State target characteristic doctor clinical and patient life management system

Abstract

The application discloses a doctor clinic and patient life management and control system with state target characteristics, relates to the technical field of doctor-patient collaborative management, and comprises the following: a patient data acquisition module, which is used for acquiring health data of a patient; a state target analysis module, which is used for performing state target characteristic identification on the health data to generate a state target analysis result; a diagnosis and treatment scheme generation module, which is used for determining a diagnosis and treatment scheme according to the state target analysis result and a drug-dose-effect relationship; and a doctor-patient bidirectional management module, which is used for transmitting the state target analysis result, the diagnosis and treatment scheme, health management guidance and monitoring feedback between a doctor end and a patient end to form a health data management cycle. The application is based on the state target theory and the one-to-eight method theory, acquires health data of a patient, generates a state target analysis result through state target characteristic identification, determines a diagnosis and treatment scheme in combination with a drug-dose-effect relationship, and forms a closed-loop management mechanism between doctors and patients, so that a new paradigm of combined diagnosis and treatment of traditional Chinese medicine and western medicine is realized.

Description

Technical Field

[0001] This application relates to the field of doctor-patient collaborative management technology, specifically a doctor's clinical and patient life management system with Taipi characteristics. Background Technology

[0002] Currently, doctor-patient collaborative management systems based on the new paradigm of integrated traditional Chinese and Western medicine diagnosis and treatment have broad application prospects in the contemporary medical and health field. These systems can be applied to various scenarios such as primary healthcare institutions, specialized hospitals, and regional health management centers. When doctors need to provide TCM diagnosis and treatment or when patients need to manage their health themselves, Taibai's unique doctor clinical and patient life management system can find treatment plans that match the patient's condition based on a large amount of clinical data.

[0003] In traditional medical systems, TCM diagnosis and treatment often rely on the doctor's personal experience to differentiate syndromes and formulate corresponding treatment plans. However, the TCM diagnosis and treatment system lacks standardized and quantitative indicators. How to combine TCM syndrome differentiation theory with modern objective indicators and make the diagnosis and treatment process data-driven and visualized is a significant challenge facing the modernization of TCM. Summary of the Invention

[0004] In view of the aforementioned problems, this application is hereby filed.

[0005] Therefore, this application provides a physician clinical and patient life management system with unique features, which can solve the problems mentioned in the background art.

[0006] To address the aforementioned technical issues, this application provides the following technical solution: a doctor's clinical and patient life management system with unique features, including: a patient data acquisition module for collecting patients' health data;

[0007] The state-target analysis module is used to identify state-target features in the health data and generate state-target analysis results.

[0008] The treatment plan generation module is used to determine the treatment plan based on the target analysis results and the dose-effect relationship of the prescription.

[0009] The doctor-patient two-way management module is used to transmit the target analysis results, treatment plans, health management guidance and monitoring feedback between the doctor and the patient, forming a health data management cycle.

[0010] As a preferred embodiment of the physician clinical and patient life management system with state-target features described in this application, wherein: the state-target analysis module, used for state-target feature identification of health data, includes:

[0011] The health data is preprocessed to obtain preprocessed health data;

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

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

[0014] The state target analysis results are formed by comparing the state target feature vector with the standard state target model;

[0015] Specifically, the state-target analysis module identifies the syndrome characteristics based on the representation intensity of the state-target feature parameters in different physiological dimensions. When the representation intensity of the state-target feature parameter in a certain physiological dimension is ranked first, the state-target analysis module determines this physiological dimension as the main basis for syndrome identification. When the difference in representation intensity of the state-target feature parameters in multiple physiological dimensions is less than the standard deviation of representation intensity, the state-target analysis module determines the syndrome combination in the state-target analysis results based on the interaction relationship between each physiological dimension.

[0016] As a preferred embodiment of the physician clinical and patient life management system with the characteristics of the target described in this application, wherein: the treatment plan generation module determines the treatment plan in the following ways:

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

[0018] Prescription combinations are matched based on the syndrome characteristics in the target analysis results and the efficacy data.

