Doctor clinical and patient life management and control system with state target characteristic

Abstract

The invention discloses a state target characteristic doctor clinical and patient life management and control system, and relates to the technical field of doctor-patient collaborative management, and the system comprises a patient data collection module which is used for collecting health data of a patient; the state target analysis module is used for performing state target feature recognition on the health data and generating a state target analysis result; the diagnosis and treatment scheme generation module is used for determining a diagnosis and treatment scheme according to the state target analysis result and the prescription dose-effect relationship; and the doctor-patient bidirectional management module is used for transmitting a state target analysis result, a diagnosis and treatment scheme, health management guidance and monitoring feedback between the doctor end and the patient end to form a health data management cycle. Based on the state target theory and the one-rule-eight-method theory, the health data of the patient is collected, the state target analysis result is generated through state target feature recognition, the diagnosis and treatment scheme is determined in combination with the prescription dose-effect relationship, a closed-loop management mechanism is formed between doctors and the patient, and the new diagnosis and treatment normal form combining traditional Chinese medicine and western medicine is achieved.

Description

Technical Field

[0001] This application relates to the technical field of doctor-patient collaborative management, specifically to a doctor's clinical and patient life management and control system with the characteristics of state-target. Background Art

[0002] Currently, the doctor-patient collaborative management system based on the new paradigm of integrated traditional Chinese and Western medicine diagnosis and treatment has broad application prospects in the contemporary medical and health field. Such systems can be applied to various scenarios such as primary medical institutions, specialized hospitals, and regional health management centers. When doctors need to conduct traditional Chinese medicine syndrome differentiation and treatment for patients or patients need to conduct health self-management, the doctor's clinical and patient life management and control system with the characteristics of state-target can find a diagnosis and treatment plan that matches the patient's condition based on a large amount of clinical data.

[0003] In the traditional medical system, traditional Chinese medicine diagnosis and treatment often rely on doctors' personal experience for syndrome differentiation and treatment, and output corresponding treatment plans. However, the traditional Chinese medicine diagnosis and treatment system lacks standardized and quantitative indicators. How to combine traditional Chinese medicine syndrome differentiation theory with modern objective indicators and make the diagnosis and treatment process data-based and visual is an important challenge faced by the modernization development of traditional Chinese medicine. Summary of the Invention

[0004] In view of the above problems, this application is proposed.

[0005] Therefore, this application provides a doctor's clinical and patient life management and control system with the characteristics of state-target, which can solve the problems mentioned in the background art.

[0006] To solve the above technical problems, this application provides the following technical solutions: A doctor's clinical and patient life management and control system with the characteristics of state-target, including: a patient data collection module for collecting the health data of patients;

[0007] A state-target analysis module for performing state-target feature recognition on the health data to generate a state-target analysis result;

[0008] A diagnosis and treatment plan generation module for determining a diagnosis and treatment plan according to the state-target analysis result and the relationship between the dosage of traditional Chinese medicine and its efficacy;

[0009] A doctor-patient two-way management module for transmitting the state-target analysis result, the diagnosis and treatment plan, health management guidance, and monitoring feedback between the doctor side and the patient side to form a health data management loop.

[0010] As a preferred embodiment of the doctor's clinical and patient life management and control system with the characteristics of state-target described in this application, among them: the processing of the health data by the state-target analysis module includes:

[0011] Preprocessing the health data to obtain preprocessed health data;

[0012] Extract the state-target characteristic parameters from the preprocessed health data;

[0013] Generate a state-target feature vector based on the state-target characteristic parameters;

[0014] Form the state-target analysis result by comparing the state-target feature vector with the standard state-target model;

[0015] Among them, the state-target analysis module identifies the syndrome type characteristics according to the representation intensity of the state-target characteristic parameters in different physiological dimensions. When the representation intensity of the state-target characteristic parameters in a certain physiological dimension ranks first, the state-target analysis module determines this physiological dimension as the main basis for syndrome type; when the difference in the representation intensity of the state-target characteristic parameters in multiple physiological dimensions is less than the standard deviation of the representation intensity, the state-target analysis module determines the syndrome type combination in the state-target analysis result based on the interaction relationship between each physiological dimension.

[0016] As a preferred solution of the doctor's clinical and patient life management and control system with the state-target feature of the present application, wherein: the ways for the diagnosis and treatment plan generation module to determine the diagnosis and treatment plan include:

[0017] Obtain the prescription data and the corresponding effect data in the prescription-dose-effect database;

[0018] Match the prescription combination according to the syndrome type characteristics in the state-target analysis result and the effect data;

[0019] Adjust the prescription combination according to the patient's individual characteristics;

[0020] Generate the diagnosis and treatment plan;

[0021] Among them, the diagnosis and treatment plan generation module selects the corresponding main prescription for the main syndrome type identified in the state-target analysis result, and selects the corresponding auxiliary prescription for the secondary syndrome type. When the state-target analysis result contains multiple interacting syndrome types, the diagnosis and treatment plan generation module adjusts the ratio of the main prescription to the auxiliary prescription according to the interaction relationship between the syndrome types, so that the effect of the diagnosis and treatment plan matches the syndrome type characteristics in the state-target analysis result.

