1711 results about "Medication use" patented technology

The present invention provides a method and intelligent agent for data collection and drug safety signal mining for PMS, which relates to the field of drug monitoring technology, including standardizing PMS data using a medical text pre-training model, and integrating drug molecular characteristics and clinical medication information to construct a multi-layer drug knowledge graph. A heterogeneous graph neural network with a time series feature extraction function is used to represent the graph, and the drug action mechanism rules are integrated into the training process to obtain a node vector representation of multi-dimensional information fusion. Based on the node vector representation, a multi-task learning framework is constructed to simultaneously complete the drug-adverse reaction association probability prediction and risk level prediction, and combine time series analysis to detect dynamic change characteristics to generate candidate signals. Finally, a hierarchical evaluation system is constructed to verify the candidate signal, and its reliability is confirmed in combination with clinical data, the target signal is output, the warning level is determined, and differentiated monitoring cycles and trigger thresholds are set to perform real-time risk warnings.

The invention provides application of an SRC inhibitor combined with a KRAS-G12C inhibitor to preparation of a medicine for treating KRAS-G12C mutant lung cancer and a medicine composition, and relates to the technical field of biological medicine, compared with independent use of the KRAS-G12C inhibitor, the scheme of the SRC inhibitor combined with the KRAS-G12C inhibitor can effectively inhibit proliferation and survival of KRAS-G12C mutant lung cancer cells, and the application of the SRC inhibitor combined with the KRAS-G12C inhibitor to preparation of the medicine for treating the KRAS-G12C mutant lung cancer can be used for preparing the medicine for treating the KRAS-G12C mutant lung cancer. The compound can effectively inhibit the activation of a KRAS-MARK pathway and an SRC-FAK pathway, the combination of the compound and the compound has a synergistic anti-tumor effect, and the compound and the compound can be directly combined for medication, can also be prepared into a combined preparation for treating KRAS mutant lung cancer, and have a good clinical transformation prospect.

The invention discloses a chronic disease patient personalized diet and exercise guidance system and method, and relates to the technical field of medical health information, and the system comprises a data sensing module which continuously collects the dynamic physiological data, behavior data and environment variable data of a patient through an intelligent sensing device, the dynamic physiological data comprises a heart rate time sequence, a step number time sequence and a blood glucose concentration time sequence monitored by the wearable device, and the behavior data comprises a medication operation record with a timestamp and a patient's daily symptom self-grading number. According to the personalized diet and exercise guidance system and method for the chronic disease patient, the time synchronization precision of multi-source data is effectively improved, the accuracy of medication compliance monitoring and physiological index correlation analysis is ensured, and by establishing the dynamic correlation model of the environment temperature and the human body metabolic rate, the accuracy of medication compliance monitoring and physiological index correlation analysis is improved. The timeliness and safety of clinical intervention are improved, and powerful support is provided for health management of chronic disease patients.

The invention relates to the technical field of medical information processing, in particular to an information sharing method of a pediatric clinical nursing system. And obtaining complete medical record sample data including age, symptom results, treatment cycles and drug doses. Comprehensively analyzing the similarity between the age and the disease result, determining a difference characteristic value, and carrying out initial clustering to form a cluster; disease distribution and influence characteristics are analyzed from disease results in view of differences of disease response and treatment requirements of child patients of different age groups. Selecting a to-be-analyzed disease, determining a prominence index by combining the distribution density and the treatment cycle of the to-be-analyzed disease in the cluster, and reflecting distribution characteristics; meanwhile, the occurrence frequency, the difference characteristic value, the treatment cycle and the medication dosage are analyzed, and the influence degree index is judged. Clinical indexes are determined by combining the influence degree and the outburst index, and individual features of the child patient are accurately represented. Clustering analysis is carried out based on clinical indexes, a more accurate clustering result is obtained, and the information sharing accuracy is improved.

