54 results about "Medication risk" patented technology

The invention discloses a patient medication risk early warning method, and relates to the technical field of medication risk evaluation. The method comprises the following steps: acquiring multi-dimensional data such as age, past medical history, allergy history, clinical examination data and prescription data of a patient, extracting chronic disease diagnosis information from the multi-dimensional data, and carrying out standardized coding to generate a common disease combination tag; calling a preset disease weight system, and calculating a common disease severity score in combination with an age factor; extracting metabolism-related indexes to construct a drug metabolism capability feature vector of the patient; mapping the drug combination, the co-disease tag and the metabolic feature vector to a clinical drug knowledge base to generate a comprehensive conflict detection result; and generating corresponding early warning information based on the conflict detection result and a preset early warning rule base, wherein the rule base comprises a mapping relationship between the conflict type and the early warning level. According to the method, drug use risk early warning can be realized according to the multi-disease coexistence state of the patient, and the drug use safety is improved.

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.

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 discloses an artificial intelligence-based prescription multi-modal intelligent auditing system, which comprises a multi-modal data acquisition module, a data preprocessing module, a feature fusion module, an intelligent auditing model and a result feedback and knowledge base updating module, the multi-modal data comprises basic information of a patient, diagnosis information, inspection report data, medical image data and prescription text information, the data is automatically collected through the multi-modal data collection module, a deep learning algorithm is used for rapid processing and analysis, rapid examination and verification of a prescription are completed, comprehensive analysis is conducted in combination with the multi-modal data, and the accuracy of the prescription is improved. Comprising basic information, diagnosis information, inspection report data, medical image data and the like of the patient, whether the prescription has the problems of drug interaction, dosage abnormity, mismatching of indications and the like or not can be judged more comprehensively and accurately, in this way, auditing errors are effectively reduced, the drug use risk of the patient is reduced, and the drug use safety of the patient is guaranteed.

The invention discloses an interpretable clinical decision support system based on label generation and a knowledge graph. The method comprises the following steps: based on a breast cancer domain knowledge enhanced version Qwen-BrCaAdapt of a general large language model Qwen, analyzing an unstructured medical record text of a patient, and generating a structured result containing tags, values, evidences and explanations; calculating a reasoning label through a path matching engine by utilizing an editable structured path rule table, and matching a candidate treatment scheme according to the reasoning label; and taking the matched treatment scheme as a central node, calling a medical knowledge graph to bind entity information including clinical evidence, recommendation levels, medical insurance information, medication risks, usage and dosage, and generating a traceable JSON structure and a visual report. The method has the beneficial effects that the accuracy and efficiency of tag generation in the breast cancer field are improved, the rule maintenance cost is reduced, the interpretability and traceability of a clinical decision scheme are enhanced, and the acceptability of a doctor to a recommendation result is improved.

The present invention proposes an intelligent management system for chronic comorbidities in the elderly, including a comprehensive elderly assessment module, a multidisciplinary collaborative diagnosis and treatment module, a multiple medication risk prediction management module, a chronic disease intelligent follow-up management module, and a health science knowledge base module. The comprehensive elderly assessment module inputs the patient's individual chronic disease profile into a risk factor prediction model for prediction and outputs a comprehensive assessment result. The multidisciplinary collaborative diagnosis and treatment module is used by the first-visit / admitting physician to coordinate multidisciplinary physicians for collaborative diagnosis and treatment. The multiple medication risk prediction management module is used to provide patients with comprehensive diagnosis, multiple medication management, and risk prediction through the collaborative diagnosis and treatment. The chronic disease intelligent follow-up management module performs follow-up tracking, health risk prediction, and review prompts for patients. The health science knowledge base module matches and pushes corresponding health education knowledge. The present invention improves the accuracy and timeliness of risk assessment, prediction, and management of chronic comorbidities in the elderly, and plays a good auxiliary role in improving the quality of life of patients.

The invention relates to the technical field of medical care information, in particular to an anesthesia preoperative medication risk assessment method, which comprises the following steps: acquiring preoperative anesthetic dose and operation time of each historical patient and different types of physiological index data of each historical patient in different operation stages; based on the preoperative anesthetic dosage and the operation time of each historical patient, the change condition of different types of physiological index data of each historical patient in different operation stages is analyzed, and risk weights generated by anesthetic medication for different types of physiological indexes are determined; and based on the risk weight, constructing an anesthetic drug risk assessment model, and performing real-time anesthetic drug risk assessment on the patient by using the constructed anesthetic drug risk assessment model. According to the method, the risk weights generated by the anesthetic drugs for different types of physiological indexes are determined in a self-adaptive manner, and the anesthetic drug risk assessment model is constructed based on the risk weights so as to perform anesthetic drug risk assessment, so that the risk assessment accuracy is effectively improved.