[0022] As a preferred solution of the doctor's clinical and patient life management and control system with the state-target feature of the present application, wherein: the health data collected by the patient data collection module includes life behavior data, physiological index data and clinical test data. The life behavior data is obtained through the patient-side application, the physiological index data is obtained through the health monitoring device, and the clinical test data is obtained through medical tests.

[0023] As a preferred solution of the doctor's clinical and patient life management system featuring state-target of the present application, wherein: the manner in which the state-target analysis module extracts the state-target characteristic parameters from the preprocessed health data includes:

[0024] Extract basic physiological indicators;

[0025] Convert the basic physiological indicators into traditional Chinese medicine theory parameters;

[0026] Map the traditional Chinese medicine theory parameters to pathological state characteristics;

[0027] Wherein, the state-target analysis module establishes the association between the western medicine objective indicators and the traditional Chinese medicine syndrome differentiation results through the correspondence relationship between the pathological state characteristics and the traditional Chinese medicine theory parameters.

[0028] As a preferred solution of the doctor's clinical and patient life management system featuring state-target of the present application, wherein: the operations of the doctor-patient two-way management module include:

[0029] Display the state-target analysis results and the diagnosis and treatment plan on the doctor side;

[0030] Provide health management functions on the patient side;

[0031] Transmit the doctor's decisions and the patient's feedback to the state-target analysis module;

[0032] Wherein, the state-target analysis module adjusts the state-target analysis results according to the doctor's decisions and the patient's feedback.

[0033] As a preferred solution of the doctor's clinical and patient life management system featuring state-target of the present application, wherein: it further includes a group health management module, and the processing of the group health management module includes:

[0034] Classify the state-target analysis results of multiple patients;

[0035] Analyze the disease patterns and treatment responses of patients in the same category;

[0036] Formulate group health management strategies;

[0037] Wherein, the group health management module establishes a three-layer health management structure of individual, group and region.

[0038] To further solve the above technical problems, the present application provides the following technical solution: a doctor's clinical and patient life management method featuring state-target, including: collecting the health data of patients;

[0039] Perform state-target characteristic recognition on the health data to generate state-target analysis results;

[0040] Determine the diagnosis and treatment plan according to the state-target analysis result and the relationship between the dosage of traditional Chinese medicine and its efficacy;

[0041] Transmit the state-target analysis result, the diagnosis and treatment plan, health management guidance and monitoring feedback between the doctor side and the patient side to form a health data management cycle.

[0042] A computer device includes a memory and a processor. The memory stores a computer program. Wherein, when the processor executes the computer program, it realizes the steps of the doctor's clinical and patient's life control system with the above-mentioned state-target characteristics.

[0043] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, it realizes the steps of the doctor's clinical and patient's life control system with the above-mentioned state-target characteristics.

[0044] Advantages of this application: This application establishes a new diagnosis and treatment paradigm integrating traditional Chinese and Western medicine through the state-target theory, collects multi-dimensional health data, conducts state-target feature recognition, determines the diagnosis and treatment plan, and establishes a two-way interaction mechanism between doctors and patients. Compared with the traditional traditional Chinese medicine diagnosis and treatment system, this application has the following advantages: First, it realizes the standardization and digitization of the traditional Chinese medicine syndrome differentiation theory, improving the standardization and repeatability of traditional Chinese medicine diagnosis and treatment; Second, it establishes an association mechanism between Western medicine objective indicators and traditional Chinese medicine syndrome differentiation results, enhancing the scientificity and verifiability of traditional Chinese medicine diagnosis and treatment; Third, it constructs a closed-loop management mechanism of doctor-patient collaboration, improving the efficiency of medical services and patient compliance; Fourth, it forms a precise diagnosis and treatment model supported by big data, promoting the modern development of traditional Chinese medicine. Brief Description of the Drawings

[0045] In order to more clearly illustrate the technical solutions of the embodiments of this application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0046] Figure 1 It is an application environment diagram of the doctor's clinical and patient's life control system with state-target characteristics proposed by this application;

[0047] Figure 2 It is a schematic diagram of the overall structure of the doctor's clinical and patient's life control system with state-target characteristics proposed by this application;

[0048] Figure 3 It is a computer device diagram in the doctor's clinical and patient's life control method with state-target characteristics proposed by this application. Detailed Embodiments

[0049] To make the above objects, features, and advantages of the present application more obvious and understandable, the following provides a detailed description of the specific embodiments of the present application in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0051] Example 1, referring to Figure 1 , which is an embodiment of the present application, provides a doctor clinical and patient life management control system with the characteristics of state-target.