[0019] The prescription combination is adjusted according to the individual characteristics of the patient;

[0020] Generate the aforementioned treatment plan;

[0021] Specifically, the treatment plan generation module selects the corresponding primary prescription for the primary syndrome type identified in the state-target analysis results and the corresponding auxiliary prescription for the secondary syndrome type. When the state-target analysis results contain multiple mutually influential syndrome types, the treatment plan generation module adjusts the ratio of the primary prescription to the auxiliary prescription according to the interaction relationship between the syndrome types, so that the effect of the treatment plan matches the syndrome type characteristics in the state-target analysis results.

[0022] As a preferred embodiment of the physician clinical and patient life management system with the characteristics of the target described in this application, the health data collected by the patient data acquisition module includes lifestyle data, physiological indicator data and clinical test data. The lifestyle data is obtained through a patient-side application, the physiological indicator data is obtained through a health monitoring device, and the clinical test data is obtained through medical testing.

[0023] As a preferred embodiment of the physician clinical and patient life management system with state-target features described in this application, wherein the state-target analysis module extracts the state-target feature parameters from the preprocessed health data in the following manner:

[0024] Extract basic physiological indicators;

[0025] The basic physiological indicators were converted into parameters according to traditional Chinese medicine theory.

[0026] The parameters of the TCM theory are mapped to pathological characteristics;

[0027] The target analysis module establishes a correlation between Western medicine objective indicators and TCM syndrome differentiation results by establishing the correspondence between the pathological characteristics and the TCM theoretical parameters.

[0028] As a preferred embodiment of the physician clinical and patient life management system with the characteristics of the target described in this application, the operation of the doctor-patient two-way management module includes:

[0029] The target analysis results and the treatment plan are displayed on the doctor's end.

[0030] Provide health management functions on the patient's end;

[0031] Transmit physician decisions and patient feedback to the target analysis module;

[0032] The state-target analysis module adjusts the state-target analysis results based on the doctor's decision and the patient's feedback.

[0033] As a preferred embodiment of the physician clinical and patient life management system with the characteristics of the target described in this application, it further includes a group health management module, the processing of which includes:

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

[0035] Analyze the disease patterns and treatment responses of similar patients;

[0036] Develop group health management strategies;

[0037] The group health management module establishes a three-tiered health management structure encompassing individuals, groups, and regions.

[0038] To further address the aforementioned technical issues, this application provides the following technical solution: a unique physician clinical and patient lifestyle management method, including: collecting patient health data;

[0039] The health data is subjected to state target feature identification to generate state target analysis results;

[0040] Based on the target analysis results and the dose-effect relationship of the prescription, a treatment plan is determined;

[0041] The transmission of the target analysis results, treatment plans, health management guidance, and monitoring feedback between the doctor's and patient's ends constitutes a health data management loop.

[0042] A computer device includes a memory and a processor, the memory storing a computer program, wherein the processor executes the computer program to implement the steps of a physician clinical and patient life management system as described above.

[0043] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a physician clinical and patient life management system as described above.

[0044] The beneficial effects of this application are as follows: This application establishes a new paradigm for integrated traditional Chinese and Western medicine diagnosis and treatment through state-target theory, collects multidimensional health data, identifies state-target features, determines treatment plans, and establishes a two-way interactive mechanism between doctors and patients. Compared with the traditional TCM diagnosis and treatment system, this application has the following advantages: First, it achieves the standardization and digitization of TCM syndrome differentiation theory, improving the standardization and repeatability of TCM diagnosis and treatment; second, it establishes a correlation mechanism between objective indicators of Western medicine and TCM syndrome differentiation results, enhancing the scientific nature 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; and fourth, it forms a precision diagnosis and treatment model supported by big data, promoting the modernization of traditional Chinese medicine. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a diagram illustrating the application environment of the physician clinical and patient life management system with the characteristics of the target proposed in this application.

[0047] Figure 2 This is a schematic diagram of the overall structure of the physician clinical and patient life management system with the characteristics of the target proposed in this application;

[0048] Figure 3 This is a diagram of the computer equipment used in the physician clinical and patient life management method with the characteristics of the state-targeted approach proposed in this application. Detailed Implementation

[0049] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without inventive effort should fall within the scope of protection of this application.

[0050] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0051] Example 1, referring to Figure 1 As one embodiment of this application, a physician clinical and patient life management system with the characteristics of StateTarget is provided.