The invention relates to the technical field of medical care informatics, in particular to a nephropathy medication interaction risk screening system which comprises a data acquisition unit, an SOC chip processing and analysis unit, a risk early warning unit and a man-machine interaction and communication unit. Through multi-source data access, dynamic weight scheduling and Kalman filtering fusion correction, accurate and on-demand acquisition of nephropathy core indexes is realized, and real-time intelligent discrimination of personalized medication risks is realized by relying on knowledge base support, adaptive path adjustment and a safe atomization updating mechanism, so that the risk of the nephropathy is accurately determined. Therefore, the problems that traditional system data acquisition lacks specialized pertinence, risk assessment personalization is insufficient, and model updating is lagged are effectively solved, and the accuracy and timeliness of nephropathy medication interaction risk screening are remarkably improved.

The invention relates to the technical field of medical information, in particular to a medication safety management method, device and equipment and a readable medium. The method comprises the following steps: acquiring electronic prescription data containing medicine names, specifications, dosage, usage, medication time and basic information of patients; performing multi-dimensional risk analysis on the electronic prescription data based on the medication safety knowledge base, including incompatibility analysis, dose safety analysis, medication time conflict analysis and special usage suitability analysis, and generating medication risk prompt information and a personalized medication guidance scheme; the medication guidance scheme comprises a text abstract and instruction data used for generating a two-dimensional code; driving label printing equipment to print the intelligent medication two-dimensional code; a detailed text, a voice interpretation file or a medicine use demonstration video file in the medicine use guidance scheme is associated with the two-dimensional code and stored in a server; a user can access the multimedia guidance file by scanning the two-dimensional code through terminal equipment, so that the medication compliance and safety of a patient are improved.

The invention discloses a medical prescription auditing and medication safety management and control method, device and equipment and a storage medium, and relates to the technical field of medical informatization and health big data, the method comprises the following steps: obtaining drug information, patient allergy history and multi-dimensional health data in a target prescription, and constructing a prescription data set; in combination with a medicine knowledge base and a taboo database, intelligent verification is carried out on the medication rationality of the prescription by utilizing a Duros rule engine and a graph neural network model, and a verification result and verification detail data are output; if the verification result is unreasonable, marking the prescription as a risk prescription, generating a medication risk assessment report and a prescription modification suggestion based on verification detail data, and pushing the report and the suggestion to a corresponding doctor terminal; and if the verification result is reasonable, generating personalized medication guidance through prescription-patient feature matching, and sending the personalized medication guidance to the doctor terminal, the patient terminal and the pharmacy terminal. According to the application, intelligent prescription checking and medication safety guarantee can be realized through risk assessment and personalized adaptation.

The invention relates to the field of medical health and artificial intelligence, and discloses a tumor-chronic disease co-disease medication dynamic risk early warning method fusing AI and a rule engine, and the method comprises the steps: S1, collecting multi-source data of a co-disease patient, and generating unified feature data; s2, constructing a risk prediction model comprising a rule engine and a few-sample learning model; the rule engine performs preliminary judgment on the drug use risk; carrying out refined risk probability prediction on the few-sample learning model by utilizing a global category representation and feature matching mechanism constructed by the few-sample learning model; carrying out weighted fusion on prediction results of the two; s3, dynamically optimizing parameters of the few-sample learning model by adopting a meta-heuristic optimization algorithm; and S4, based on the optimized risk prediction model, carrying out real-time risk early warning on the medication data of the co-patient. According to the method, the technical problems of difficulty in prediction of a few-sample scene, low parameter optimization efficiency, unreliable early warning result and the like are effectively solved, and real-time monitoring, accurate evaluation and individualized early warning of the medication risk of the co-patient are realized.

The invention relates to the technical field of medical care evaluation, in particular to an elderly tumor care evaluation system and method based on big data analysis, and the method comprises the steps: obtaining patient medication data and patient complication data; evaluating the medication matching degree of the patient through the medication data of the patient and the complication data of the patient; evaluating the multi-medication risk of the patient by combining the medication matching degree of the patient with medication data of the patient; evaluating the complication risk of the patient by combining the disease cumulative score table with the complication data of the patient; drug weights are distributed to the complication risks of the patients and based on the complication risk list of the patients; and adjusting the medication scheme of the patient and performing medication risk early warning on the patient by integrating the multi-medication risk assessment result of the patient and the medicine weight distribution result. According to the method, the medication scheme of the patient is adjusted in a targeted manner by quantifying the multi-medication risk of the patient and the complication risk of the patient, so that the accuracy and timeliness of nursing intervention are effectively improved.