[0052] The doctor-patient collaborative management system based on the new paradigm of integrated traditional Chinese and Western medicine diagnosis and treatment has broad application prospects in the contemporary medical and health field. Such systems can be applied to various scenarios such as primary medical institutions, specialized hospitals, and regional health management centers. When doctors need to conduct traditional Chinese medicine syndrome differentiation and treatment for patients or patients need to conduct health self-management, the doctor clinical and patient life management control system with the characteristics of state-target can find a diagnosis and treatment plan that matches the patient's state based on a large amount of clinical data.

[0053] In the traditional medical system, traditional Chinese medicine diagnosis and treatment often rely on the personal experience of doctors for syndrome differentiation and treatment, and output corresponding treatment plans. However, the traditional Chinese medicine diagnosis and treatment system lacks standardized and quantitative indicators. How to combine traditional Chinese medicine syndrome differentiation theory with modern objective indicators and make the diagnosis and treatment process data-driven and visual is an important challenge faced by the modernization development of traditional Chinese medicine.

[0054] In response to the problems of low standardization in the traditional Chinese medicine diagnosis and treatment system, lack of correlation between Western medicine objective indicators and traditional Chinese medicine syndrome differentiation results, and insufficient doctor-patient collaborative management, the present application proposes a doctor clinical and patient life management control system with the characteristics of state-target. This system is based on the state-target theory and the theory of one rule and eight methods, collects patient health data, generates state-target analysis results through state-target feature recognition, determines the diagnosis and treatment plan in combination with the relationship between the dosage and efficacy of prescriptions, and forms a closed-loop management mechanism between doctors and patients to achieve a new paradigm of integrated traditional Chinese and Western medicine diagnosis and treatment.

[0055] The doctor clinical and patient life management control system with the characteristics of state-target provided by the embodiments of the present application can be applied to such as Figure 1In the application environment shown. The doctor's clinical and patient life management system with the characteristics of state target provided by the embodiments of the present application includes a patient data collection module, a state target analysis module, a diagnosis and treatment plan generation module, and a doctor-patient two-way management module. This system is based on the state target theory and the theory of one principle and eight methods, realizes precise diagnosis and treatment of integrated traditional Chinese and Western medicine through state target analysis, and forms a closed-loop management mechanism through doctor-patient collaboration.

[0056] Figure 1 It is an application environment diagram of the doctor's clinical and patient life management system with the characteristics of state target in an embodiment. As Figure 1 shown, it includes terminal devices, servers, and data storage systems. The terminal devices can be hospital workstations, doctor's consulting room computers, mobile terminals of medical staff, and smartphones or health monitoring devices used by patients, etc. The server can be an internal server of a medical institution or a cloud medical and health platform. The data storage system is used to store information such as patient health data, state target feature models, and prescription dosage databases. The terminal devices communicate with the server through the network to achieve data exchange and information transmission.

[0057] Based on the same inventive concept, the embodiments of the present application also provide a doctor's clinical and patient life management method with the characteristics of state target. As Figure 2 shown, a working flow chart of the doctor's clinical and patient life management system with the characteristics of state target is provided, including the following steps:

[0058] Step 202, collect patient health data.

[0059] Through the patient data collection module, collect the patient's health data from multiple channels. The health data mainly includes three categories: life behavior data, physiological index data, and clinical test data. The life behavior data is usually collected through the patient-side application, including the patient's diet records, exercise conditions, sleep quality, mood changes, etc.; the physiological index data is usually collected in real time through wearable health monitoring devices, including heart rate, blood pressure, blood sugar, body temperature, weight, etc.; the clinical test data is provided by medical institutions, including various biochemical test results, imaging examination results, etc.

[0060] The patient data collection module can be set on the patient's mobile terminal or deployed in the information system of a medical institution. This module is connected to a variety of health monitoring devices, supports real-time data transmission, and at the same time provides a manual input function for patient health data.

[0061] Step 204, perform state target feature recognition on the health data to generate state target analysis results.

[0062] The state-target analysis module receives health data from the data acquisition module and first preprocesses the data, including operations such as data cleaning, standardization, and normalization, to obtain preprocessed health data. During the data cleaning process, the state-target analysis module will identify and process outliers, missing values, and redundant data to ensure the accuracy of subsequent analysis. The standardization process converts metrics with different dimensions into comparable standard scales, while the normalization process adjusts each metric into a unified numerical range for comprehensive evaluation.