[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. This type of system can be applied to various scenarios such as primary medical institutions, specialized hospitals, and regional health management centers. When doctors need to conduct TCM diagnosis and treatment for patients or when patients need to manage their health themselves, the unique doctor clinical and patient life management system of Taibai can find a diagnosis and treatment plan that matches the patient's condition based on a large amount of clinical data.

[0053] In traditional medical systems, TCM diagnosis and treatment often rely on the doctor's personal experience to differentiate syndromes and formulate corresponding treatment plans. However, the TCM diagnosis and treatment system lacks standardized and quantitative indicators. How to combine TCM syndrome differentiation theory with modern objective indicators and make the diagnosis and treatment process data-driven and visualized is a significant challenge facing the modernization of TCM.

[0054] To address the issues of low standardization in traditional Chinese medicine (TCM) diagnosis and treatment systems, lack of correlation between objective indicators from Western medicine and TCM diagnostic results, and insufficient doctor-patient collaborative management, this application proposes a state-target-based clinical and patient life management system. Based on state-target theory and the theory of "one principle and eight methods," this system collects patient health data, generates state-target analysis results through state-target feature recognition, determines treatment plans by combining the dose-effect relationship of prescriptions and drugs, and forms a closed-loop management mechanism between doctors and patients, realizing a new paradigm of integrated TCM and Western medicine diagnosis and treatment.

[0055] The physician clinical and patient life management system with the characteristics of the present application embodiment can be applied to, for example... Figure 1The application environment shown is illustrated. The physician clinical and patient life management system with state-target characteristics provided in this application includes a patient data acquisition module, a state-target analysis module, a treatment plan generation module, and a doctor-patient two-way management module. This system is based on state-target theory and the theory of "one principle and eight methods," achieving precise diagnosis and treatment combining traditional Chinese and Western medicine through state-target analysis, and forming a closed-loop management mechanism through doctor-patient collaboration.

[0056] Figure 1 This is a diagram illustrating the application environment of a physician's clinical and patient life management system, featuring a specific target, in one embodiment. For example... Figure 1 As shown, the system includes terminal devices, servers, and data storage systems. Terminal devices can be hospital workstations, doctors' office computers, mobile terminals for medical staff, and smartphones or health monitoring devices used by patients. Servers can be internal servers within the medical institution or cloud-based healthcare platforms. The data storage system stores patient health data, target characteristic models, and drug-dose-effect databases, among other information. Terminal devices communicate with the server via a network to exchange data and transmit information.

[0057] Based on the same inventive concept, embodiments of this application also provide a method for physician clinical and patient lifestyle management with a focus on specific targets. For example... Figure 2 As shown, a workflow diagram of a physician clinical and patient life management system with a focus on target characteristics is provided, including the following steps:

[0058] Step 202: Collect patient health data.

[0059] The patient data collection module gathers patient health data from multiple channels. This health data primarily falls into three categories: lifestyle data, physiological indicator data, and clinical test data. Lifestyle data is typically collected through patient-side applications and includes patient dietary records, exercise patterns, sleep quality, and mood changes. Physiological indicator data is usually collected in real-time via wearable health monitoring devices and includes heart rate, blood pressure, blood sugar, body temperature, and weight. Clinical test data is provided by medical institutions and includes various biochemical test results and imaging examination results.

[0060] The patient data acquisition module can be installed on the patient's mobile device or deployed in the medical institution's information system. This module connects to various health monitoring devices, supports real-time data transmission, and also provides a manual entry function for patient health data.

[0061] Step 204: Perform state target feature identification on the health data and generate state target analysis results.

[0062] The target analysis module receives health data from the data acquisition module and first preprocesses the data, including data cleaning, standardization, and normalization, to obtain preprocessed health data. During data cleaning, the target analysis module identifies and handles outliers, missing values, and redundant data to ensure the accuracy of subsequent analysis. Standardization converts indicators with different dimensions into comparable standard scales, while normalization adjusts the indicators to a uniform numerical range for easier comprehensive evaluation.

[0063] In one specific embodiment of this application, preprocessing may further include data smoothing, using methods such as moving average, exponential smoothing, or wavelet transform to eliminate short-term fluctuations and noise in physiological indicator data, highlighting long-term trends, and making target feature extraction more stable and reliable. For physiological indicators with obvious periodicity, such as sleep-wake cycles and diurnal blood glucose fluctuations, time-series decomposition techniques can also be used to decompose the data into trend terms, periodic terms, and random terms for analysis and feature extraction respectively.