The invention relates to the technical field of physical sign monitoring, in particular to a physical sign parameter monitoring method accompanying the medication process of a severe mental disease patient. According to the method, the dynamic change of the physical sign parameters is captured by acquiring the physical sign parameter time sequence curves of the to-be-monitored patient in the two stages before and after medication. For each time sequence curve, data value differences and fluctuation duration are analyzed, and abnormal fluctuation data segments are identified. Furthermore, a historical normal time sequence curve is utilized to determine a historical normal fluctuation section corresponding to the abnormal fluctuation, and the overall state of the physical sign parameters is visually reflected through comparative analysis and calculation of a stable index. Meanwhile, the physical sign condition of the patient is monitored by combining the stability index of the same physical sign parameter before and after medication and the time sequence characteristics of the abnormal fluctuation data segment. According to the method, the physical sign state of the patient is reflected more accurately by comparing the change of the physical sign parameters of the patient with the historical normal condition, and data support with higher credibility is provided for doctors to carry out medicine effect judgment or illness state analysis.

The invention provides a medical drug management system and an optimization method, and relates to the technical field of medical information management, the system comprises a medicine management platform, a medicine database and an information technology support module; wherein the medicine management platform adopts a modular design and comprises a medicine inventory management module, a patient information management module, a medicine use process management module and a report statistics module, and information interaction among the modules is realized through interfaces; the medicine database is used for storing medicine information, medicine inventory information and patient medication information; the information technology support module comprises a bar code scanning module, an RFID technology module and a cloud computing module. By integrating the advanced information technology and the medical management concept, the medical drug management system which is comprehensive in function, efficient and safe is provided, the management process is optimized, the drug use safety is improved, the management cost is reduced, and personalized drug use suggestions and decision support are provided.

The invention provides a digital intelligence pharmacist medication guidance system based on artificial intelligence, and relates to the technical field of medical treatment, the system comprises a user interaction module, a server and an answer generation module, the user interaction module is used for receiving and initiating a question proposed by a user to the server; the server side is used for packaging questions proposed by a user and submitting the questions to the answer generation module; and the answer generation module is used for receiving the question from the server side, retrieving information associated with the question in a pre-constructed medicine specification knowledge base by using a retrieval enhancement generation (RAG) method, integrating the retrieved information by using a pre-trained language model to generate an answer for the question, and finally, sending the answer to the server side. According to the system and the method, a more convenient, accurate and intelligent medicine information query and medication guidance mode is provided, the quality and the safety of medical services are improved, and the use experience of the user is greatly improved.

The invention discloses development, preparation and application of a carbapenemase immunochromatography kit, and belongs to the technical field of biological medicines. According to comparison of amino acid sequences of five common carbapenemase variants of KPC, IMP, NDM, VIM and OXA48 types, the immunogen is prepared from conserved epitope sequences obtained respectively. A mouse is immunized, and a specific antibody pair which is strong in affinity and can be combined with multiple variants is obtained through multi-platform screening. On the basis, the carbapenemase quintuplet detection kit is developed by utilizing an immunochromatography method, has the advantages of high sensitivity, good specificity, capability of covering various drug-resistant variants, capability of detecting multiple targets at one time and rapidness and convenience in detection, and provides important help for clinical auxiliary diagnosis and medication guidance.

The invention relates to the technical field of medical data processing and prediction, and discloses a chemotherapy adverse reaction prediction and intervention system based on big data. The system integrates an unstructured disease course text and structured inspection data of a patient, generates a time-series symptom event, and performs time window alignment and fusion on the time-series symptom event, a medication record and a physical sign monitoring stream to form a multi-dimensional time-series data block. The system is combined with an external medical knowledge base to construct a dynamic association network among symptoms, medicines and physiological indexes, and dynamically calculates the confidence coefficient of an adverse reaction mode according to real-time data. By using a predictive model of the timing attention mechanism, the system can output a continuous curve of patient risk over time. The system automatically matches and generates a personalized intervention instruction sequence containing specific measures and execution time windows according to key time points and modes when the risk curve exceeds a threshold value. According to the invention, dynamic and advanced early warning and accurate intervention of adverse reaction risks of chemotherapy are realized.

