Intelligent subject evaluation method and device based on multi-dimensional data

Abstract

The invention relates to the technical field of medical treatment, and discloses an intelligent subject evaluation method based on multi-dimensional data, which comprises the following steps: acquiring the multi-dimensional data corresponding to a subject, inputting the multi-dimensional data into a pre-trained evaluation model, and acquiring an analysis report of the subject participating in clinical research, the multi-dimensional data comprises clinical symptoms, traditional Chinese and western medicine diagnosis, medication information and social background of the subject, and the analysis report comprises accuracy, compliance and risk; based on the analysis report, determining a corresponding group-entering suggestion and a treatment research scheme for each subject, the treatment research scheme including drug selection, dose adjustment and a follow-up plan; in the clinical research process, the latest research data are continuously obtained, and the analysis report and the treatment research scheme of the subject are adjusted based on the latest research data. The quality and effectiveness of clinical research can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of medicine, and in particular to a method for intelligent assessment of subjects based on multi-dimensional data. Background Art

[0002] In recent years, with the rapid development of the medical and health field, especially in clinical research, the evaluation of subjects has increasingly emphasized accuracy and individualization. However, traditional evaluation methods are usually limited to single-dimensional data collection and analysis, which is inadequate when faced with complex diseases and individual differences.

[0003] Specifically, traditional evaluation methods mainly focus on medical information such as the subjects' clinical symptoms and diagnostic results, while ignoring other important aspects such as the subjects' social background and living habits. This single-perspective evaluation method is difficult to fully reflect the actual situation of the subjects, thus affecting the quality and effectiveness of clinical research.

[0004] From the above, we can see that how to improve the quality and effectiveness of clinical research remains to be resolved. Summary of the invention

[0005] In order to improve the quality and effectiveness of clinical research, the present application provides a method and device for intelligent assessment of subjects based on multi-dimensional data.

[0006] In the first aspect, the present application provides a method for intelligent assessment of subjects based on multi-dimensional data, which adopts the following technical solutions:

[0007] A method for intelligent assessment of subjects based on multidimensional data, comprising: obtaining multidimensional data corresponding to the subject, inputting the multidimensional data into a pre-trained assessment model, obtaining an analysis report on the subject's participation in a clinical study, wherein the multidimensional data includes the subject's clinical symptoms, Chinese and Western medicine diagnosis, medication information, and social background, and the analysis report includes accuracy, compliance, and risk; determining a corresponding enrollment recommendation and treatment study plan for each subject based on the analysis report, wherein the treatment study plan includes drug selection, dosage adjustment, and follow-up plan; during the clinical study process, continuously obtaining the latest research data, and adjusting the analysis report and treatment study plan for the subject based on the latest research data.

[0008] By adopting the above technical solutions, the intelligent subject assessment method based on multi-dimensional data can achieve a comprehensive assessment of the subjects and provide personalized enrollment recommendations and treatment research plans based on the assessment results. This method not only improves the accuracy and compliance of clinical research, but also reduces potential risks, thereby significantly improving the quality and effectiveness of clinical research.

[0009] Optionally, in the process of obtaining an analysis report on the subject's participation in a clinical study, the method also includes: obtaining the subject's TCM-related information, the TCM-related information including complexion, tongue condition, pulse condition, mental state, eating habits, sleep quality, and bowel movement; identifying the subject's constitution based on the TCM-related information to determine the corresponding target constitution type; inputting the target constitution type and the multi-dimensional data into an evaluation model to obtain the subject's corresponding analysis report.

[0010] By adopting the above technical solution, by adding relevant information of traditional Chinese medicine in the process of obtaining analysis reports of subjects participating in clinical research, and performing physical constitution identification based on this information, a comprehensive assessment of the health status of the subjects can be achieved.

[0011] Optionally, the evaluation model also includes a statistical module, and the method also includes: retrieving clinical change data corresponding to the subject, wherein the clinical change data includes the subject's quality of life, psychological state, and social support; unifying and standardizing the format of the clinical change data, and selecting exit-related features related to the exit rate; inputting the exit-related features and using the statistical module to calculate the subject's exit rate.

[0012] By adopting the above technical solution, by introducing a statistical module into the evaluation model, and retrieving the clinical change data of the subjects (including quality of life, psychological state, social support, etc.), unifying the format and standardizing it, selecting features related to the withdrawal rate, and calculating the withdrawal rate of the subjects, a comprehensive evaluation of the clinical changes of the subjects can be achieved. This method not only improves the accuracy of the withdrawal rate prediction, enhances the effectiveness of personalized intervention measures, but also improves the efficiency and reliability of clinical research.