[0064] Extracting state-target feature parameters from preprocessed health data is based on the core principles of state-target theory. This involves extracting key parameters reflecting a patient's Qi, blood, body fluid status, organ function, and state of cold / heat or deficiency / excess from lifestyle, physiological, and clinical test data. For example, parameters reflecting Qi and blood status are extracted from blood biochemical indicators; parameters reflecting organ function are extracted from heart rate variability and blood pressure data; and parameters reflecting cold / heat status are extracted from body temperature curves and metabolic indicators. These state-target feature parameters constitute a multidimensional representation of a patient's health status.

[0065] According to another embodiment of this application, the extraction of state-target feature parameters can employ a multi-level feature extraction method. The first level is the basic indicator layer, which directly extracts the statistical features of various detection data, such as mean, standard deviation, peak value, trough, and rate of change. The second level is the TCM parameter layer, which transforms the basic indicators into parameters under the TCM theoretical system through mapping functions. For example, indicators such as hemoglobin and red blood cell count are transformed into parameters of qi and blood fullness, and heart rate variability and blood pressure changes are transformed into parameters of organ function. The third level is the state-target feature layer, which maps the combination of TCM parameters into specific pathological state-target features, forming the final set of state-target feature parameters.

[0066] In the specific implementation of feature extraction, this application can adopt a method that combines feature engineering techniques with deep learning. On the one hand, feature engineering rules are constructed based on expert knowledge to extract explicit features that are highly related to traditional Chinese medicine theory; on the other hand, implicit features are automatically learned from the original data using deep neural networks. The combination of the two forms a more comprehensive state-target feature parameter system.

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

[0068] In a preferred embodiment of this application, the state-target feature vector can be optimized using sparse representation or low-dimensional embedding techniques. Sparse representation improves the interpretability of the vector by retaining the most salient feature parameters, eliminating redundancy and noise; low-dimensional embedding utilizes techniques such as principal component analysis, t-SNE, or autoencoders to project high-dimensional feature vectors into a low-dimensional space, preserving data structure while improving computational efficiency. Furthermore, the dimensional weights of the state-target feature vector can be dynamically adjusted according to the characteristics of different diseases, making it more adaptable to the diagnostic needs of specific diseases.

[0069] The state-target analysis module compares the generated state-target feature vectors with the system's built-in standard state-target models. These standard state-target models are a set of reference models built upon extensive clinical data and expert knowledge, containing typical state-target feature patterns for different syndrome types. By calculating the similarity between the state-target feature vectors and each standard model, the most likely syndrome type characteristics of the patient are identified, forming the state-target analysis results. This comparison process achieves a mapping transformation from objective data to TCM syndrome types.

[0070] According to one embodiment of this application, the standard target model can be constructed in several forms. One approach is a rule-based model based on expert knowledge, where TCM experts define characteristic patterns for various typical syndromes based on clinical experience and theoretical knowledge. Another approach is a data-driven statistical model, which uses machine learning algorithms to extract statistical features of each syndrome by analyzing a large number of clinical cases labeled with syndromes. A third approach is a hybrid model, which combines expert knowledge and data analysis to construct a more comprehensive and accurate standard target model.

[0071] Regarding similarity calculation, this application can select different measurement methods according to different application scenarios. For cases emphasizing the overall distribution of features, Euclidean distance or cosine similarity can be used; for cases focusing on specific key features, weighted Euclidean distance or Mahalanobis distance can be used; for cases that need to consider the correlation between features, kernel function similarity calculation method can be used. Multiple similarity measures can also be combined to improve the accuracy of ID card recognition through ensemble learning.

[0072] In the process of state-target feature identification, a correlation mapping mechanism between Western medicine objective indicators and TCM syndrome differentiation results was established based on state-target theory and the "One Principle and Eight Methods" theory. The "One Principle and Eight Methods" theory provides the diagnostic approach, while state-target theory provides quantitative standards and judgment criteria. When the intensity of the state-target feature parameter of a certain physiological dimension ranks first, that physiological dimension is identified as the primary syndrome. For example, when the intensity of the state-target feature parameter related to Qi and blood is significantly higher than that of other dimensions, Qi and blood imbalance will be identified as the primary syndrome.