This invention discloses a method, apparatus, device, and storage medium for medication risk assessment during pregnancy, relating to the interdisciplinary fields of medical information technology and clinical pharmacy. The method includes: receiving patient information and proposed medication regimen information, whereby the patient information includes gestational age and information on multiple comorbidities; calling the corresponding gestational age adaptive risk weight matrix based on gestational age, weighting the patient features to generate a weighted feature vector, and inputting this vector into a machine learning model to obtain an initial medication risk score; based on a constructed heterogeneous knowledge graph, using entities corresponding to multiple comorbidities and medication regimen information as query starting points, performing multi-hop reasoning to determine the existence and severity of conflicting paths; if a conflicting path is detected, revising the initial medication risk score according to the severity of the conflict to generate a final risk score. This application enables individualized, interpretable, and evidence-based intelligent risk assessment of proposed medication regimens for pregnant women with multiple comorbidities.

The invention belongs to the technical field of medical information, and particularly relates to a newly diagnosed HIV infected person antiviral treatment intelligent decision support system which comprises a multi-source data integration module, a medicine knowledge module, an individualized decision support module, an interactive output module, a knowledge optimization module and a safety and privacy protection module. The system replaces doctors to query and integrate patient data across multiple systems, clinical time is saved, key information omission is avoided, comprehensive data support is provided for treatment decisions, experience decisions are converted into systematic analysis and graded risk prompts are generated by means of an individualized decision support module, scheme deviation is effectively reduced, the medication risk is reduced, and the treatment efficiency is improved. The method assists the basic level in improving the treatment normalization, allows doctors to input new experience and new achievements summarized in practice into the system after double-blind auditing, and provides continuously iterative knowledge support for the scientificity of the antiviral treatment decision of the newly diagnosed HIV infected person.

The embodiment of the invention discloses a Crohn disease longitudinal queue course prediction system based on time perception Transform, and relates to the technical field of medical data processing, the system comprises a data preprocessing module, an input embedding layer, a Transform encoder and a multi-task multi-time window prediction header, the system breaks through the limitation that only baseline data is used for prediction of a traditional model, and the prediction efficiency is improved. According to the method, the full-cycle longitudinal data (such as inflammation indexes and medication records of each treatment) of the patient from the morbidity to the prediction can be integrated, and the disease progress, operation and medication risks of 1 year / 3 years / 5 years can be dynamically output. And reference is provided for clinicians.

The invention discloses an interactive safe medication suggestion method and system oriented to medicine-diagnosis cross check, and belongs to the technical field of medical artificial intelligence and clinical decision support (CDSS). According to the method, an intelligent probe is implanted into a writing interface of an electronic medical record (EMR), so that full-dimensional context information such as structured diagnosis, unstructured texts (such as chief complaint) and past medicine history of a patient is sensed in real time. At the moment when a doctor inputs a prescription, the system concurrently executes'drug-disease 'taboo verification and'drug-drug' interaction verification by using a pre-constructed heterogeneous medical knowledge graph. Once a potential medication risk is detected, the system immediately triggers a blocking mechanism, and automatically generates compliant alternative drug suggestions which are pharmacologically equivalent and conform to the physiological characteristics of the current patient based on graph embedding vector similarity and a multi-objective optimization sorting algorithm.

Embodiments of the present disclosure relate to the technical field of medical data processing, and specifically to a nutritional preparation medication information processing method, device, equipment and medium. The main steps of the foregoing method include: in response to an audit request for a medication prescription, retrieving and obtaining clinical diagnosis data corresponding to the medication prescription, extracting a disease keyword from the clinical diagnosis data, matching and analyzing the disease keyword with preset parenteral nutrition indication information to obtain a matching result, when the matching result indicates a matching success, auditing the medication prescription according to a first medication standard, and when the matching result indicates a matching failure, determining a second medication standard according to a nutritional risk score, gastrointestinal disorder information and a preset decision matrix, and auditing the medication prescription according to the second medication standard. The foregoing method can improve the efficiency of prescription auditing, improve the accuracy and rationality of the nutritional preparation medication prescription audit result, and reduce the medication risk of the nutritional preparation medication prescription.

The embodiment of the invention provides an autonomous medicine delivery and safety monitoring method and robot, and the method comprises the steps: responding to a received medicine delivery instruction, and preloading a historical complexion health index database of a user; controlling the robot to move to a target user position of the user, and performing identity verification at the target user position; when the identity verification is passed, calling a personalized medication scheme and a historical complexion health index database of the user; collecting blood pressure, heart rate and body temperature data of the user to obtain a current-day complexion health index of the user; comparing the current-day complexion health index with a historical complexion health index database to obtain the complexion health degree of the user; fusing the complexion health degree, the physiological data and the drug taboo information to generate a drug use risk result; and adjusting the personalized medication scheme based on the medication risk result, and dynamically generating and outputting target medication guidance information. According to the method, the accuracy, the interactive experience and the safety of the medicine delivery service are improved.