[0013] Optionally, the method also includes: constructing a real-time feedback platform, and the real-time feedback platform is linked to the subject's mobile device; the real-time feedback platform regularly sends reminder information to the subject's mobile device, and obtains the latest health status uploaded by the subject based on the subject's mobile device, wherein the latest health status includes symptom changes, medication status, eating habits, and exercise amount; based on the latest health status, the subject's treatment research plan is adjusted, and the adjusted treatment research plan is sent to the subject's mobile device.

[0014] By adopting the above technical solutions, building a real-time feedback platform and linking the subjects' mobile devices, it is possible to monitor the health status of the subjects in real time and dynamically adjust the treatment plan. This method not only improves the real-time and accuracy of the data, enhances the compliance and interactivity of the subjects, but also enables timely adjustment of the treatment plan and improves research efficiency and safety. In addition, the real-time feedback platform also improves the user experience and increases the subjects' participation and satisfaction with the research.

[0015] Optionally, in the process of determining the corresponding enrollment recommendation and treatment research plan for each subject based on the analysis report, the method also includes: sending the analysis report to multiple doctor user terminals to obtain treatment research feedback data corresponding to each doctor user terminal; scoring each treatment research feedback data to obtain corresponding score data, and selecting a target treatment research feedback data set based on the score data corresponding to each treatment research feedback data; and determining the corresponding enrollment recommendation and treatment research plan based on the target treatment research feedback data set.

[0016] By adopting the above technical solution, by sending the analysis report to multiple doctor users and determining the final enrollment recommendation and treatment research plan based on the doctor's feedback data, it is possible to gather the opinions of multiple experts and improve the rationality and personalization of the treatment plan. This method not only improves the scientificity and safety of the treatment plan, but also promotes multidisciplinary collaboration and enhances the transparency and credibility of clinical research.

[0017] Optionally, the method also includes: obtaining physiological data of the subject, the physiological data including heart rate, respiratory rate, skin conductance level, and muscle tension; displaying the physiological data to the subject in a visual form, and retrieving the target physical fitness type corresponding to the subject; and generating a feedback training plan for the subject based on the physiological data and the target physical fitness type.

[0018] By adopting the above technical solution, by obtaining the subject's physiological data and displaying it to the subject in a visual form, and then generating a feedback training plan based on the subject's target physical type, it is possible to achieve real-time monitoring of the subject's physiological state and provide personalized training suggestions. This method not only improves the subject's self-management ability and treatment effect, enhances the compliance and pertinence of the treatment plan, but also ensures the effectiveness of the treatment plan through dynamic adjustment.

[0019] Optionally, the method also includes: obtaining the genetic data of the subject, and identifying the genetic variation of the subject based on the genetic data; retrieving a known genotype-phenotype association database, and parsing the genetic characteristics of the subject based on the genetic data and the genotype-phenotype association database; and recommending the most suitable drug and drug dosage for the subject based on the genetic variation of the subject and the genetic data.

[0020] By adopting the above technical solution, by obtaining the genetic data of the subject and combining it with the known genotype-phenotype association database to analyze the genetic characteristics of the subject, and then recommending the most suitable drug and drug dosage, it is possible to achieve accurate drug selection and personalized dosage adjustment for the subject. This method not only improves the safety and effectiveness of treatment, but also reduces the risk of adverse drug reactions.

[0021] In the second aspect, the present application provides a method for intelligent assessment of subjects based on multi-dimensional data, which adopts the following technical solutions:

[0022] A subject intelligent assessment device based on multi-dimensional data, comprising:

[0023] An analysis report acquisition module, which acquires multi-dimensional data corresponding to the subject, and inputs the multi-dimensional data into a pre-trained evaluation model to obtain an analysis report of the subject's participation in the clinical study, wherein the multi-dimensional data includes the subject's clinical symptoms, Chinese and Western medicine diagnosis, medication information, and social background, and the analysis report includes accuracy, compliance, and risk;

[0024] A treatment research program determination module is used to determine corresponding enrollment recommendations and treatment research programs for each subject based on the analysis report, wherein the treatment research program includes drug selection, dosage adjustment, and follow-up plan;

[0025] The adjustment module continuously obtains the latest research data during the clinical research process, and uses the latest research data to adjust the analysis report and treatment research plan for the subjects.