The invention discloses a drug use dynamic risk assessment method and system for an elderly patient, and relates to the technical field of drug use risk assessment, and the method comprises the steps: collecting static factor data and dynamic factor data of the elderly patient, carrying out the preprocessing of the collected static factor data and dynamic factor data, and generating a feature vector for risk assessment; calculating a comprehensive risk score through a preset evaluation rule based on the feature vector; the comprehensive risk score is compared with a preset risk grading threshold value, the medication compliance risk grade of the elderly patient is determined, and the system comprises a data collection module, a preprocessing module, a risk assessment module and a grading module. The comprehensiveness and timeliness of medication compliance risk assessment of elderly patients are improved, and technical support is provided for medication intervention.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

The invention discloses a drug risk monitoring method based on multi-source data fusion, and relates to the technical field of data analysis. Through multi-source data fusion and processing, the problems of data isomerism and information islands are solved, a comprehensive and high-quality data basis is provided for drug risk monitoring, a weighted collaborative filtering algorithm and a Bayesian network are adopted, potential risk signals are accurately mined, real-time dynamic monitoring and early warning of drug risks are achieved, and the drug risk monitoring and early warning efficiency is improved. The timeliness and the reliability of monitoring are obviously improved; meanwhile, the real-time stream processing framework is used for carrying out incremental updating on newly-added data, dynamically adjusting a risk assessment result, and carrying out automatic early warning when a risk value exceeds a standard, so that the contradiction between a complex process and a rapid early warning demand is effectively solved, the efficiency and accuracy of drug safety supervision are improved, and powerful support is provided for guaranteeing the public drug safety.

The invention relates to the technical field of clinical medical data visualization, in particular to a visualization system for clinical internal medicine and a visualization method thereof. The method comprises the following steps: acquiring basic information of a clinical patient; according to the basic information of the clinical patient, performing medical treatment and medication event analysis, and performing triple entity relationship processing to generate patient medical record relationship map data; performing patient drug tolerance analysis based on the patient medical record relation map data to generate patient drug tolerance characteristic data; performing clinical physiological index risk probabilistic reasoning according to the drug tolerance characteristic data of the patient to generate individual clinical medication risk data; according to the individual clinical medication risk data, individual characteristic medicine dosage adjustment is carried out, medicine dosage curve visual dynamic display is carried out, and visual clinical medication response data is generated. According to the method, personalized and visual medication decision support is provided for clinicians through patient medication conditions and real-time physiological index analysis.

The invention provides an intelligent management system for chronic diseases and common diseases of old people. The intelligent management system comprises an old people comprehensive evaluation module, a multidisciplinary collaborative diagnosis and treatment module, a multi-medication risk prediction management module, an intelligent chronic disease follow-up diagnosis management module and a health science popularization knowledge base module, the senile comprehensive assessment module inputs the personal chronic disease map of the patient into a risk factor prediction model for prediction, and outputs a comprehensive assessment result; the multidisciplinary cooperative diagnosis and treatment module is used for the first diagnosis / receiving diagnosis doctor to coordinate multidisciplinary doctors to perform cooperative diagnosis and treatment; the multi-medication risk prediction management module is used for giving comprehensive diagnosis, multi-medication management and risk prediction to the patient through collaborative diagnosis and treatment; the chronic disease intelligent follow-up diagnosis management module carries out follow-up visit tracking, health risk prediction and review prompting on the patient; and the health science knowledge base module matches and pushes corresponding health education knowledge. The accuracy and timeliness of senile chronic disease co-disease risk assessment prediction and management are improved, and a good auxiliary effect is achieved on improvement of the life quality of patients.

To provide a pharmacist other than those who prepare a medicine with an opportunity of guiding administration of medicine on-line.SOLUTION: An on-line medicine administration guidance request section 52 transmits information pertaining to a request for guidance of administration of medicine on-line including information for identifying a predetermined prescription medicine based on a predetermined prescription to a pharmacist terminal 4-1 of a predetermined pharmacist as medicine administration guidance request information. An on-line medicine administration guidance enabling section 54 enables communication between the pharmacist terminal 4-1 of a predetermined pharmacist and a patient terminal 3 of a patient who receives medicine administration guidance on-line upon receipt of information regarding acceptance of a request for medicine administration guidance on-line. A delivery arrangement section 56 arranges delivery of a predetermined prescription medicine upon receipt of information on completion of each of on-line medicine administration guidance of the predetermined prescription medicine and preparation of medicine of the predetermined prescription medicine.SELECTED DRAWING: Figure 4