[0073] In determining complex syndrome types, when the difference in the intensity of state-target characteristic parameters across multiple physiological dimensions is less than the standard deviation of the intensity, the complex syndrome type is determined based on the interaction relationships between the physiological dimensions. The standard deviation of the intensity is a reference value set based on statistical analysis of historical data, used to determine whether there are significant differences in state-target characteristic parameters between different physiological dimensions.

[0074] According to further embodiments of this application, the determination of complex syndromes can also employ probabilistic graphical models such as decision trees or Bayesian networks to capture the hierarchical relationships and conditional dependencies between syndromes. These models can construct reasoning paths for syndrome determination based on expert knowledge and clinical data, making the determination process of complex syndromes more consistent with TCM diagnostic thinking. Furthermore, fuzzy logic theory can be incorporated to address the uncertainty and fuzziness in TCM syndrome determination, better simulating the thought process of TCM experts.

[0075] The final state-target analysis results not only include descriptions of the patient's primary and secondary syndromes and the relationships between them, but also confidence assessments of syndrome differentiation, analysis of key influencing factors, and predictions of dynamic changes. For complex or atypical cases, multiple possible syndrome differentiation schemes are generated, along with detailed analytical justifications, for doctors to refer to and select, thereby achieving accurate syndrome differentiation through the integration of traditional Chinese and Western medicine. For example, in a specific case, the patient simultaneously exhibits symptoms such as elevated blood sugar, fatigue, dry mouth and throat, and nocturia. The state-target analysis module may identify the following multiple possible syndrome differentiation schemes:

[0076] Option 1: The main syndrome is "deficiency of both Qi and Yin" (78% confidence level).

[0077] Analysis basis: poor glycemic control (HbA1c 8.2%) and low serum cortisol (morning value 8.5 μg / dL);

[0078] Key characteristics: fatigue and weakness (manifestations of Qi deficiency), dry mouth and throat (manifestations of Yin deficiency);

[0079] Relevant physiological indicators: decreased heart rate variability, pale red tongue with little saliva, and weak pulse.

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

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

[0082] Key symptoms: lower back and knee pain, cold limbs, and increased urination at night;

[0083] Relevant physiological indicators: low basal body temperature (36.1℃), mild hypothyroidism.

[0084] Option 3: Complex syndrome “Qi and Yin deficiency combined with spleen and kidney Yang deficiency” (53% confidence level).

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

[0086] Suggested treatment approach: Focus on tonifying Qi and nourishing Yin, while also warming and tonifying the spleen and kidneys.

[0087] This application provides doctors with comprehensive reference information by offering multiple syndrome differentiation methods and their analytical basis. Doctors can combine their clinical experience with the patient's specific condition to choose the most appropriate treatment direction or develop a more comprehensive treatment plan. This approach fully leverages the advantages of computer systems in data analysis while preserving the doctor's leading role in clinical decision-making, achieving a precise syndrome differentiation model through human-computer collaboration.

[0088] Step 206: Determine the treatment plan based on the target analysis results and the dose-effect relationship of the prescription.

[0089] The treatment plan generation module generates treatment plans for patients based on the results of state-target analysis and data from the prescription dose-effect database. This process includes querying the prescription dose-effect database to obtain data on the effects of prescriptions on specific state targets, matching prescription combinations suitable for the patient's syndrome characteristics, adjusting the prescription ratios according to the patient's individual characteristics, and finally forming a complete treatment plan.

[0090] In one implementation, the query process of the prescription dose-effect database employs multidimensional indexing technology. By establishing a multidimensional mapping relationship between syndrome type, prescription, and effect, it achieves fast and accurate querying. First, the state-target analysis results are converted into standardized query conditions. Then, prescription records that meet the conditions are retrieved from the prescription dose-effect database. The query results are sorted according to the efficacy score of the prescription on the target syndrome type, providing candidate solutions for subsequent prescription combinations. The prescription dose-effect data also includes compatibility information, contraindications information, and synergistic effect data between prescriptions, providing important references for prescription combinations.