[0026] In a third aspect, the present application provides a method for intelligent assessment of subjects based on multi-dimensional data, which adopts the following technical solutions:

[0027] A method for intelligent assessment of a subject based on multi-dimensional data comprises a processor in which a program of any one of the above-mentioned methods for intelligent assessment of a subject based on multi-dimensional data is run.

[0028] In a fourth aspect, the present application provides a storage medium, which adopts the following technical solution:

[0029] A storage medium storing a program of any one of the above-mentioned methods for intelligently evaluating subjects based on multi-dimensional data.

[0030] In summary, this application includes at least one of the following beneficial technical effects: The intelligent subject assessment method based on multi-dimensional data can achieve a comprehensive assessment of the subject, and provide personalized enrollment recommendations and treatment research plans based on the assessment results. This method not only improves the accuracy and compliance of clinical research and reduces potential risks, but also significantly improves the quality and effectiveness of clinical research through real-time monitoring, multi-expert opinion collection, physiological state feedback training and genetic data application, and enhances the subject's participation and treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The present invention is a flowchart of a method for intelligently evaluating a subject based on multi-dimensional data according to an exemplary embodiment.

[0032] Figure 2 It is a structural block diagram of a device for intelligently evaluating subjects based on multi-dimensional data according to an exemplary embodiment. DETAILED DESCRIPTION

[0033] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0034] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0035] The present application embodiment discloses a method for intelligent assessment of subjects based on multi-dimensional data, referring to Figure 1 , including the following steps:

[0036] S100, obtaining multi-dimensional data corresponding to the subject, inputting the multi-dimensional data into a pre-trained evaluation model, and obtaining an analysis report on the subject's participation in the clinical study.

[0037] Among them, multi-dimensional data include but are not limited to the subjects' clinical symptoms, Chinese and Western medicine diagnoses, medication information, and social background; data collection: collecting multi-dimensional data of subjects from various channels; clinical symptoms: such as pain, fever, cough, etc.; Chinese and Western medicine diagnoses: the diagnosis results of Western medicine and the four diagnostic information of Chinese medicine: looking, smelling, asking, and touching; medication information: currently used drugs, dosage, medication history, etc.; social background: including the subjects' occupation, education level, family support, etc.; the collected different types of multi-dimensional data are integrated to form a comprehensive subject information file.

[0038] The analysis report includes accuracy, compliance and risk; accuracy: assessing whether the subject is suitable for participating in a specific clinical study; compliance: predicting the subject's degree of compliance with the research plan; risk: identifying risk factors that the subject may encounter during the study.

[0039] Through the evaluation model, we can ensure that only eligible subjects are included in the study, improve the accuracy of the study, and predict the compliance of the subjects in advance, which will help to develop more effective intervention measures. At the same time, potential risks can be identified and preventive measures can be taken in advance to ensure the safety of the subjects.

[0040] In the embodiment of the present application, the specific training process of the evaluation model is as follows:

[0041] 1. Data collection: Collect a large amount of subject data from various sources, including multi-dimensional data such as clinical symptoms, Chinese and Western medicine diagnoses, medication information, social background, etc.; clean the collected data, remove invalid or erroneous data items, fill in missing values, and ensure data quality; annotate each subject's data set with their performance results in the clinical study, such as whether the study was completed, whether there were adverse reactions, etc., as the target variable for model training; select and construct useful features based on domain knowledge and data exploratory analysis (EDA), such as converting text descriptions into numerical representations, or creating new derived features.

[0042] 2. Data partitioning: Divide the dataset into training set, validation set and test set, usually in a ratio of 70% training set, 15% validation set and 15% test set, to ensure that the data distribution among the training set, validation set and test set is as consistent as possible to avoid data bias affecting the generalization ability of the model.

[0043] 3. Model selection Choose a suitable machine learning model or deep learning framework based on the nature of the problem. Common models include logistic regression, random forest, support vector machine (SVM), neural network, etc. Initialize parameters such as weights and biases according to the model type.

[0044] 4. Model Training

[0045] Loss function definition: Define the loss function to measure the gap between the model's predicted value and the true value, select an appropriate optimization algorithm to minimize the loss function, such as gradient descent, use the training set data for multiple iterations of training, update the model parameters after each iteration until the loss function converges or reaches the predetermined training rounds, and save the trained model for subsequent use in practical applications.

[0046] S110, determining corresponding enrollment recommendations and treatment research plans for each subject based on the analysis report.