The invention discloses medical medicine medication auxiliary equipment, and particularly relates to the technical field of medical instruments.The medical medicine medication auxiliary equipment comprises an anti-skid handle, the lower end of the anti-skid handle is fixedly connected with an equipment shell, and a medicine squeezing structure communicating with the rear end of an inner cavity of the equipment shell is arranged on the rear portion of the inner cavity of the equipment shell; the front portion of an inner cavity of the equipment shell and the lower end of the equipment shell are jointly provided with a smearing structure, and the rear portion of the lower end of the equipment shell is rotationally connected with auxiliary wheels. According to the medical medicine medication auxiliary equipment, ointment and the external environment are sealed under the action of the shielding assembly, pollution of the external environment to medicine is reduced, meanwhile, the medicine is extruded out of the ointment tube under the action of the extrusion driving assembly and the extrusion assembly, and therefore the medicine is extruded out of the ointment tube. The smearing structure is synchronously driven to operate through transmission of the extrusion driving assembly and the smearing structure, ointment fed by the connecting base is evenly smeared to the skin of a patient, operation steps are reduced, smearing uniformity is improved, medicine residues in an ointment tube are reduced, and the utilization efficiency is improved.

The invention relates to the technical field of chronic disease information management, in particular to a chronic disease information management method which comprises the following steps: S1, acquiring physiological index data, medication record data and behavioral habit data of a patient through a multi-source data acquisition module; s2, performing feature extraction on the multi-source data by using a data analysis module to generate multi-dimensional health assessment parameters; and S3, dynamically comparing the health assessment parameters with a preset assessment value through a risk early warning module, triggering hierarchical early warning when an abnormal parameter combination is detected, realizing multi-modal data deep mining by using an intelligent analysis module, performing association analysis on wearable device sensor data, an electronic medical record and a medication compliance record, and determining whether the health assessment parameters are abnormal or not. An individualized medication portrait of the chronic disease patient is constructed, parameters such as gene detection and metabolic indexes are combined, through medicine-disease conflict detection and dose self-adaptive calculation, an accurate medication adjustment strategy and a chronic disease patient health behavior guidance scheme are generated, and individual differences are fully reflected.

The embodiment of the invention discloses a medication answering method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-answered medication question, a knowledge base associated with the medication question and a large language model which is obtained by learning at least one candidate prompt word in advance and has a medication answering function; identifying a question category to which the medication question belongs, and determining a preliminary cue word matched with the question category in the at least one candidate cue word; according to the knowledge base and the medication problem, adjusting the preliminary cue word to obtain a target cue word; and inputting the target cue word into the large language model, so as to generate a drug use answer for solving the drug use problem according to the large language model and based on the target cue word. According to the technical scheme provided by the embodiment of the invention, the knowledge base is embedded into the large language model, so that the professionality of the medication solution generated by the large language model is improved by utilizing the knowledge base, and the accurate solution of the medication problem is ensured.

The invention provides an automatic determination method and system for an individualized medication adjustment dosage, and the method comprises the steps: calculating an initial administration scheme through employing a weighting result of a first atrioventricular model and a second atrioventricular model based on the physiological data and medication types of a patient; meanwhile, when the initial administration scheme of the patient is poor in effect or has adverse reaction, the administration scheme of the patient is adjusted. Compared with the prior art, the method has the advantages that the medication dosage can be accurately predicted and adjusted, adjustment suggestions can be given in real time through a feedback mechanism, and the safety and the curative effect of individualized medication are ensured.

A system and method for generating patient care instructions based on real-time sensor and medical data. The method includes receiving time-stamped sensor data from a sensor network comprising motion, occupancy, and environmental sensors, and receiving medical data associated with a patient, including medical conditions, treatment history, medication data, and biometric data. The sensor data is enriched with room-specific information, and activity pattern data is generated in real time using a pattern recognition model. The activity pattern data includes mobility, sleep patterns, medication adherence, statistical measures, temporal patterns, and correlations with medical data. Anomalies indicating potential health risks are detected by comparing current activity patterns with baseline data. A prediction model, trained on historical patient data, assesses the patient's health and generates care instructions accordingly. The care instructions are securely delivered to patient devices, caregiver applications, or automated medication dispensing systems, enabling timely interventions and continuous patient monitoring.

