat exterior syndrome, cold evil invasion, damp evil obstruction, qi stagnation and blood stasis, qi deficiency, blood deficiency, yin deficiency, yang deficiency, etc. are all single syndromes. A compound syndrome refers to the presence of more than one syndrome in a patient at the same time, for example, "qi and blood deficiency", "wind-cold cold and damp-heat diarrhea", "wind-cold dampness and liver depression and qi stagnation" are all compound syndromes. This example is only an example and is not limited here.

[0101] Therefore, the single syndrome type rule set and the compound syndrome type rule set can be used together to verify whether the syndrome type in the diagnosis conclusion in the student's medical record is consistent with the symptoms (such as sweating, pain, etc.), tongue image (such as color, shape, texture) and pulse image (such as speed, strength, rhythm, pulse type) recorded in the student's medical record.

[0102] Optionally, the symptom descriptions, tongue and pulse records, and diagnostic conclusions in the student medical records can be matched one by one with the static rules in the single syndrome rule set and the static rules in the compound syndrome rule set. If there are matching static rules, it means that the syndrome verification has passed. Otherwise, if no static rules are matched, it means that the syndrome verification has failed.

[0103] S1024. If the syndrome verification fails, the student's medical record is judged to be an unqualified medical record due to incorrect syndrome differentiation.

[0104] In this embodiment, if the syndrome type verification fails, it can be determined that the student's medical record belongs to an unqualified medical record, and the reason for the failure is that the syndrome differentiation is incorrect. For example, if the diagnosis conclusion in the student's medical record is "spleen and stomach deficiency and cold syndrome", but the recorded symptoms are described as dry mouth and bitter mouth, sore throat, burning sensation in the upper abdomen, sticky stool, red tongue, red tongue tip, thick and greasy yellow fur, and pulse is pulse rate (referring to the number of pulses is too fast, exceeding 90 times per minute), but from the symptoms, tongue and pulse, this is obviously a manifestation of "excessive syndrome, damp-heat syndrome", which does not belong to "spleen and stomach deficiency and cold syndrome", so the student's diagnosis conclusion is wrong, that is, the syndrome differentiation is wrong. This example is only an example and is not limited here.

[0105] S1025. If the syndrome type verification passes, the diagnosis conclusion and treatment prescription are verified based on the prescription rule set.

[0106] In this embodiment, since the treatment principle needs to be consistent with the basic theory of traditional Chinese medicine, the corresponding relationship between the syndrome type in the prescription rule set and a variety of commonly used Chinese medicines can be matched one by one with the static rules in the prescription rule set based on the symptoms, the syndrome type of the diagnosis conclusion and each Chinese medicine used in the treatment prescription. If there is a matching static rule, it means that the prescription verification has passed, otherwise, if no static rule is matched, it means that the prescription verification has failed.

[0107] S1026. If the prescription verification fails, the student's medical record is determined to be an unqualified medical record with an incorrect prescription.

[0108] In this embodiment, if the prescription verification fails, the student's medical record can be determined to be an unqualified medical record, and the reason for the failure is that the prescription is incorrect. For example, when the syndrome type belongs to a fever, since the treatment of fever should follow the principle of "clearing heat and detoxifying", the drugs in the prescription need to have the effect of clearing heat and detoxifying; when the syndrome type belongs to a cold disease, since the treatment of cold disease should follow the principle of "warming yang and dispersing cold", the drugs in the prescription need to have the effect of expelling cold. For another example, if certain specific symptoms appear, which drugs need to be added or which drugs need to be reduced.

[0109] Optionally, for such unqualified medical records with incorrect prescriptions, although the prescriptions and medications violate some principles of Chinese medicine to a certain extent, the medical questioning and differentiation are basically correct. The medical questioning data of the unqualified medical records can also be input into a pre-trained dynamic simulation model to obtain simulated diagnosis and prescription data, so that students can refer to and study based on the output simulated diagnosis and prescription data.

[0110] S1027. If the prescription verification passes, the student's medical record is determined to be a preliminarily qualified medical record awaiting guidance.

[0111] In this embodiment, if the prescription verification is passed, it means that the medical interview and syndrome differentiation of the student's medical record are basically correct and there are no fundamental errors in the prescription, and the student's medical record can be determined to be a preliminarily qualified medical record to be guided.

[0112] The above steps S1021 to S1027 are performed for each student medical record to complete the preliminary analysis of each student medical record.