[0047] Among them, the inclusion recommendation: based on the results of the analysis report, decide whether to recommend the subject to be included and give the reasons; the treatment research plan formulates a personalized treatment research plan for the enrolled subjects, including: drug selection: select the most appropriate drug according to the specific situation of the subject; dose adjustment: adjust the drug dose according to the individual differences of the subject; follow-up plan: formulate a detailed follow-up schedule to ensure the continuity and integrity of the study.

[0048] Through enrollment recommendations and treatment research plans, we can ensure that each subject can receive the treatment plan that best suits him or her, and also ensure the actual feasibility and effectiveness of the treatment plan.

[0049] S120: During clinical research, continuously obtain the latest research data and adjust the analysis report and treatment research plan for the subjects based on the latest research data.

[0050] During the research process, the latest data of the subjects are continuously collected, including new clinical symptoms, medication status, life changes, etc., the latest data is re-entered into the evaluation model, the analysis report is regenerated, and then the treatment research plan of the subjects is dynamically adjusted according to the latest analysis report to ensure that the treatment plan is always optimal.

[0051] By executing steps S100 to S120, the intelligent subject assessment method based on multi-dimensional data can achieve a comprehensive assessment of the subjects and provide personalized enrollment recommendations and treatment research plans based on the assessment results. It also ensures that the treatment plan can be adjusted in time according to the latest situation of the subjects through continuous data collection and dynamic adjustment, thereby improving the treatment effect and significantly improving the quality and effectiveness of clinical research.

[0052] For the evaluation model used in this application, when generating the analysis report, it is also related to the physical constitution of the subject. The specific process includes:

[0053] S101, obtaining the subject's TCM related information.

[0054] Among them, TCM-related information includes complexion, tongue, pulse, mental state, eating habits, sleep quality, and bowel movements; complexion: observe changes in the subject's complexion, such as pale, ruddy, dull, etc.; tongue: check the subject's tongue quality, the color and shape of the tongue coating, etc.; pulse: record the subject's pulse characteristics through TCM pulse diagnosis, such as floating, sinking, slow, and rapid; mental state: understand the subject's mental outlook, such as emotional stability, anxiety, depression, etc.; eating habits: record the subject's dietary preferences, such as preference for cold or hot foods, etc.; sleep quality: assess the subject's sleep conditions, such as difficulty falling asleep, number of times waking up at night, etc.; bowel movements: understand the subject's bowel habits, such as constipation, diarrhea, etc.

[0055] S102, identifying the physical constitution of the subject based on the relevant information of traditional Chinese medicine, and determining the corresponding target physical constitution type.

[0056] Among them, according to the collected traditional Chinese medicine related information and referring to the standards such as "Classification and Determination of Traditional Chinese Medicine Constitution", the subjects can be classified into one of the nine basic constitution types, such as balanced constitution, qi deficiency constitution, yang deficiency constitution, yin deficiency constitution, phlegm-damp constitution, damp-heat constitution, blood stasis constitution, qi stagnation constitution, and special constitution; according to the characteristics of different constitution types, targeted preventive health care suggestions can be provided to the subjects.

[0057] S103, input the target physical type and multi-dimensional data into the evaluation model to obtain the corresponding analysis report of the subject.

[0058] Among them, the integrated data is input into a pre-trained evaluation model to generate an analysis report on the subjects' participation in clinical research; by combining information related to traditional Chinese medicine with multi-dimensional data, the evaluation model can more accurately assess the subjects' health status and suitability for participating in clinical research; it also takes into account the subjects' physical constitution type, and the formulated treatment plan is more in line with their individual needs, thereby improving the subjects' compliance with the treatment plan.

[0059] In an embodiment of the present application, the evaluation model further includes a statistical module, and the method further includes:

[0060] S104, retrieve the clinical change data corresponding to the subject.

[0061] Among them, the data on clinical changes of the subjects throughout the study process were collected, mainly including: quality of life: the quality of life of the subjects was assessed through standardized questionnaires, including physical function, psychological function, social function and other aspects; psychological state: the mental health status of the subjects was assessed through psychological scales, including symptoms such as depression and anxiety; social support: the degree of social support obtained by the subjects was assessed through social support scales, including support from family, friends, community and other aspects.

[0062] S105, unifying and standardizing the format of the clinical change data, and selecting exit-related features that are related to the exit rate.

[0063] Among them, data from different sources are converted into a unified format to facilitate subsequent data processing and analysis; the data are standardized to eliminate differences between different scales or measurement units to ensure that the data are compared on the same scale.