[0091] For the matching of prescription combinations, this application adopts a hierarchical optimization strategy. First, a core prescription, i.e., the main prescription, is selected based on the primary syndrome type; then, auxiliary prescriptions, i.e., the adjuvant prescriptions, are selected for secondary syndrome types; finally, appropriate flavoring drugs are added or subtracted according to the patient's individual characteristics and symptom presentation. This hierarchical prescription strategy conforms to the TCM principle of "principal, assistant, adjuvant, and guide" in prescription formulation, enabling the development of comprehensive and precise treatment plans for complex syndrome types.

[0092] In another embodiment of this application, the treatment plan generation module can employ a combination of rule-based expert systems and machine learning-based intelligent recommendations. The rule-based expert system incorporates prescription rules and experiential knowledge summarized by TCM experts, capable of handling common syndromes and typical cases. The machine learning-based intelligent recommendation system, on the other hand, learns the optimal prescription combination pattern by analyzing treatment effect data from a large number of clinical cases, making it particularly suitable for complex syndromes and atypical cases. The two methods complement each other, jointly improving the scientific rigor and quality of the treatment plan.

[0093] For the primary syndrome identified in the target analysis results, the corresponding primary prescription is selected; for secondary syndromes, the corresponding auxiliary prescription is selected. The determination of the primary and auxiliary prescriptions follows the traditional principles of traditional Chinese medicine prescription formulation. The primary prescription targets the primary syndrome and plays a leading role in treatment; the auxiliary prescription targets the secondary syndrome and plays a supporting role in treatment. This prescription structure ensures the integrity and specificity of the prescription, and can comprehensively cover the syndrome characteristics of the patient.

[0094] In preferred embodiments, the selection of the principal and auxiliary prescriptions also considers factors such as the frequency of clinical application of the prescriptions, the stability of therapeutic effects, and the risk of adverse reactions. Classic prescriptions with wide clinical application, definite efficacy, and high safety are given priority as candidates for principal and auxiliary prescriptions, followed by meticulous screening based on specific syndrome characteristics and symptom presentation. For special cases or rare syndromes, innovative prescription suggestions are also provided, referencing expert consensus and the latest research findings.

[0095] When the analysis results include multiple interrelated syndromes, the ratio of the primary and auxiliary prescriptions is adjusted according to the relationships between the syndromes to ensure that the treatment plan matches the patient's overall syndrome characteristics. The interrelationships between syndromes refer to the mutual influence and transformation patterns between different syndromes. For example, some syndromes may be mutually causal, some may be mutually antagonistic, and some may be mutually beneficial. Based on these relationships, the composition and dosage ratio of the primary and auxiliary prescriptions are dynamically adjusted to achieve overall prescription balance.

[0096] For example, in a specific case, the patient simultaneously exhibits two syndromes: "Qi deficiency and blood stasis" and "Liver stagnation and spleen deficiency," with the former being the primary syndrome and the latter a secondary one. These two syndromes have a mutually influential relationship: Qi deficiency can worsen blood stasis, while Liver stagnation can exacerbate spleen deficiency, and spleen deficiency further affects the production of Qi and blood. In this situation, the following prescription combination might be generated:

[0097] Main prescription: Qi-tonifying and blood-activating formula (Astragalus membranaceus, Angelica sinensis, Ligusticum chuanxiong, Paeonia lactiflora, etc.) Auxiliary prescription: Liver-soothing and spleen-strengthening formula (Bupleurum chinense, Atractylodes macrocephala, Poria cocos, Citrus reticulata, etc.) Adjustment of formula ratio: Increase the dosage of Astragalus membranaceus to enhance the qi-tonifying effect, appropriately reduce the dosage of Bupleurum chinense to avoid excessive liver-soothing and damage to the body's vital energy, and increase the dosage of Atractylodes macrocephala and Poria cocos to enhance the spleen-strengthening effect.

[0098] By adjusting the proportions in this way, a coordinated and balanced prescription combination can be formulated for the patient's complex syndrome characteristics. This combination not only treats the primary syndrome but also takes into account the secondary syndromes, while also considering the mutual influence between the syndromes.

[0099] Regarding individualized adjustments, the treatment plan generation module also considers individual characteristics such as the patient's age, gender, physical condition, comorbid symptoms, and history of drug allergies, further adjusting the prescription. For example, for elderly and frail patients, the dosage and potency of medications may be reduced; for patients with poor digestion, spleen-strengthening and digestive-aiding medications may be added; and for patients with a history of specific drug allergies, the relevant medications may be avoided or alternative medications may be used. This individualized adjustment ensures the safety and applicability of the treatment plan, allowing it to best meet the individual needs of patients.