[0063] In a specific implementation manner of this application, the preprocessing may further include data smoothing processing. By methods such as moving average, exponential smoothing, or wavelet transform, short-term fluctuations and noises in the physiological index data are eliminated, highlighting the long-term change trend, making the extraction of state-target features more stable and reliable. For physiological indexes with obvious periodicity, such as the sleep-wake cycle and the daily blood glucose fluctuation, time series decomposition technology can also be used to decompose the data into a trend term, a periodic term, and a random term, and analyze and extract features respectively.

[0064] Based on the core principle of the state-target theory, state-target characteristic parameters are extracted from the preprocessed health data, and key parameters that can reflect the state of qi, blood, body fluid, zang-fu organ functions, and cold, heat, deficiency, and excess states of the patient are extracted from the life behavior data, physiological index data, and clinical test data. For example, parameters reflecting the state of qi and blood are extracted from blood biochemical indexes, parameters reflecting zang-fu organ functions are extracted from heart rate variability and blood pressure data, and parameters reflecting cold and heat states are extracted from body temperature curves and metabolic indexes. These state-target characteristic parameters constitute a multi-dimensional representation of the patient's health state.

[0065] According to another embodiment of this application, a multi-level feature extraction method can be used to extract the state-target characteristic parameters. The first level is the basic index layer, directly extracting statistical features of each detection data, such as mean value, standard deviation, peak value, trough value, and change rate, etc.; the second level is the traditional Chinese medicine parameter layer, converting the basic indexes into parameters under the traditional Chinese medicine theory system through a mapping function, such as converting indexes such as hemoglobin and red blood cell count into qi and blood sufficiency parameters, and converting heart rate variability, blood pressure changes, etc. into zang-fu organ function parameters; the third level is the state-target feature layer, mapping the combination of traditional Chinese medicine parameters into specific pathological state-target features to form the final set of state-target characteristic parameters.

[0066] In the specific implementation of feature extraction, this application can adopt a method combining feature engineering technology and deep learning. On the one hand, based on expert knowledge, feature engineering rules are constructed to extract explicit features highly relevant to traditional Chinese medicine theory; on the other hand, deep neural networks are used to automatically learn implicit features from the original data, and the two are combined to form a more comprehensive state-target characteristic parameter system.

[0067] Based on the extracted state-target characteristic parameters, the state-target analysis module generates a state-target feature vector. The state-target feature vector is a multi-dimensional mathematical representation, where each dimension corresponds to a specific state-target characteristic parameter, and the value of the vector represents the quantitative measurement result of the parameter. This vectorized representation enables the precise description and quantification of the patient's health status within the framework of the state-target theory. The state-target feature vector forms a comprehensive description of the patient's health status by integrating multi-source health data, covering all aspects concerned in traditional Chinese medicine theory.

[0068] In a preferred embodiment of the present application, the state-target feature vector can be optimized using sparse representation or low-dimensional embedding techniques. Sparse representation eliminates redundancy and noise by retaining the most significant characteristic parameters, enhancing the interpretability of the vector; low-dimensional embedding projects the high-dimensional feature vector into a low-dimensional space using techniques such as principal component analysis, t-SNE, or autoencoders, improving computational efficiency while preserving the data structure. Additionally, the dimensional weights of the state-target feature vector can be dynamically adjusted according to the characteristics of different diseases to make it more suitable for the diagnostic requirements of specific diseases.

[0069] The state-target analysis module compares the generated state-target feature vector with the standard state-target models built into the system. The standard state-target models are a set of reference models constructed based on a large amount of clinical data and expert knowledge, containing the typical state-target characteristic patterns of different syndromes. By calculating the similarity between the state-target feature vector and each standard model, the most likely syndrome characteristics of the patient are identified, forming the state-target analysis result. This comparison process realizes the mapping transformation from objective data to traditional Chinese medicine syndromes.

[0070] According to an embodiment of the present application, the standard state-target models can be constructed in various forms. One way is a rule-based model based on expert knowledge, where traditional Chinese medicine experts define the characteristic patterns for various typical syndromes according to clinical experience and theoretical knowledge; another way is a data-driven statistical model that extracts the statistical characteristics of each syndrome by analyzing a large number of clinically annotated cases using machine learning algorithms; the third way is a hybrid model that combines expert knowledge and data analysis to construct a more comprehensive and accurate standard state-target model.

[0071] In terms of similarity calculation, the present application can select different measurement methods according to different application scenarios. For situations emphasizing the overall distribution of features, Euclidean distance or cosine similarity can be used; for situations focusing on specific key features, weighted Euclidean distance or Mahalanobis distance can be used; for situations where the correlation between features needs to be considered, kernel function similarity calculation methods can be used. Multiple similarity measures can also be combined to improve the accuracy of syndrome identification through ensemble learning.