[0113] Therefore, by initially screening multiple student medical records through the static rule base, it is possible to quickly determine the preliminarily qualified medical records to be guided and the unqualified medical records that obviously do not conform to the basic theories of traditional Chinese medicine and the experience of famous Chinese medicine practitioners, so that the consultation data of the medical records to be guided can be used as a more accurate and reasonable input for the subsequent dynamic simulation model. This can not only improve the efficiency of system analysis, but also ensure the reliability of the analysis results to a certain extent.

[0114] Optionally, the process of building a dynamic simulation model can be roughly divided into the following 5 steps:

[0115] 1. Data collection: Collect several clinical medical records of famous Chinese medicine practitioners. The contents of a clinical medical record of a famous Chinese medicine practitioner may include medical history description, symptom description, tongue and pulse condition records, diagnosis conclusion, treatment prescription, etc.;

[0116] 2. Data cleaning and preprocessing: Clean the collected data, remove duplicate, erroneous, and incomplete data, and perform standardization, such as unifying symptom descriptions, drug names, etc.;

[0117] 3. Data mining and analysis: Use data mining techniques (such as cluster analysis, association rule mining, decision trees, etc.) to conduct in-depth analysis on the cleaned data, extract the teacher's diagnostic logic, syndrome differentiation and treatment methods, and commonly used prescriptions and medicines, and then mine to form a training set;

[0118] 4. Model training: Based on the training set and machine learning algorithm, a dynamic simulation model that can simulate the teacher’s diagnostic logic is trained.

[0119] Therefore, the dynamic simulation model can simulate famous Chinese medicine practitioners to perform syndrome differentiation and prescription based on the input consultation data.

[0120] In an optional implementation, the famous Chinese medicine inheritance analysis system can compare the simulated diagnosis and prescription data output by the dynamic simulation model with the diagnosis and prescription data in the student's medical record to evaluate the accuracy of the student's syndrome differentiation and the degree of compliance with the treatment plan. Correspondingly, the sub-steps of the above step S104 can include S1041 to S1042.

[0121] S1041. Based on the simulated diagnosis, the accuracy of the diagnosis conclusion is evaluated.

[0122] In this embodiment, the simulated diagnosis is compared with the student's diagnosis conclusion to see whether the two are consistent. If they are consistent, it means that the student's syndrome differentiation is correct. If there is a difference, it means that the student's syndrome differentiation needs to be improved.

[0123] S1042. Evaluate the similarity between the simulation prescription and the treatment prescription.

[0124] In this embodiment, the similarity between the simulated prescription and the treatment prescription can be evaluated based on the two aspects of the Chinese medicine used and the dosage. Therefore, the sub-steps of step S1042 can include S10421 to S10425:

[0125] S10421. Obtain the number of simulated medications in the simulated prescription.

[0126] In this embodiment, the number of simulated medications is the number of Chinese medicinal ingredients used in the simulated prescription.

[0127] S10422. Match each Chinese medicine in the treatment prescription with the Chinese medicine in the simulation prescription respectively to obtain the number of shared Chinese medicines between the treatment prescription and the simulation prescription and each shared Chinese medicine.

[0128] In this embodiment, it is necessary to find out each common Chinese medicine between the student's treatment prescription and the simulation prescription, so as to determine the number of common Chinese medicines.

[0129] S10423. From all the shared Chinese medicines, find out each target shared Chinese medicine whose dosage is consistent in the treatment prescription and the simulation prescription.

[0130] S10424. Calculate the ratio of the number of shared Chinese medicines to the number of simulated medicines to obtain the prescription similarity between the treatment prescription and the simulated prescription.

[0131] S10425. Calculate the ratio of the number of all target shared Chinese medicines to the number of shared Chinese medicines to obtain the dosage similarity between the treatment prescription and the simulation prescription.

[0132] It is understandable that for some typical diseases, there are standard prescriptions, and the dosage of each Chinese medicine in the standard prescription is rated, such as Sijunzi Decoction for treating symptoms such as loss of appetite, indigestion, fatigue and weakness caused by spleen and stomach qi deficiency, and Guizhi Decoction for treating symptoms such as fever, aversion to cold, sweating, headache, body pain and so on caused by exogenous wind and cold. However, when prescribing, most famous Chinese medicine practitioners will increase / decrease the dosage of certain Chinese medicines according to the actual physical condition of the patient, so as to more accurately meet the individualized treatment needs of the patient, better play the role of the drug, and improve the treatment effect. Therefore, the dynamic simulation model also adjusts the drug dosage based on the input consultation data in the process of generating the simulated prescription, so the output simulated prescription may also have dosage differences compared with the standard prescription.