[0064] Secondly, the accuracy of the dropout rate can be improved by finding out the features that are highly correlated with the dropout rate through correlation analysis and using feature importance evaluation methods (such as feature importance of random forest) to determine which features have the greatest impact on the dropout rate.

[0065] S106, inputting the exit-related features and using the statistical module to calculate the exit rate of the subject.

[0066] Among them, appropriate statistical modules, such as logistic regression, Cox proportional hazard model, etc., are selected to calculate the dropout rate of subjects. By calculating the dropout rate of subjects through statistical modules, it is possible to more accurately predict which subjects are more likely to drop out of the study midway, so that intervention measures can be taken in time to improve the compliance and success rate of the study.

[0067] During the research process, adjustments need to be made based on the subjects’ feedback. To this end, the following steps are also performed:

[0068] S121, constructing a real-time feedback platform, and the real-time feedback platform is connected to the subject's mobile device.

[0069] Among them, design a real-time feedback platform that can receive and process data in real time. The real-time feedback platform must have the ability to transmit, store and process data; mobile device side: develop an application (App) suitable for the subject's mobile device, which can communicate with the real-time feedback platform; user interface design of the application on the mobile device side: design a user-friendly interface so that the subjects can easily record and upload health status data.

[0070] Through the real-time feedback platform that has been constructed, subjects can record and upload data at any time through mobile devices, without relying on traditional paper records or regular visits to medical institutions. The link between the subjects' mobile devices and the real-time feedback platform ensures real-time upload of data, allowing researchers to obtain the latest health information in a timely manner.

[0071] S122, the real-time feedback platform regularly sends reminder information to the subject's mobile device, and obtains the latest health status uploaded by the subject based on the subject's mobile device.

[0072] Among them, regular reminders can help subjects develop the habit of recording and uploading data on time, and can ensure that subjects can submit data on time, avoid data loss, and improve data integrity and research effectiveness.

[0073] In addition, by collecting the latest health status uploaded by the subjects on mobile devices, including changes in symptoms, medication, eating habits, exercise volume, etc., it is possible to monitor changes in the subjects' health status in real time and detect potential problems in a timely manner.

[0074] S123, adjusting the treatment research plan of the subject based on the latest health status, and sending the adjusted treatment research plan to the subject's mobile device.

[0075] The latest health status data is integrated with other multi-dimensional data (such as clinical symptoms, medication information, etc.) and analyzed through a pre-trained evaluation model, so that the subject's treatment research plan can be dynamically adjusted. The adjusted treatment research plan is then sent to the subject via a mobile device to ensure that the subject can understand and implement the new treatment plan in a timely manner.

[0076] By executing steps S121 to S123, by building a real-time feedback platform and linking the subjects' mobile devices, real-time monitoring of the subjects' health status and dynamic adjustment of treatment plans can be achieved. This not only improves the real-time and accuracy of the data, enhances the subjects' compliance and interactivity, but also enables timely adjustment of treatment plans to improve research efficiency and safety.

[0077] In the process of determining the corresponding enrollment recommendation and treatment study plan for each subject based on the analysis report, the method further performs the following process:

[0078] S124, sending the analysis report to multiple doctor user terminals to obtain treatment research feedback data corresponding to each doctor user terminal.

[0079] Among them, the analysis report is sent to multiple doctors participating in the research through a specially designed doctor-side application or platform. By sending the analysis report to multiple doctors, the opinions of different experts can be gathered to ensure the comprehensiveness and scientificity of the treatment plan. The feedback from multiple doctors can make up for the limitations of a single doctor's point of view and improve the accuracy and reliability of decision-making.

[0080] Then, after receiving the analysis report, the doctor will make treatment research recommendations for the subject based on his or her professional knowledge and experience. The doctor submits the treatment research recommendations as feedback data through the user-side application or platform and records them in a structured manner. The doctor's feedback data should include specific treatment recommendations, medication regimens, follow-up plans, etc.

[0081] S125, scoring each treatment research feedback data to obtain corresponding score data, and selecting a target treatment research feedback data set based on the score data corresponding to each treatment research feedback data.

[0082] Among them, by formulating a set of scoring criteria, the rationality and feasibility of the treatment research feedback data proposed by each doctor are evaluated. According to the scoring criteria, each feedback data is scored to generate corresponding score data; and through the scoring mechanism, the suggestions of different doctors can be objectively evaluated, ensuring fair evaluation of each suggestion and avoiding the influence of personal bias on decision-making.

[0083] All feedback data are sorted according to the scoring results, and the feedback data with the highest scores are selected to form the target treatment research feedback data set. Comprehensive analysis of multiple high-scoring plans can further optimize the final treatment recommendations.