The invention provides a medication guidance generation system, and relates to the technical field of medical informatization. Comprising a knowledge base construction module used for constructing a medication guidance knowledge base; the graph term construction module is used for constructing a knowledge graph and a standard term set according to the medication guidance knowledge base; the index construction module is used for splitting the medication guidance knowledge base and constructing a vector index and a full-text index; the personal information processing module is used for acquiring personal information of a patient and preprocessing the personal information to obtain preprocessed personal information; the mixed detection module is used for performing retrieval by utilizing a knowledge graph, a vector index and a full-text index according to the preprocessed personal information to obtain a retrieval result set and obtain an intersection result according to the retrieval result set; and the generation module is used for generating medication guidance corresponding to the target patient according to the intersection result. The problems that traditional medication guidance mainly depends on experience and professional knowledge of medical staff, and is low in efficiency, prone to errors and difficult to personalize are solved.

The invention discloses an automatic medicine vending machine based on AI inquiry. The automatic medicine vending machine comprises a machine body, an intelligent interaction system, an AI inquiry system, a medicine storage bin, a control system, a payment system and a printing device. A constant-temperature and constant-humidity system and a disinfection and air purification module are arranged in the machine body. The intelligent interaction system collects symptom information in a multi-mode mode, and the AI inquiry system achieves symptom analysis, intelligent triage and personalized medication recommendation based on a medical knowledge graph and a natural language processing technology. The medicine storage bin carries a three-dimensional matrix type medicine cabinet and an intelligent inventory system, and intelligent replenishment is carried out through an LSTM algorithm. And the control system adopts a controller and triple verification to realize high-precision medicine discharging operation. The payment system supports multiple settlement modes and block chain evidence storage data. And the printing device outputs the medication guide sheet and the anti-counterfeit label. According to the invention, AI and automation technologies are deeply integrated, the service of less than or equal to 3 minutes in the whole process of inquiry-payment-medicine dispensing is realized, and the safety and convenience of medicine use are improved.

The invention discloses an internal medicine drug curative effect evaluation method based on big data analysis of an electronic health record, and the method comprises the steps: extracting basic health data, diagnosis and treatment time sequence data and drug intervention data from the electronic health record, and carrying out the time-space alignment to generate a dynamic feature set; subgroups are obtained based on disease typing standard hierarchical clustering, and historical data and real world data are fused through transfer learning to construct a subgroup curative effect reference matrix; collecting data after medication in real time, and generating an evaluation vector containing short-term physiological response, middle-term symptom improvement and long-term prognosis risk through deep learning; dynamically matching the evaluation vector with the reference matrix, and introducing an individual weight coefficient to correct deviation; taking the deviation correction value as input, constructing a self-adaptive evaluation model through reinforcement learning, and performing iterative optimization; and generating an individualized report containing the curative effect level, the medication suggestion and the risk early warning, and quantifying the curative effect level through a fuzzy comprehensive evaluation method. According to the method, individual differences are accurately captured, full-cycle dynamic evaluation is realized, and the curative effect evaluation accuracy and the clinical decision-making efficiency are improved.

The invention discloses a diabetic nephropathy risk assessment and prediction system based on platelet indexes, and particularly relates to the technical field of disease risk prediction, which comprises a platelet index acquisition module, a drug use data acquisition module, a drug intervention assessment module, an index correction module, a clustering analysis module and a diabetic nephropathy risk assessment module, the method comprises the following steps: acquiring platelet related detection indexes and historical medication data of a patient, calculating a drug intervention influence index and generating a correction factor, realizing platelet index reverse reduction correction by adopting a convolutional neural network model, determining a similar group in combination with clustering analysis, and dynamically evaluating the risk of diabetic nephropathy based on the average morbidity of the group. Personalized accurate prediction is realized; the platelet index correction accuracy is improved, individualized drug intervention elimination is achieved, the similar population clustering analysis precision is enhanced, diabetic nephropathy risk prediction is dynamically adjusted in combination with comprehensive scores, and the early screening and individualized intervention effects are improved.