[0133] However, the dosage of the drug prescribed by students in the early stage of learning is consistent with the standard prescription. As the learning progresses, students may try to adjust the dosage in the treatment prescription according to the actual situation of the patient. Therefore, the dosage similarity can measure the students' prescription level.

[0134] In this embodiment, the evaluation result of a medical record to be guided may include the calculated prescription similarity and dosage similarity.

[0135] In the optional example, suppose the student's treatment prescription uses 10 Chinese medicines, and the simulated prescription uses 12 Chinese medicines, of which 8 are shared, then the prescription similarity is: 8 / 12*100%≈66.7%; if the dosage of 7 of the 8 shared Chinese medicines is the same (that is, the target number of shared Chinese medicines is 7), then the dosage similarity is: 7 / 8*100%=87.5%. This example is only an example and is not limited here.

[0136] In an optional implementation, after the analysis and evaluation of multiple student medical records are completed, each student medical record and its evaluation results can also be stored in a student medical record library. Correspondingly, the famous Chinese medicine inheritance analysis method can also include the following steps S105 to S107:

[0137] S105. Use the current time as the review time for each student's medical record.

[0138] S106. Set a qualified label for each medical record to be guided and each unqualified medical record.

[0139] In this embodiment, the qualified label is preliminarily qualified (eg, 1) or unqualified (eg, 0). The qualified label of the medical record to be designated is preliminarily qualified, and the qualified label of the unqualified medical record is unqualified.

[0140] S107. Save each unqualified medical record and its review time, qualified label and reason for unqualified in the student medical record database, and save each medical record to be guided and its review time, qualified label, prescription similarity and dosage similarity in the student medical record database.

[0141] In this embodiment, the student medical record library can be used to store the student medical records submitted for review by each student and their analysis and evaluation results.

[0142] Optionally, the student information in a student medical record may include the student's name and the time of following the teacher. Based on the time of following the teacher, the number of years that the student has studied with the famous Chinese medicine practitioner can be determined. For example, if the time of following the teacher is 1 to 6 months, the number of years of study can be considered to be half a year, if the time of following the teacher is 7 to 12 months, the number of years of study can be considered to be 1 year, if the time of following the teacher is 13 to 18 months, the number of years of study can be considered to be one and a half years, and so on.

[0143] In the optional implementation, it is assumed that the student (young Chinese medicine practitioner) and the instructor (senior famous Chinese medicine practitioner) each have a personal account in the famous Chinese medicine practitioner inheritance analysis system. After each student logs in to his or her personal account on his or her personal terminal, he or she can upload his or her own student medical record. The famous Chinese medicine practitioner inheritance analysis system can immediately analyze and evaluate the student medical record and save it to the student medical record library. The analysis and evaluation process of a student medical record includes: first confirming whether the student medical record is a preliminarily qualified medical record to be guided or an unqualified medical record based on the static rule library. If it is an unqualified medical record, the reason for the unqualified medical record must be output at the same time. If it is a preliminarily qualified medical record to be designated, the consultation data of the medical record to be guided can continue to be input into the dynamic simulation model to obtain the simulated diagnosis and prescription data corresponding to the medical record to be guided; the diagnostic prescription data of the medical record to be guided is evaluated using the simulated diagnosis and prescription data to obtain the evaluation result of the medical record to be guided; finally, the analysis and evaluation result of the student medical record is stored in the student medical record library. Alternatively, the instructor can uniformly control the famous Chinese medicine inheritance analysis system to analyze and evaluate multiple student medical records: after the instructor logs into his or her personal account on his or her personal terminal, he or she can check whether each student he or she has taught has submitted a student medical record. If so, the instructor can control the famous Chinese medicine inheritance analysis system to uniformly analyze and evaluate the student medical records submitted by each student and save them to the student medical record library (the analysis, evaluation and saving process are as described in steps S101 to S107 above).

[0144] The instructor can retrieve and view the medical records of each student that has been reviewed by the system from the student medical record library. The instructor can also edit, modify, and annotate the evaluation results of the retrieved student medical records and save them for students to view. On the one hand, the instructor's modified data on the evaluation results will be collected by the famous Chinese medicine inheritance analysis system and used for subsequent model optimization and upgrades to improve the system's analysis accuracy and practicality; on the other hand, the instructor's modified data on the evaluation results will help correct the inaccuracy of the analysis and evaluation of the medical records by combining the static rule base and the dynamic simulation model. Only after the instructor makes the modification, the students are allowed to view the analysis and evaluation results of the medical records they submitted, which are accurate, and will not lead to incorrect teaching due to the analysis errors of the famous Chinese medicine inheritance analysis system.