[0072] In the process of state-target feature recognition, based on the state-target theory and the One-Principle and Eight-Methods theory, an association mapping mechanism between Western medicine objective indicators and traditional Chinese medicine syndrome differentiation results is established. The One-Principle and Eight-Methods theory provides the thinking for syndrome differentiation, while the state-target theory provides the quantification standard and judgment basis. When the characterization intensity of the state-target feature parameters in a certain physiological dimension ranks first, this physiological dimension is determined as the main syndrome type basis. For example, when the intensity of the state-target feature parameters related to qi and blood is significantly higher than other dimensions, qi and blood disharmony will be determined as the main syndrome type.

[0073] In terms of the determination of complex syndrome types, when the difference in the characterization intensity of the state-target feature parameters in multiple physiological dimensions is less than the standard deviation of the characterization intensity, the complex syndrome type of the patient will be determined based on the interaction relationship between each physiological dimension. The standard deviation of the characterization intensity is a reference value set based on historical data statistical analysis, which is used to judge whether there are significant differences in the state-target feature parameters between different physiological dimensions.

[0074] According to a further embodiment of the present application, for the determination of complex syndrome types, probability graph models such as decision trees or Bayesian networks can also be used to capture the hierarchical relationship and conditional dependence between syndrome types. These models can construct an inference path for syndrome type judgment based on expert knowledge and clinical data, making the determination process of complex syndrome types more in line with the thinking of traditional Chinese medicine diagnosis. In addition, fuzzy logic theory can be combined to handle the uncertainty and ambiguity in traditional Chinese medicine syndrome type judgment, and better simulate the thinking process of traditional Chinese medicine experts.

[0075] The finally formed state-target analysis results not only include the patient's main syndrome type, secondary syndrome type, and the relationship description between each syndrome type, but also can include the confidence evaluation of syndrome type judgment, key influencing factor analysis, and dynamic change prediction. For complex or atypical cases, multiple possible syndrome type judgment schemes are generated, accompanied by detailed analysis bases for doctors to refer to and select, so as to achieve precise syndrome differentiation of the integration of traditional Chinese and Western medicine. For example, in a specific case, the patient shows symptoms such as elevated blood sugar, fatigue, dry mouth and throat, and polyuria at night. The state-target analysis module may identify the following multiple possible syndrome type judgment schemes:

[0076] Scheme 1: The main syndrome type is "qi-yin deficiency" (confidence level 78%).

[0077] Analysis basis: Poor blood sugar control (HbA1c 8.2%), low serum cortisol (morning value 8.5 μg / dL);

[0078] Key features: Fatigue (qi deficiency manifestation), dry mouth and throat (yin deficiency manifestation);

[0079] Related physiological indicators: Reduced heart rate variability, pale and less moist tongue, thready and weak pulse.

[0080] Plan 2: The main syndrome type is "spleen-kidney yang deficiency" (confidence level: 65%).

[0081] Analysis basis: Frequent urination at night (3-4 times / night), low serum sodium (135 mmol / L);

[0082] Key features: Weak and sore waist and knees, fear of cold and cold limbs, increased nocturia;

[0083] Related physiological indicators: Low basal body temperature (36.1°C), slightly reduced thyroid function.

[0084] Plan 3: Composite syndrome type "qi-yin deficiency combined with spleen-kidney yang deficiency" (confidence level: 53%).

[0085] Analysis basis: Combining the characteristics of the above two syndrome types;

[0086] Suggested treatment idea: Focus on replenishing qi and nourishing yin, and also pay attention to warming and tonifying the spleen and kidney.

[0087] This application provides multiple syndrome type judgment plans and their analysis bases, providing comprehensive reference information for doctors. Doctors can combine their clinical experience and the specific conditions of patients to select the most appropriate diagnosis and treatment direction or formulate a more comprehensive treatment plan. This method not only gives full play to the advantages of the computer system in data analysis but also retains the leading position of doctors in clinical decision-making, realizing an accurate syndrome differentiation mode of human-computer collaboration.

[0088] Step 206: Determine the diagnosis and treatment plan according to the state-target analysis result and the relationship between formula and drug dosage-effect.

[0089] The diagnosis and treatment plan generation module generates a diagnosis and treatment plan for the patient based on the state-target analysis result and in combination with the data in the formula-drug dosage-effect database. This process includes querying the formula-drug dosage-effect database to obtain the data on the effect of the formula on specific state-targets, matching the prescription combinations suitable for the patient's syndrome type characteristics, and adjusting the formula-drug ratio according to the patient's individual characteristics, finally forming a complete diagnosis and treatment plan.

[0090] In one implementation, the query process of the formula-drug dosage-effect database adopts a multi-dimensional indexing technology. By establishing a multi-dimensional mapping relationship of syndrome type - formula - effect, fast and accurate query is realized. First, the state-target analysis result is converted into a standardized query condition, and then the formula records that meet the conditions are retrieved in the formula-drug dosage-effect database. The query results are sorted according to the effect score of the formula on the target syndrome type, providing candidate plans for subsequent prescription combinations. The formula-drug dosage-effect data also includes the compatibility information, taboo information, and synergistic effect data between formulas, providing important references for prescription combinations.