[0084] S126, determining corresponding enrollment recommendations and treatment research plans based on the target treatment research feedback data set.

[0085] Among them, the final enrollment recommendation and treatment research plan are formulated based on the selected target treatment research feedback data set. The final plan is based on the professional opinions of multiple doctors, which is more personalized and can better adapt to the specific circumstances of the subjects. By gathering opinions from multiple experts, potential risks can be identified and prevented, thereby improving the safety of treatment.

[0086] In an embodiment of the present application, the method further includes:

[0087] S127, obtaining physiological data of the subject.

[0088] Physiological data types include: heart rate: records the subject's heart rate; respiratory rate: records the subject's breathing rate; skin conductance level: records the subject's skin conductance value, reflecting the level of activity of their autonomic nervous system; muscle tension: records the subject's muscle tension; through a variety of physiological parameters, the subject's physiological state can be comprehensively evaluated to provide a basis for subsequent personalized training.

[0089] S128, displaying the physiological data to the subject in a visual form, and retrieving the target physical type corresponding to the subject.

[0090] The processed physiological data are displayed to the subjects in the form of charts, curves, etc. The visual display allows the subjects to understand their physiological state more intuitively, improve their ability to self-manage their health, and determine the subject's physical constitution type based on previous traditional Chinese medicine related information.

[0091] S129, generating a feedback training plan for the subject based on the physiological data and the target physical fitness type.

[0092] Among them, the physiological data is combined with the physical type information for comprehensive analysis, and a personalized feedback training plan is generated for the subjects based on the corresponding analysis results, including but not limited to: relaxation exercises: such as deep breathing, meditation, etc., to help reduce heart rate and muscle tension; breathing training: improve breathing rate through specific breathing techniques; exercise suggestions: recommend suitable exercise types and intensities based on physical type; lifestyle adjustments: provide suggestions on diet, work and rest, etc.

[0093] By executing steps S127 to S129, the subject's physiological data is obtained, and a feedback training plan is generated in combination with his or her target physical condition type. This allows real-time monitoring of the subject's physiological state and provides personalized training suggestions. This not only improves the subject's self-management ability and treatment effect, enhances the compliance and pertinence of the treatment plan, but also ensures the effectiveness of the treatment plan through dynamic adjustment.

[0094] In addition, the method includes:

[0095] S111, obtaining genetic data of the subject, and identifying genetic variation of the subject based on the genetic data.

[0096] Among them, a DNA sample is obtained by collecting biological samples (such as blood, saliva, etc.) from the subject, and then the DNA sample is sequenced using high-throughput sequencing technology (such as whole genome sequencing, exome sequencing or targeted sequencing) to obtain the genetic information of the subject.

[0097] In addition, bioinformatics tools and algorithms are used to analyze sequencing data to identify genetic variations in subjects, including single nucleotide polymorphisms (SNPs), copy number variations (CNVs), insertions / deletions (InDels), etc., and the identified genetic variations are annotated to explain their possible functional significance, such as coding region variations, regulatory region variations, etc.

[0098] S112, accessing a known genotype-phenotype association database, and analyzing the genetic characteristics of the subject based on the genetic data and the genotype-phenotype association database.

[0099] The identified gene variation information is compared with the data in the database to find the relevant phenotypic information. Based on the comparison results, the genetic characteristics of the subjects are analyzed, including drug metabolism ability, drug responsiveness, disease susceptibility, etc.; and by comparing with known databases, the genetic characteristics of the subjects can be analyzed to understand their potential response to drugs.

[0100] S113, recommending the most suitable drug and drug dosage for the subject based on the genetic variation and genetic data of the subject.

[0101] Among them, the most suitable drug is recommended based on the genetic variation of the subject. For example, if the subject carries a variation in a drug-metabolizing enzyme, a drug with a different metabolic pathway is selected; then the drug dosage is adjusted based on the genetic data. For example, if the subject metabolizes a certain drug slowly, the dosage is reduced; if the metabolism is fast, the dosage is appropriately increased; finally, the recommended drug and dosage information is integrated into a specific treatment plan and communicated to the subject and his or her attending physician through appropriate channels.

[0102] By executing steps S111 to S113 and combining with the known genotype-phenotype association database to analyze the genetic characteristics of the subject, and then recommending the most suitable drug and drug dosage, accurate drug selection and personalized dosage adjustment for the subject can be achieved, which not only improves the safety and effectiveness of treatment, but also reduces the risk of adverse drug reactions.