[0145] In an optional implementation, based on the data of the student medical record library, it can also support the analysis of the learning growth of a single student. Correspondingly, the famous Chinese medicine inheritance analysis method can also include the following steps S201 to S205.

[0146] S201. Receive a personal growth analysis request, and obtain an analysis period and a name of a student to be analyzed from the personal growth analysis request.

[0147] In this embodiment, when a personal growth analysis request is received, the analysis period and the name of the student to be analyzed can be obtained from the personal growth analysis request. The analysis period can be but not limited to the current school year, the historical school year, one month, one week, half a year, etc. Optionally, the personal growth analysis request can be initiated by a personal terminal of the student to be analyzed, or by a personal terminal of the instructor of the student to be analyzed.

[0148] S202. Find out the analysis and evaluation results of all target student medical records belonging to the student to be analyzed from the student medical record database based on the student name of the student to be analyzed.

[0149] In this embodiment, in the student medical record database, the analysis and evaluation results of each preliminarily qualified student medical record include the review time, qualified label, prescription similarity, dosage similarity and teacher evaluation, while the analysis and evaluation results of each unqualified student medical record include the review time, qualified label and teacher evaluation.

[0150] S203. Generate a qualified line graph based on the review time and qualified labels of all target students' medical records.

[0151] In this embodiment, the qualified line graph reflects the change of the medical record qualification of the student to be analyzed over time.

[0152] In the optional example, suppose there are 18 target student medical records in total, and each is numbered according to the review time. The qualified labels (1 represents preliminary qualified, 0 represents unqualified) are shown in the following table. Then the generated qualified line chart can be as follows: Figure 3 As shown, from Figure 3 It can be seen that as time goes by, the eligibility of the medical records of the students to be analyzed tends to be stable, and the qualification rate of these 18 target students' medical records reaches 77.7%.

[0153] Medical records 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Qualified label 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1

[0154] This example is only an example and is not intended to be limiting.

[0155] S204. Generate a prescription similarity growth curve based on the analysis and evaluation results including the review time and prescription similarity corresponding to all target student medical records of prescription similarity.

[0156] In the optional example, assuming that 14 of the 18 target student medical records are qualified, then the prescription similarity growth curve generated based on these 14 qualified medical records can be as follows: Figure 4 As shown, from Figure 4 It can be seen that as time goes by, the similarity of the square root of the students to be analyzed shows an overall upward trend, with slight fluctuations in the middle. This example is only an example and is not limited here.

[0157] S205. Generate a dosage similarity growth curve based on the analysis and evaluation results including the review time and dosage similarity corresponding to all target student medical records with dosage similarity.

[0158] In the optional example, assuming that 14 of the 18 target student medical records are qualified, the dose similarity growth curve generated based on these 14 qualified medical records can be as follows: Figure 5 As shown, from Figure 5 It can be seen that as time goes by, the dose similarity of the students to be analyzed shows a steady upward trend, that is, the students' prescription level is gradually improving. This example is only an example and is not limited here.

[0159] In an optional implementation, based on the data of the student medical record library, it can also support comparative analysis of the learning growth of students with different study years. Correspondingly, the famous Chinese medicine inheritance analysis method can also include the following steps S301 to S304.

[0160] S301. Obtain a qualified line graph, a square root similarity growth curve, and a dosage similarity growth curve of representative students whose apprenticeship time belongs to each study period in the previous study cycle.

[0161] In this embodiment, the representative student for each study period can be a student randomly selected from all students whose apprenticeship time belongs to that study period, or can be a student with the best / middle / worst academic performance among all students. This embodiment of the present invention is not limited to this.

[0162] Optionally, the previous learning cycle can be: last week, last month, last quarter, first half of the year, last year, etc., which is not limited here. The principle of obtaining each qualified line graph, square root similarity growth curve and dosage similarity growth curve representing the student in the previous learning cycle is consistent with the above steps S202-S205 (the analysis period at this time = the previous learning cycle), which will not be repeated here.

[0163] S302. Integrate the qualified line graphs of representative students in different study years to obtain a comparative line graph.

[0164] In this embodiment, in the integrated comparison line graph, the lines of different colors reflect the qualified line graphs of representative students in different years of study.