[0091] For the matching of prescription combinations, the present application adopts a hierarchical optimization strategy. First, based on the main syndrome types, the core prescription, i.e., the main prescription, is selected; then, for the secondary syndrome types, the auxiliary prescriptions, i.e., the auxiliary prescriptions, are selected; finally, according to the individual characteristics and symptom manifestations of the patient, appropriate flavoring drugs are selected for addition or subtraction adjustment. This hierarchical prescription strategy conforms to the traditional Chinese medicine prescription principle of "sovereign, minister, assistant, and guide", and can formulate a comprehensive and accurate treatment plan for complex syndrome types.

[0092] In another embodiment of the present application, the diagnosis and treatment plan generation module can adopt a method that combines a rule-based expert system with intelligent recommendation based on machine learning. The rule-based expert system contains the prescription rules and empirical knowledge summarized by traditional Chinese medicine experts, and can handle common syndrome types and typical cases; the intelligent recommendation based on machine learning learns the optimal prescription combination pattern by analyzing the treatment effect data of a large number of clinical cases, and is especially suitable for complex syndrome types and atypical cases. The two methods complement each other to jointly improve the scientificity and level of the diagnosis and treatment plan.

[0093] For the main syndrome types identified in the state-target analysis results, the corresponding main prescriptions are selected; for the secondary syndrome types, the corresponding auxiliary prescriptions are selected. The determination of the main prescription and the auxiliary prescription follows the traditional Chinese medicine prescription principle. The main prescription targets the main syndrome type and plays a leading treatment role; the auxiliary prescription targets the secondary syndrome type and plays an auxiliary treatment role. This prescription structure ensures the integrity and pertinence of the prescription and can comprehensively cover the syndrome type characteristics of the patient.

[0094] In the preferred implementation, the selection of the main prescription and the auxiliary prescription also considers factors such as the clinical application frequency of the prescription, the stability of the treatment effect, and the risk of adverse reactions. Classic prescriptions with wide clinical application, definite curative effects, and high safety are preferentially selected as candidates for the main prescription and the auxiliary prescription, and then fine screening is carried out according to the specific syndrome type characteristics and symptom manifestations. For special situations or rare syndrome types, expert consensus and the latest research results are also referred to provide innovative prescription suggestions.

[0095] When the analysis results contain multiple mutually influencing syndrome types, the ratio of the main prescription to the auxiliary prescription is adjusted according to the mutual relationship between the syndrome types to ensure that the diagnosis and treatment plan matches the overall syndrome type characteristics of the patient. The mutual relationship between syndrome types refers to the mutual influence and transformation laws between different syndrome types. For example, some syndrome types may be causal to each other, some may be antagonistic to each other, and some may promote each other. According to these relationships, the composition and dosage ratio of the main prescription and the auxiliary prescription are dynamically adjusted to achieve the overall balance of the prescription.

[0096] For example, in a specific case, the patient has two syndromes simultaneously, namely "qi deficiency and blood stasis" and "liver depression and spleen weakness". The former is the main syndrome type, and the latter is the secondary syndrome type. There is an interaction between these two syndrome types: qi deficiency can lead to aggravated blood stasis, liver depression can exacerbate spleen weakness, and spleen weakness further affects the generation and transformation of qi and blood. In response to this situation, the following prescription combinations may be generated:

[0097] Main prescription: Prescription for supplementing qi and promoting blood circulation (Astragalus membranaceus, Angelica sinensis, Ligusticum wallichii, Paeonia lactiflora, etc.) Auxiliary prescription: Prescription for soothe the liver and strengthen the spleen (Bupleurum chinense, Atractylodes macrocephala, Poria cocos, Citrus reticulata Blanco, etc.) Dosage adjustment: Increase the dosage of Astragalus membranaceus to strengthen the qi-supplementing effect, appropriately reduce the dosage of Bupleurum chinense to avoid excessive liver-soothing and damaging the healthy qi, and at the same time increase the dosages of Atractylodes macrocephala and Poria cocos to enhance the spleen-strengthening effect

[0098] Through this dosage adjustment, it is possible to formulate a coordinated and overall balanced prescription combination according to the characteristics of the patient's complex syndrome types, treating both the main syndrome type and taking into account the secondary syndrome type, while considering the interaction between syndrome types.