[0100] The Chinese herbal formula dosage-effect database is built upon extensive clinical practice and research data, encompassing evaluations of the effects of various Chinese herbal formulas on different syndrome types. This database is continuously updated and improved, providing reliable data support for the generation of treatment plans.

[0101] In one embodiment of this application, the prescription dosage-effect database adopts a multi-source data fusion architecture, integrating multiple data sources: first, classic prescriptions and their indications recorded in traditional Chinese medicine classics; second, prescription efficacy data verified in modern clinical research; third, prescription usage patterns and effect feedback extracted from real-world treatment data; and fourth, the mechanisms of action and pharmacodynamic data of Chinese herbal components discovered in pharmacological research. This multi-source data fusion architecture ensures the comprehensiveness and scientific rigor of the database content, simultaneously taking into account both traditional experience and modern research findings.

[0102] Database updates and improvements are an ongoing process. New dosage-effect data for prescriptions are collected through multiple channels: firstly, by regularly integrating efficacy data from newly published TCM research literature; and secondly, by collecting data on the application effects of prescriptions in actual treatment through user feedback from the system itself. This new data, after standardization and quality assessment, is integrated into the existing database, continuously enriching and optimizing its content. Furthermore, this application can employ machine learning algorithms to periodically analyze and update the dosage-effect relationships of prescriptions in the database, identifying potential new associations and patterns, thereby improving the accuracy and usability of the database.

[0103] Through this scientific, dynamic, and comprehensive prescription and dosage-effect database, combined with an intelligent treatment plan generation algorithm, this application can provide doctors with highly accurate, theoretically sound, and clinically practical TCM prescription suggestions, effectively improving the standardization and precision of TCM diagnosis and treatment while preserving the characteristics of TCM syndrome differentiation and treatment.

[0104] Step 208 involves transmitting target analysis results, treatment plans, health management guidance, and monitoring feedback between the doctor's and patient's ends, thus forming a health data management cycle.

[0105] The doctor-patient two-way management module, as a crucial component, enables information exchange and collaborative management between doctors and patients. On the doctor's end, multi-dimensional dashboards display the patient's status analysis results and recommended treatment plans, allowing doctors to make informed treatment decisions. On the patient's end, functions such as health data reporting, status monitoring, health education, and behavioral intervention are provided to guide patients in scientific health management.

[0106] Patient feedback and new health data are continuously collected and transmitted back to the system. Based on this, the target analysis module updates its target analysis results, and the treatment plan generation module adjusts its treatment recommendations accordingly. This two-way interaction and data feedback mechanism between doctors and patients forms a complete health data management cycle, enabling dynamic monitoring of patients' health status and continuous optimization of medical plans.

[0107] This application establishes a new paradigm for integrated traditional Chinese and Western medicine diagnosis and treatment based on state-target theory. It collects multidimensional health data, identifies state-target features, determines treatment plans, and establishes a two-way interactive mechanism between doctors and patients. Compared with traditional Chinese medicine diagnosis and treatment systems, this application has the following advantages: First, it standardizes and digitizes the theory of TCM syndrome differentiation, improving the standardization and repeatability of TCM diagnosis and treatment; second, it establishes a correlation mechanism between objective Western medicine indicators and TCM syndrome differentiation results, enhancing the scientific nature 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; and fourth, it forms a precision diagnosis and treatment model supported by big data, promoting the modernization of traditional Chinese medicine.

[0108] In another exemplary embodiment, this application may further include a group health management module for implementing three-tiered health management at the individual-group-region level. This module classifies and clusters the state-target analysis results of multiple patients to identify disease progression patterns and treatment response characteristics of similar patient groups, providing data support and decision-making basis for regional health management and disease prevention and control.

[0109] The various modules in the aforementioned targeted physician clinical and patient life management system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a 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 corresponding operations of each module.

[0110] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. The display unit is used to create a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0111] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0112] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0113] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

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

[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 used for analysis, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0116] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this 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), magnetic 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 take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0117] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this application.