[0165] S303, integrating the square root similarity growth curves of representative students of different study years to obtain a square root comparison chart.

[0166] In this embodiment, in the integrated square root comparison diagram, curves of different colors reflect the square root similarity growth curves of representative students of different study years.

[0167] S304. Integrate the dose similarity growth curves of representative students of different study years to obtain a dose comparison chart.

[0168] In this embodiment, in the integrated dose comparison diagram, curves of different colors reflect the dose similarity growth curves of representative students of different study years.

[0169] It can be understood that from the comparison line chart, the square root comparison chart and the dosage comparison chart, the progress of the representative students of different study years can be compared, and it can be clearly seen which representative student of the study year has made the greatest progress. This helps the instructor to grasp the teaching progress of students of different study years.

[0170] It should be noted that the execution order of each step in the above method embodiment is not limited to the steps shown in the drawings and the step numbers, and the execution order of each step shall be based on the actual application situation.

[0171] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0172] The present invention uses a combination of static rule bases and dynamic simulation models to analyze the eligibility of students' medical records based on the essence of traditional Chinese medicine diagnosis, and uses machine learning to achieve dynamic optimization of diagnosis and prescription logic, and ultimately comprehensively evaluates the syndrome differentiation, medication and dosage of students' medical records, making up for the deficiency that instructors cannot provide timely guidance and teaching to students.

[0173] The present invention also supports the instructor to modify and supplement the analysis and evaluation results of the student's medical records;

[0174] The present invention also supports the analysis of the personalized growth of individual students, thereby showing the students' learning progress and ability improvement in the process of traditional Chinese medicine inheritance;

[0175] The present invention also supports comparative growth analysis of students of different study years, thereby providing data support for teaching management and learning planning.

[0176] In order to execute the corresponding steps in the above method embodiment and each possible implementation method, an implementation method of a famous Chinese medicine inheritance analysis device is given below.

[0177] See also Figure 6 , Figure 6 The schematic diagram of the structure of the famous Chinese medicine inheritance analysis device provided by the embodiment of the present invention is shown. The famous Chinese medicine inheritance analysis device 200 comprises: an acquisition module 210, a static analysis module 220, a simulation diagnosis module 230 and a comparison and evaluation module 240.

[0178] The acquisition module 210 is used to acquire multiple student medical records; the student medical records include consultation data and diagnosis and prescription data;

[0179] The static analysis module 220 is used to classify all student medical records into medical records to be guided and unqualified medical records based on a static rule base, and output the unqualified reason for each unqualified medical record; the static rule base is established based on the general principles in the field of traditional Chinese medicine, the corresponding relationship between symptoms and signs, and the inherent syndrome differentiation and medication thinking of famous Chinese medicine practitioners;

[0180] The simulation diagnosis module 230 is used to input the consultation data of each medical record to be guided into a pre-trained dynamic simulation model to obtain the simulation diagnosis prescription data corresponding to the medical record to be guided; the dynamic simulation model is trained based on a number of clinical medical records of famous Chinese medicine practitioners;

[0181] The comparison and evaluation module 240 is used to evaluate the diagnosis and prescription data of the medical record to be guided by using the simulated diagnosis and prescription data to obtain the evaluation result of the medical record to be guided.

[0182] Optionally, the medical interview data includes medical history description, symptom description and tongue and pulse records; the diagnosis and prescription data includes diagnosis conclusion and treatment prescription; the static rule base includes description rule set, single syndrome rule set, compound syndrome rule set and prescription rule set. The static analysis module 220 can be specifically used for: for each student medical record, based on the description rule set, performing description verification on the medical history description and symptom description in the student medical record; if the description verification fails, the student medical record is judged to be an unqualified medical record with unreasonable medical history and symptom descriptions; if the description verification passes, based on the single syndrome type rule set and the compound syndrome type rule set, the symptom description, tongue and pulse condition record and syndrome type conclusion in the diagnosis conclusion in the student medical record are subjected to syndrome type verification; if the syndrome type verification fails, the student medical record is judged to be an unqualified medical record with incorrect syndrome differentiation; if the syndrome type verification passes, the diagnosis conclusion and treatment prescription are subjected to prescription verification based on the prescription rule set; if the prescription verification fails, the student medical record is judged to be an unqualified medical record with incorrect prescription; if the prescription verification passes, the student medical record is judged to be a preliminarily qualified medical record awaiting guidance.