[0099] In terms of individualized adjustment, the diagnosis and treatment plan generation module will also consider individual characteristics of the patient, such as age, gender, physical constitution characteristics, co-existing symptoms, drug allergy history, etc., and further adjust the prescription. For example, for elderly and frail patients, the drug dosage and potency may be reduced; for patients with poor digestive function, drugs for strengthening the spleen and promoting digestion may be increased; for patients with a specific drug allergy history, the relevant drugs will be avoided or alternative drugs will be used. This individualized adjustment ensures the safety and applicability of the diagnosis and treatment plan, enabling it to best meet the individual needs of the patient.

[0100] The prescription dosage-effect database is constructed based on a large amount of clinical practice and research data, and includes the evaluation of the effects of various traditional Chinese medicine prescriptions on different syndrome types. This database is continuously updated and improved, providing reliable data support for the generation of diagnosis and treatment plans.

[0101] In an implementation manner of the present application, the prescription dosage-effect database adopts a multi-source data fusion architecture, integrating multiple data sources: one is the classical prescriptions and their applicable syndrome types recorded in traditional Chinese medicine classics; the second is the prescription efficacy data verified in modern clinical research; the third is the prescription usage patterns and effect feedback extracted from real-world treatment data; the fourth is the action mechanisms of traditional Chinese medicine components and pharmacodynamic data found in pharmacological research. This multi-source data fusion architecture ensures the comprehensiveness and scientific nature of the database content, and can take into account both traditional experience and modern research results at the same time.

[0102] Database update and improvement is an ongoing process. New prescription-dosage data is collected through multiple channels: on the one hand, regularly integrating the prescription efficacy data in the latest published traditional Chinese medicine research literature; on the other hand, collecting the prescription application effect data in actual treatment through the system's own usage feedback. After being processed by standardization and quality assessment, these new data are integrated into the existing database, continuously enriching and optimizing the data content. At the same time, this application can also use machine learning algorithms to regularly analyze and update the prescription-dosage relationship in the database, identify potential new associations and rules, and improve the accuracy and practicality of the database.

[0103] Through this scientific, dynamic, and comprehensive prescription-dosage database, combined with an intelligent diagnosis and treatment plan generation algorithm, this application can provide highly theoretical and clinically practical TCM prescription suggestions for doctors, effectively improving the standardization level and accuracy of TCM diagnosis and treatment, while retaining the characteristics of TCM syndrome differentiation and treatment.

[0104] Step 208, transmit the state-target analysis results, diagnosis and treatment plans, health management guidance, and monitoring feedback between the doctor side and the patient side to form a health data management cycle.

[0105] As an important component, the doctor-patient two-way management module realizes information communication and collaborative management between the doctor side and the patient side. On the doctor side, the state-target analysis results and recommended diagnosis and treatment plans of patients are displayed through a multi-dimensional dashboard, and doctors can make diagnosis and treatment decisions based on this information. On the patient side, functions such as health data filling, state-target status viewing, health education, and behavior intervention are provided to guide patients to conduct scientific health management.

[0106] The feedback from patients and new health data are continuously collected and transmitted back to the system. Based on this, the state-target analysis module updates the state-target analysis results, and the diagnosis and treatment plan generation module adjusts the diagnosis and treatment suggestions accordingly. This doctor-patient two-way interaction and data feedback mechanism forms a complete health data management cycle, realizing the dynamic monitoring of patients' health conditions and the continuous optimization of medical plans.

[0107] This application establishes a new paradigm for integrated traditional Chinese and Western medicine diagnosis and treatment through the state-target theory, collects multi-dimensional health data, conducts state-target feature recognition, determines diagnosis and treatment plans, and establishes a doctor-patient two-way interaction mechanism. Compared with traditional TCM diagnosis and treatment systems, this application has the following advantages: First, it realizes the standardization and digitization of TCM syndrome differentiation theory, improving the standardization and repeatability of TCM diagnosis and treatment; second, it establishes an association mechanism between Western medicine objective indicators and TCM syndrome differentiation results, enhancing the scientificity and verifiability of TCM diagnosis and treatment; third, it constructs a closed-loop management mechanism for doctor-patient collaboration, improving the efficiency of medical services and patient compliance; fourth, it forms a precision diagnosis and treatment model supported by big data, promoting the modern development of traditional Chinese medicine.

[0108] In another exemplary embodiment, the present application may further include a population health management module for implementing three-level health management of "individual-population-region". This module classifies and clusters the state-target analysis results of multiple patients, identifies the disease development patterns and treatment response characteristics of the same type of patient groups, and provides data support and decision-making basis for regional health management and disease prevention and control.

[0109] Each module in the above-mentioned doctor's clinical and patient life control system with state-target characteristics can be implemented in whole or in part by software, hardware, and their combinations. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned each module.

[0110] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 3 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, mobile cellular networks, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for positioning a grounding wire clamp using satellite positioning and landmark calibration. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0111] Those skilled in the art can understand, Figure 3The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0112] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0113] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0114] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0115] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0116] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0117] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in the present application.