[0118] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A unique physician clinical and patient life management system, characterized by: include: The patient data acquisition module is used to collect patients' health data; The state-target analysis module is used to identify state-target features in the health data and generate state-target analysis results. The treatment plan generation module is used to determine the treatment plan based on the target analysis results and the dose-effect relationship of the prescription. The doctor-patient two-way management module is used to transmit the target analysis results, treatment plans, health management guidance and monitoring feedback between the doctor's end and the patient's end, forming a health data management loop; The state-target analysis module is used to identify state-target features in the health data, including: The health data is preprocessed to obtain preprocessed health data; Extract state target feature parameters from the preprocessed health data; Generate a state target feature vector based on the state target feature parameters; The state target analysis results are formed by comparing the state target feature vector with the standard state target model; Specifically, the state-target analysis module identifies syndrome features based on the representation intensity of the state-target feature parameters across different physiological dimensions. When the representation intensity of the state-target feature parameter in a certain physiological dimension is ranked first, the state-target analysis module determines this physiological dimension as the primary basis for syndrome identification. When the difference in representation intensity of the state-target feature parameters across multiple physiological dimensions is less than the standard deviation of representation intensity, the state-target analysis module determines the syndrome combination in the state-target analysis results based on the interaction relationship between each physiological dimension. The treatment plan generation module determines the treatment plan in the following ways: Obtain prescription data and corresponding efficacy data from the prescription dose-effect database; Prescription combinations are matched based on the syndrome characteristics in the target analysis results and the efficacy data. The prescription combination is adjusted according to the individual characteristics of the patient; Generate the aforementioned treatment plan; Specifically, the treatment plan generation module selects the corresponding primary prescription for the primary syndrome type identified in the state-target analysis results and the corresponding auxiliary prescription for the secondary syndrome type. When the state-target analysis results contain multiple mutually influential syndrome types, the treatment plan generation module adjusts the ratio of the primary prescription to the auxiliary prescription according to the interaction relationship between the syndrome types, so that the effect of the treatment plan matches the syndrome type characteristics in the state-target analysis results.

2. The physician clinical and patient life management system with state-targeting characteristics as described in claim 1, characterized in that: The health data collected by the patient data acquisition module includes lifestyle data, physiological indicator data, and clinical test data. The lifestyle data is obtained through a patient-side application, the physiological indicator data is obtained through health monitoring devices, and the clinical test data is obtained through medical testing.

3. The physician clinical and patient life management system with state-targeting characteristics as described in claim 2, characterized in that: The state target analysis module extracts the state target feature parameters from the preprocessed health data in the following ways: Extract basic physiological indicators; The basic physiological indicators were converted into parameters according to traditional Chinese medicine theory. The parameters of the TCM theory are mapped to pathological characteristics; The target analysis module establishes a correlation between Western medicine objective indicators and TCM syndrome differentiation results by establishing the correspondence between the pathological characteristics and the TCM theoretical parameters.

4. The physician clinical and patient life management system with state-targeting characteristics as described in claim 3, characterized in that: The operation of the doctor-patient two-way management module includes: The target analysis results and the treatment plan are displayed on the doctor's end. Provide health management functions on the patient's end; Transmit physician decisions and patient feedback to the target analysis module; The state-target analysis module adjusts the state-target analysis results based on the doctor's decision and the patient's feedback.

5. The physician clinical and patient life management system with state-targeting characteristics as described in claim 4, characterized in that: It also includes a group health management module, the processing of which includes: Classify the state-target analysis results from multiple patients; Analyze the disease patterns and treatment responses of similar patients; Develop group health management strategies; The group health management module establishes a three-tiered health management structure encompassing individuals, groups, and regions.

6. A method for managing physician clinical and patient life with state-target characteristics, based on the state-target characteristic physician clinical and patient life management system according to any one of claims 1 to 5, characterized in that: include, Collect patients' health data; The health data is subjected to state target feature identification to generate state target analysis results; Based on the target analysis results and the dose-effect relationship of the prescription, a treatment plan is determined; The transmission of the target analysis results, treatment plans, health management guidance, and monitoring feedback between the doctor's and patient's ends constitutes a health data management loop.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the physician clinical and patient life management system with the characteristics of any one of claims 1 to 5.

8. 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 physician clinical and patient life management system with the characteristics of any one of claims 1 to 5.

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