[0183] Optionally, the diagnosis prescription data includes a diagnosis conclusion and a treatment prescription; the simulation diagnosis prescription data includes a simulation diagnosis and a simulation prescription. The comparison evaluation module 240 can be used to: evaluate the accuracy of the diagnosis conclusion based on the simulation diagnosis; and evaluate the similarity between the simulation prescription and the treatment prescription.

[0184] Optionally, when the comparative evaluation module 240 is used to evaluate the similarity between the simulation prescription and the treatment prescription, it can be specifically used to: obtain the number of simulated medications in the simulation prescription; match each Chinese medicine in the treatment prescription with the Chinese medicine in the simulation prescription respectively to obtain the number of shared Chinese medicines between the treatment prescription and the simulation prescription and each shared Chinese medicine; from all shared Chinese medicines, find each target shared Chinese medicine with the same dosage in the treatment prescription and the simulation prescription; calculate the ratio of the number of shared Chinese medicines to the number of simulated medications to obtain the prescription similarity between the treatment prescription and the simulation prescription; calculate the ratio of the number of all target shared Chinese medicines to the number of shared Chinese medicines to obtain the dosage similarity between the treatment prescription and the simulation prescription.

[0185] Optionally, the evaluation results of the medical records to be guided include prescription similarity and dosage similarity. The famous Chinese medicine inheritance analysis device 200 may also include a storage module 250, which is used to: use the current time as the review time of each student medical record; set the qualified label of each medical record to be guided and each unqualified medical record; the qualified label is preliminarily qualified or unqualified; save each unqualified medical record and its review time, qualified label and unqualified reason to the student medical record library, and save each medical record to be guided and its review time, qualified label, prescription similarity and dosage similarity to the student medical record library.

[0186] Optionally, the student medical record also includes the student's name. The Chinese medicine inheritance analysis device 200 may also include a personalized analysis module 260, which is used to: receive a personal growth analysis request, obtain the analysis period and the student name of the student to be analyzed from the personal growth analysis request; find out the analysis and evaluation results of all target student medical records whose review time is within the analysis period and belongs to the student to be analyzed from the student medical record library based on the student name of the student to be analyzed; wherein, the analysis and evaluation results of the preliminarily qualified target student medical records include the review time, qualified label, prescription similarity and dosage similarity; the analysis and evaluation results of the unqualified target student medical records include the review time and qualified label; the qualified label is qualified or unqualified; based on the review time and qualified label of all target student medical records, generate a qualified line graph; the qualified line graph reflects the change of the medical record qualification of the student to be analyzed over time; based on the review time and prescription similarity corresponding to all target student medical records including the prescription similarity in the analysis and evaluation results, generate a prescription similarity growth curve; based on the review time and dosage similarity corresponding to all target student medical records including the dosage similarity in the analysis and evaluation results, generate a dosage similarity growth curve.

[0187] Optionally, the student medical record also includes the time of following a teacher. The personalized analysis module 260 can also be used to: obtain the qualified line graph, the prescription similarity growth curve and the dosage similarity growth curve of the representative students of each learning year in the previous learning cycle whose teaching time belongs to each learning year; integrate the qualified line graphs of the representative students of each learning year to obtain the comparison line graph; the different colored lines in the comparison line graph reflect the qualified line graphs of the representative students of each learning year; integrate the prescription similarity growth curves of the representative students of each learning year to obtain the prescription comparison graph; the different colored curves in the prescription comparison graph reflect the prescription similarity growth curves of the representative students of each learning year; integrate the dosage similarity growth curves of the representative students of each learning year to obtain the dosage comparison graph; the different colored curves in the dosage comparison graph reflect the dosage similarity growth curves of the representative students of each learning year.

[0188] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the famous Chinese medicine inheritance analysis device 200 described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0189] See also Figure 6 , Figure 7 The electronic device 300 includes a processor 310 , a memory 320 , and a bus 330 , wherein the processor 310 is connected to the memory 320 via the bus 330 .

[0190] The memory 320 can be used to store software programs, for example, software programs corresponding to the famous Chinese medicine inheritance analysis device 200 provided in the embodiment of the present invention. The processor 310 executes various functional applications and data processing to implement the famous Chinese medicine inheritance analysis method provided in the embodiment of the present invention by running the software programs stored in the memory 320.

[0191] Among them, the memory 320 can be but is not limited to: RAM (Random Access Memory), ROM (Read Only Memory), FLASH (Flash Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electric Erasable Programmable Read-Only Memory), etc.