The invention relates to the field of medical health and artificial intelligence, and discloses a cancer patient low-molecular heparin individualized dose dynamic optimization method based on reinforcement learning, and the method comprises the steps: S1, collecting multi-dimensional clinical data of a cancer patient, carrying out the preprocessing, carrying out the enhancement of the preprocessed data, and generating time sequence synthesis data; s2, based on the multi-dimensional clinical data and the time sequence synthesis data, constructing a reinforcement learning environment, including defining a state space, an action space and a reward function; s3, constructing an environment adaptive deep reinforcement learning EA-DRL model as a dose optimization model; performing model training and optimization in the reinforcement learning environment until convergence and obtaining an optimal dose decision strategy; and S4, inputting real-time dynamic clinical data of a patient to be decided into the trained dosage optimization model, and outputting individualized low molecular heparin LMWH administration dosage recommendation. Individualized precise administration is realized, and the treatment effect and the clinical medication safety are improved.

A medication container encoding, verification and identification method is provided that includes receiving data characterizing a medication, generating an identifier encapsulating the data and applying an identifier to a medication container such that it is automatically readable by a medication device. Related apparatus, systems, methods and articles are also described.

The invention relates to the technical field of medical information, and discloses a dosage regulation and control data storage method and system based on multi-modal diagnosis and treatment data, and the method comprises the steps: obtaining historical diagnosis and treatment data and historical dosage data of a patient; matching the diagnosis result data with health diagnosis result data in the health diagnosis and treatment data, and matching the wearable device data with health wearable device data in the health diagnosis and treatment data; determining whether to adjust the historical dose data to obtain the current dose, and outputting a judgment result needing to be adjusted; and obtaining the adjusted dose data, and storing the diagnosis result data, the diagnosis result similarity, the wearable device data, the wearable data similarity, the historical dose data and the adjusted dose data into a dose adjustment unit in a patient database. According to the invention, multi-modal diagnosis and treatment data can be integrated and applied to dose regulation and control, and a complete medication decision data chain is constructed.

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.

The invention provides a whole-process targeting polypeptide, a drug compound thereof, a drug delivery system and application of the whole-process targeting polypeptide. The whole-process targeting polypeptide comprises pHA and VAP which are covalently connected through a bridging structure; the VAP is LVAP, a reverse sequence polypeptide of the LVAP, DVAP or SVAP; wherein the amino acid sequence of the LVAP is as shown in SEQ ID NO: 1; the SVAP is a D-configuration polypeptide of the LVAP; the DVAP is a D-configuration polypeptide of the LVAP inverted sequence polypeptide; the bridging structure is amino-monoethylene glycol-carboxylic acid or a derivative thereof. The whole-process targeting polypeptide not only can cross a blood-brain barrier, target tumor neovascularization, cross a blood-tumor barrier, target tumor mimicry vessels, tumor cells and stem cells thereof, but also has better tumor cell affinity, anti-tumor treatment and in-vivo safety effects. Meanwhile, the drug compound or the drug delivery system has a synergistic effect after being combined with a chemotherapeutic drug, and the anti-tumor drug effect can be remarkably improved.

The invention discloses a liver transplantation post-operation intelligent management system and method based on multi-modal monitoring and AI. The method comprises the following steps that S1, physiological parameters, biomarker data, iconography data and self-reporting data of a patient are collected in real time; s2, carrying out preprocessing, feature extraction and fusion on the collected data, and predicting the postoperative complication risk of the patient by using a risk prediction model; s3, generating a personalized management strategy by using a reinforcement learning algorithm according to a risk prediction result, and providing early warning information and intervention suggestions for medical personnel and patients; s4, according to the postoperative management plan and the doctor's advice of the patient, a fixed-point medication reminding function is set, a personalized medication scheme is generated, and medication compliance of the patient is monitored; and S5, the patient makes an appointment for remote medical services, selects appropriate doctors and time to carry out video inquiry or online consultation, and records inquiry content.