[0103] The present application also discloses a device for intelligently evaluating a subject based on multi-dimensional data. Figure 2 , including but not limited to:

[0104] The analysis report acquisition module 200 acquires multi-dimensional data corresponding to the subject, and inputs the multi-dimensional data into a pre-trained evaluation model to acquire an analysis report of the subject participating in the clinical study. The multi-dimensional data includes the subject's clinical symptoms, Chinese and Western medicine diagnosis, medication information, and social background. The analysis report includes accuracy, compliance, and risk.

[0105] A treatment research program determination module 210 is used to determine corresponding enrollment recommendations and treatment research programs for each subject based on the analysis report, wherein the treatment research program includes drug selection, dosage adjustment, and follow-up plan;

[0106] Adjustment module 220, during the clinical research process, continuously obtains the latest research data and uses the latest research data to adjust the analysis report and treatment research plan for the subject

[0107] Optionally, including but not limited to:

[0108] A TCM-related information acquisition module, which is used to obtain TCM-related information of the subject, including complexion, tongue condition, pulse condition, mental state, eating habits, sleep quality, and defecation conditions;

[0109] A target constitution type determination module is used to identify the constitution of the subject based on relevant information of traditional Chinese medicine to determine the corresponding target constitution type;

[0110] The input module is used to input the target physical type and multi-dimensional data into the evaluation model to obtain the corresponding analysis report of the subject.

[0111] Optionally, including but not limited to:

[0112] A clinical change data retrieval module is used to retrieve clinical change data corresponding to the subject, wherein the clinical change data includes the subject's quality of life, psychological state, and social support;

[0113] The exit-related feature selection module unifies and standardizes the format of clinical change data to select exit-related features related to the exit rate;

[0114] The dropout rate calculation module is used to input dropout-related features and use the statistical module to calculate the dropout rate of the subject.

[0115] Optionally, including but not limited to:

[0116] A platform building module is used to build a real-time feedback platform, and the real-time feedback platform is connected to the subject's mobile device;

[0117] The latest health status acquisition module, the real-time feedback platform regularly sends reminder information to the subject's mobile device, based on the subject's mobile device to obtain the subject's latest health status uploaded, where the latest health status includes symptom changes, medication status, eating habits, and exercise volume;

[0118] The sending module adjusts the treatment research plan of the subject based on the latest health status and is used to send the adjusted treatment research plan to the mobile device of the subject.

[0119] Optionally, including but not limited to:

[0120] The latest health status acquisition module sends the analysis report to multiple doctor user terminals to obtain the corresponding treatment research feedback data of each doctor user terminal;

[0121] A score data acquisition module is used to score each treatment research feedback data to obtain corresponding score data, and select a target treatment research feedback data set based on the score data corresponding to each treatment research feedback data;

[0122] The module for determining the enrollment recommendation and treatment study plan is used to determine the corresponding enrollment recommendation and treatment study plan based on the target treatment study feedback data set.

[0123] Optionally, including but not limited to:

[0124] A physiological data acquisition module is used to acquire the subject's physiological data, including heart rate, respiratory rate, skin conductance level, and muscle tension;

[0125] A target physical type retrieval module displays physiological data to the subject in a visual form, and is used to retrieve the target physical type corresponding to the subject;

[0126] The feedback training plan generation module is used to generate a feedback training plan for the subject based on the physiological data and the target physical type.

[0127] Optionally, including but not limited to:

[0128] A genetic data acquisition module, used to acquire the genetic data of the subject and identify the genetic variation of the subject based on the genetic data;

[0129] The genetic characteristics analysis module retrieves the known genotype-phenotype association database and analyzes the genetic characteristics of the subject based on the genetic data and the genotype-phenotype association database;

[0130] The recommendation module is used to recommend the most suitable drugs and drug dosages for the subjects based on the genetic variations and genetic data of the subjects.

[0131] An embodiment of the present application further discloses a method for intelligent assessment of a subject based on multi-dimensional data, comprising a processor, wherein a program of any one of the above-mentioned methods for intelligent assessment of a subject based on multi-dimensional data is running on the processor.

[0132] The embodiment of the present application also discloses a storage medium storing a program of any one of the above-mentioned methods for intelligent assessment of subjects based on multi-dimensional data.