[0192] The processor 310 may be an integrated circuit chip with signal processing capability. The processor 310 may be a general-purpose processor, including: CPU (Central Processing Unit), NP (Network Processor), etc.; it may also be: DSP (Digital Signal Processing), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0193] Understandably, Figure 7 The structure shown is for illustration only. The electronic device 300 may also include Figure 7 More or fewer components as shown, or with Figure 7 Different configurations are shown. Figure 7 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0194] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the famous Chinese medicine inheritance analysis method disclosed in the above embodiment is implemented. The computer-readable storage medium can be, but is not limited to, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a PROM, an EPROM, an EEPROM, a FLASH disk, or an optical disk.

[0195] In summary, the embodiments of the present invention provide a method, device, electronic device and storage medium for analyzing the inheritance of famous Chinese medicine practitioners. First, multiple student medical records including consultation data and diagnosis prescription data are obtained, and all student medical records are classified into medical records to be guided and unqualified medical records based on the static rule base, and the unqualified reasons for each unqualified medical record are output; for each medical record to be guided, the consultation data of the medical record to be guided is input into a pre-trained dynamic simulation model to obtain the simulated diagnosis prescription data corresponding to the medical record to be guided; finally, the simulated diagnosis prescription data is used to evaluate the diagnosis prescription data of the medical record to be guided, and the evaluation result of the medical record to be guided is obtained. In the present invention, the static rule base is established based on the general principles in the field of traditional Chinese medicine, the corresponding relationship between symptoms and signs, and the inherent syndrome differentiation and medication thinking of famous Chinese medicine practitioners, and the dynamic simulation model is obtained by training based on several clinical medical records of famous Chinese medicine practitioners. The combination of the static rule base and the dynamic simulation model realizes the evaluation and review of student medical records.

[0196] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for analyzing the inheritance of famous Chinese medicine practitioners, characterized in that: include: Obtain multiple student medical records; The student medical records include consultation data and diagnosis and prescription data; Based on the static rule base, all student medical records are classified into medical records to be guided and unqualified medical records, and the reasons for unqualified medical records are output; The static rule base is established based on the general principles in the field of traditional Chinese medicine, the correspondence between symptoms and signs, and the inherent syndrome differentiation and medication thinking of famous Chinese medicine practitioners; For each medical record to be guided, input the medical inquiry data of the medical record to be guided into a pre-trained dynamic simulation model to obtain simulated diagnosis and prescription data corresponding to the medical record to be guided; The dynamic simulation model is trained based on several clinical case records of famous Chinese medicine practitioners; The simulated diagnosis prescription data is used to evaluate the diagnosis prescription data of the medical record to be guided, so as to obtain an evaluation result of the medical record to be guided.

2. The method according to claim 1, characterized in that The medical consultation data includes medical history description, symptom description and tongue and pulse condition records; the diagnosis and prescription data includes diagnosis conclusion and treatment prescription; the static rule base includes description rule set, single syndrome rule set, compound syndrome rule set and prescription rule set; The steps of classifying all student medical records into medical records to be guided and unqualified medical records based on the static rule base, and outputting the unqualified reasons for each unqualified medical record, include: For each of the student medical records, based on the description rule set, a description verification is performed on the medical history description and the symptom description in the student medical record; If the description verification fails, the student's medical record is judged to be an unqualified medical record with unreasonable medical history and symptom descriptions; If the description verification passes, then based on the single syndrome type rule set and the compound syndrome type rule set, the syndrome type verification is performed on the symptom description, tongue and pulse condition records in the student's medical record and the syndrome type conclusion in the diagnosis conclusion; If the syndrome type verification fails, the student's medical record is judged to be an unqualified medical record due to incorrect syndrome differentiation; If the syndrome type verification is passed, the diagnosis conclusion and the treatment prescription are verified based on the prescription rule set; If the prescription verification fails, the student's medical record is determined to be an unqualified medical record with incorrect prescriptions; If the prescription verification passes, the student's medical record is determined to be a preliminarily qualified medical record awaiting guidance.

3. The method according to claim 1, characterized in that The diagnosis prescription data includes a diagnosis conclusion and a treatment prescription; the simulation diagnosis prescription data includes a simulation diagnosis and a simulation prescription; The step of evaluating the diagnosis prescription data of the medical record to be guided by using the simulated diagnosis prescription data comprises: Based on the simulated diagnosis, evaluating the accuracy of the diagnostic conclusion; A similarity evaluation is performed on the simulation prescription and the treatment prescription.