[0118] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A doctor's clinical and patient life management system with the characteristics of a state target, characterized in that including: a patient data acquisition module for acquiring the health data of a patient; a state-target analysis module for performing state-target feature recognition on the health data to generate a state-target analysis result; a diagnosis and treatment plan generation module for determining a diagnosis and treatment plan according to the state-target analysis result and the relationship between the dosage of the prescription and the efficacy; a doctor-patient two-way management module for transmitting the state-target analysis result, the diagnosis and treatment plan, health management guidance and monitoring feedback between the doctor side and the patient side to form a health data management cycle.

2. The state-target characteristic doctor clinical and patient life management and control system according to claim 1, characterized in that: The processing of the health data by the state-target analysis module includes: performing preprocessing on the health data to obtain preprocessed health data; extracting state-target feature parameters from the preprocessed health data; generating a state-target feature vector based on the state-target feature parameters; forming the state-target analysis result by comparing the state-target feature vector with a standard state-target model; wherein, the state-target analysis module identifies syndrome type features according to the representation intensity of the state-target feature parameters in different physiological dimensions. When the representation intensity of the state-target feature parameters in a certain physiological dimension ranks first, the state-target analysis module determines this physiological dimension as the main syndrome type basis; when the difference in the representation intensity of the state-target feature parameters in multiple physiological dimensions is less than the standard deviation of the representation intensity, the state-target analysis module determines the syndrome type combination in the state-target analysis result based on the interaction relationship between the physiological dimensions.

3. The doctor's clinical and patient life management system with the characteristics of the state target as described in claim 2, characterized in that: The manner in which the diagnosis and treatment plan generation module determines the diagnosis and treatment plan includes: obtaining the prescription data and the corresponding effect data in the prescription dosage-effect database; matching a prescription combination according to the syndrome type features in the state-target analysis result and the effect data; adjusting the prescription combination according to the individual characteristics of the patient; generating the diagnosis and treatment plan; wherein, the diagnosis and treatment plan generation module selects the corresponding main prescription for the main syndrome type identified in the state-target analysis result and selects the corresponding auxiliary prescription for the secondary syndrome type. When the state-target analysis result contains multiple interacting syndrome types, the diagnosis and treatment plan generation module adjusts the ratio of the main prescription to the auxiliary prescription according to the interaction relationship between the syndrome types so that the effect of the diagnosis and treatment plan matches the syndrome type features in the state-target analysis result.

4. The state-target characteristic doctor clinical and patient life management and control system according to claim 3, characterized in that: The health data collected by the patient data acquisition module includes life behavior data, physiological index data and clinical test data. The life behavior data is obtained through the patient-side application, the physiological index data is obtained through health monitoring devices, and the clinical test data is obtained through medical tests.

5. The doctor's clinical and patient life management system with the characteristics of the state target as described in claim 4, characterized in that: The manner in which the state-target analysis module extracts the state-target feature parameters from the preprocessed health data includes: extracting basic physiological indexes; converting the basic physiological indexes into traditional Chinese medicine theory parameters; mapping the traditional Chinese medicine theory parameters into pathological state features; wherein, the state-target analysis module establishes the association between the western medicine objective indexes and the traditional Chinese medicine syndrome differentiation results through the corresponding relationship between the pathological state features and the traditional Chinese medicine theory parameters.

6. The doctor's clinical and patient life management system with the characteristics of the state target as described in claim 5, characterized in that: The operations of the doctor-patient two-way management module include: displaying the state-target analysis result and the diagnosis and treatment plan on the doctor side; providing a health management function on the patient side; transmitting the doctor's decision and the patient's feedback to the state-target analysis module; Among them, the state-target analysis module adjusts the state-target analysis result according to the doctor's decision and the patient's feedback.

7. The state-target characteristic doctor clinical and patient life management and control system according to claim 6, characterized in that: It further includes a population health management module, and the processing of the population health management module includes: Classifying the state-target analysis results of multiple patients; Analyzing the disease patterns and treatment responses of patients in the same category; Formulating population health management strategies; Among them, the population health management module establishes a three-layer health management structure of individual, population, and region.

8. The method for doctors' clinical practice and patient life management with the characteristics of state targets, based on the system for doctors' clinical practice and patient life management with the characteristics of state targets according to any one of claims 1 to 7, is characterized in that: It includes: Collecting the health data of patients; Performing state-target feature recognition on the health data to generate state-target analysis results; Determining a diagnosis and treatment plan according to the state-target analysis result and the relationship between the dosage of medicine and its efficacy; Transmitting the state-target analysis result, the diagnosis and treatment plan, health management guidance, and monitoring feedback between the doctor side and the patient side to form a health data management cycle.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the state-target featured doctor's clinical and patient life control system according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the state-target featured doctor's clinical and patient life control system according to any one of claims 1 to 7.

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