[0133] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for intelligent assessment of subjects based on multi-dimensional data, characterized in that: include: Obtain multi-dimensional data corresponding to the subject, input the multi-dimensional data into a pre-trained evaluation model, and obtain an analysis report on the subject's participation in the clinical study, wherein the multi-dimensional data includes the subject's clinical symptoms, Chinese and Western medicine diagnosis, medication information, and social background, and the analysis report includes accuracy, compliance, and risk; Determine corresponding enrollment recommendations and treatment research plans for each subject based on the analysis report, wherein the treatment research plan includes drug selection, dose adjustment, and follow-up plan; During the clinical research process, the latest research data is continuously obtained, and the analysis report and treatment research plan for the subjects are adjusted based on the latest research data.

2. The subject intelligent assessment method based on multi-dimensional data according to claim 1, characterized in that: In the process of obtaining the analysis report of the subject participating in the clinical study, the method further includes: Obtaining TCM-related information of the subject, including complexion, tongue condition, pulse condition, mental state, eating habits, sleep quality, and bowel movement; Identify the subject's constitution based on the TCM related information to determine the corresponding target constitution type; The target constitution type and the multi-dimensional data are input into the evaluation model to obtain an analysis report corresponding to the subject.

3. The subject intelligent assessment method based on multi-dimensional data according to claim 1, characterized in that: The evaluation model also includes a statistical module, and the method further includes: Retrieving the clinical change data corresponding to the subject, wherein the clinical change data includes the subject's quality of life, psychological state, and social support; The clinical change data are formatted and standardized, and exit-related features related to the exit rate are selected; The dropout-related features are input and the dropout rate of the subjects is calculated using the statistics module.

4. The subject intelligent assessment method based on multi-dimensional data according to claim 1, characterized in that: The method also includes: Building a real-time feedback platform, and the real-time feedback platform is connected to the subject's mobile device; The real-time feedback platform regularly sends reminder information to the subject's mobile device, and obtains the subject's latest health status uploaded by the subject based on the subject's mobile device. The latest health status includes symptom changes, medication status, eating habits, and exercise volume; The treatment research plan of the subject is adjusted based on the latest health status, and the adjusted treatment research plan is sent to the mobile device of the subject.

5. The subject intelligent assessment method based on multi-dimensional data according to claim 1, characterized in that: In the process of determining a corresponding enrollment recommendation and treatment research plan for each subject based on the analysis report, the method further includes: Sending the analysis report to multiple doctor user terminals to obtain treatment research feedback data corresponding to each doctor user terminal; Scoring each treatment research feedback data to obtain corresponding score data, and selecting a target treatment research feedback data set based on the score data corresponding to each treatment research feedback data; Determine the corresponding enrollment recommendations and treatment research plans based on the target treatment research feedback data set.

6. The subject intelligent assessment method based on multi-dimensional data according to claim 2, characterized in that: The method also includes: Obtaining the subject's physiological data, including heart rate, respiratory rate, skin conductance level, and muscle tension; Displaying the physiological data to the subject in a visual form, and retrieving the target physical type corresponding to the subject; A feedback training plan is generated for the subject based on the physiological data and the target physical fitness type.

7. The subject intelligent assessment method based on multi-dimensional data according to claim 1, characterized in that: The method also includes: Obtaining genetic data of the subject, and identifying genetic variation of the subject based on the genetic data; Retrieving a known genotype-phenotype association database, and analyzing the genetic characteristics of the subject based on the genetic data and the genotype-phenotype association database; The most suitable drug and drug dosage are recommended for the subject according to the genetic variation and genetic data of the subject.

8. A subject intelligent assessment device based on multi-dimensional data, characterized in that: include: An analysis report acquisition module, which acquires multi-dimensional data corresponding to the subject, and inputs the multi-dimensional data into a pre-trained evaluation model to obtain an analysis report of the subject's participation in the clinical study, wherein the multi-dimensional data includes the subject's clinical symptoms, Chinese and Western medicine diagnosis, medication information, and social background, and the analysis report includes accuracy, compliance, and risk; A treatment research program determination module is used to determine corresponding enrollment recommendations and treatment research programs for each subject based on the analysis report, wherein the treatment research program includes drug selection, dosage adjustment, and follow-up plan; The adjustment module continuously obtains the latest research data during the clinical research process, and uses the latest research data to adjust the analysis report and treatment research plan for the subjects.

9. A method for intelligent assessment of subjects based on multi-dimensional data, characterized in that: It comprises a processor, in which runs a program of the subject intelligent assessment method based on multi-dimensional data as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: A program storing the subject intelligent assessment method based on multi-dimensional data as described in any one of claims 1 to 7.

Images