4. The method according to claim 3, characterized in that: The step of evaluating the similarity between the simulation prescription and the treatment prescription comprises: Obtaining the number of simulated medications in the simulated prescription; Matching each Chinese medicine in the treatment prescription with the Chinese medicine in the simulation prescription respectively to obtain the number of common Chinese medicines between the treatment prescription and the simulation prescription and each common Chinese medicine; From all the shared Chinese medicines, find each target shared Chinese medicine whose dosage in the treatment prescription and the simulation prescription is consistent; Calculating the ratio of the number of the common Chinese medicines to the number of the simulated medicines to obtain the prescription similarity between the treatment prescription and the simulated prescription; The ratio of the number of all target shared Chinese medicines to the number of shared Chinese medicines is calculated to obtain the dosage similarity between the treatment prescription and the simulation prescription.

5. The method according to any one of claims 1 to 4, characterized in that: The evaluation results of the medical records to be guided include prescription similarity and dosage similarity; the method further includes: Use the current moment as the review time for each student's medical record; A qualified label is set for each medical record to be guided and each unqualified medical record; the qualified label is preliminarily qualified or unqualified; Each unqualified medical record and its review time, qualified label and reason for unqualified are saved in the student medical record database, and each medical record to be guided and its review time, qualified label, prescription similarity and dosage similarity are saved in the student medical record database.

6. The method according to claim 5, characterized in that The student medical record also includes the student's name; the method also includes: receiving a personal growth analysis request, and obtaining an analysis period and a name of a student to be analyzed from the personal growth analysis request; Based on the name of the student to be analyzed, the analysis and evaluation results of all target student medical records whose review time is within the analysis period and belongs to the student to be analyzed are searched from the student medical record database; wherein the analysis and evaluation results of the preliminarily qualified target student medical records include the review time, qualified label, prescription similarity and dosage similarity; the analysis and evaluation results of the unqualified target student medical records include the review time and qualified label; the qualified label is qualified or unqualified; Generate a qualified line graph based on the review time and qualified labels of all target students' medical records; the qualified line graph reflects the changes in the qualification of the medical records of the students to be analyzed over time; Based on the analysis and evaluation results including the review time and prescription similarity corresponding to all target student medical records of prescription similarity, a prescription similarity growth curve is generated; Based on the analysis and evaluation results including the review time and dosage similarity corresponding to all target student medical records of dosage similarity, a dosage similarity growth curve is generated.

7. The method according to claim 6, characterized in that The student medical record also includes the time of following the teacher; the method also includes: Obtaining a qualified line graph, a similarity growth curve of prescriptions, and a similarity growth curve of dosages of representative students in the previous learning cycle whose apprenticeship time belongs to each learning year; The qualified line graphs of the representative students of each study year are integrated to obtain a comparative line graph; the broken lines of different colors in the comparative line graph reflect the qualified line graphs of the representative students of each study year; Integrate the square root similarity growth curves of representative students of different study years to obtain a square root comparison graph; curves of different colors in the square root comparison graph reflect the square root similarity growth curves of representative students of different study years; The dose similarity growth curves of representative students of different study years are integrated to obtain a dose comparison diagram; curves of different colors in the dose comparison diagram reflect the dose similarity growth curves of representative students of different study years.

8. A famous Chinese medicine inheritance analysis device, characterized in that: include: An acquisition module, used to acquire multiple student medical records; the student medical records include consultation data and diagnosis and prescription data; Static analysis module, used to classify all student medical records into medical records to be guided and unqualified medical records based on the static rule base, and output the unqualified reasons for each unqualified medical record; The static rule base is established based on the general principles in the field of traditional Chinese medicine, the correspondence between symptoms and signs, and the inherent syndrome differentiation and medication thinking of famous Chinese medicine practitioners; A simulation diagnosis module, for each medical record to be guided, inputs the consultation data of the medical record to be guided into a pre-trained dynamic simulation model to obtain simulation diagnosis prescription data corresponding to the medical record to be guided; The dynamic simulation model is trained based on several clinical case records of famous Chinese medicine practitioners; The comparison and evaluation module is used to evaluate the diagnosis and prescription data of the medical record to be guided by using the simulated diagnosis and prescription data to obtain the evaluation result of the medical record to be guided.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a software program, and when the electronic device is running, the processor executes the software program to implement the famous Chinese medicine inheritance analysis method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the famous Chinese medicine inheritance analysis method according to any one of claims 1 to 7 is implemented.

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