The invention discloses a pharmacogenomics report generation method and device, a medium and a product, and relates to the field. The method comprises the following steps: in response to the completion of a drug genome experiment of a first object, obtaining experimental result data of the drug genome experiment, and determining experimental result interpretation data based on a first drug gene association database; inputting the experimental result interpretation data, the object attribute information corresponding to the first object, the medical record information and the first medicine information into a pre-trained medication risk assessment model to obtain a medication risk assessment result of the first object; and generating a pharmacogenomics report of the first object based on the medication risk assessment result, the experimental result interpretation data, the object attribute information, the medical record information and the first drug information. According to the technical scheme of the invention, the problems of high threshold and low efficiency of the existing pharmacogenomics report generation are solved, the pharmacogenomics report can be automatically generated, the accuracy of the report content is improved, and the report generation cost is reduced.

The application discloses a model for predicting lactation medication risk, wherein when the model is used to predict the lactation medication risk, human trials are not needed for the to-be-tested drug, the lactation risk can be predicted based on the molecular structure of the to-be-tested drug, and when the lactation medication risk is predicted, the ROC_AUC value of 10-fold cross-validation reaches 0.80, indicating that the model has excellent prediction performance and generalization ability. The model can provide a rapid lactation medication risk evaluation tool for doctors and pharmacists, assist in formulating a safe medication scheme, avoid unnecessary weaning, reduce the adverse reaction risk of breastfed infants, and improve the medication safety level of mothers and infants. Meanwhile, the model can be combined to accurately evaluate the early safety of a new drug, identify and avoid potential lactation medication risks, guide molecular optimization in the new drug research and development process, and reduce the research and development cost and time.

The invention discloses a construction method and application of a safety evaluation risk prediction model after child medication marketing, and the method comprises the steps: constructing a three-level index system, and determining the weight of each level of index through an analytic hierarchy process; based on the three-level index system, determining a risk feature set, constructing a three-level model architecture, and performing model training by adopting a machine learning algorithm; collecting multi-source real world data, and establishing a standardized data pool; the performance of the model is improved through hyper-parameter optimization and integrated learning, the performance of the model is verified and evaluated by adopting internal cross validation and an external independent data set, and the transparency of the model is enhanced through an interpretability technology; and establishing a dynamic updating mechanism, deploying the model as a cloud API service, and realizing risk prediction and early warning functions. According to the invention, multi-source real world data can be effectively integrated, the children medication safety risk can be accurately identified, the method is used for safety evaluation after children medication is listed, and the method is of great significance to supplement clinical test insufficiency before children medication is listed, reduce medication risk and guarantee children medication safety.

The present invention discloses a method and system for dynamic risk assessment of medication for elderly patients, relating to the technical field of medication risk assessment. The method comprises collecting static factor data and dynamic factor data of elderly patients, pre-processing the collected static factor data and dynamic factor data, and generating a feature vector for risk assessment; calculating a comprehensive risk score based on the feature vector using preset assessment rules; comparing the comprehensive risk score with a preset risk grading threshold to determine the medication compliance risk level of the elderly patients. The system comprises data collection, pre-processing, risk assessment, and grading modules. The present application realizes multi-dimensional risk assessment by integrating static and dynamic data, thereby improving the comprehensiveness and timeliness of medication compliance risk assessment for elderly patients and providing technical support for medication intervention.

The invention belongs to the technical field of artificial intelligence, and relates to a data processing method, which comprises the following steps: acquiring multi-dimensional physiological data and drug combination data of a target patient; calling a target prediction model issued by the central server; performing prediction processing on the multi-dimensional physiological data and the drug combination data based on the target prediction model to obtain corresponding adverse reaction evaluation data; judging whether the adverse reaction evaluation data is smaller than a preset dynamic safety threshold value or not; if yes, target drugs corresponding to the drug combination data are grabbed based on a drug dispensing tool; and generating medicine taking information corresponding to the target medicine, and displaying the medicine taking information. The invention further provides a data processing device, computer equipment and a storage medium. In addition, the medicine taking information can be stored in the block chain. The method can be applied to a medication management scene in the field of medical science and technology, and improves the efficiency and accuracy of medication risk assessment processing and medicine distribution processing.

The embodiment of the invention provides a method and a device for detecting medication safety, and aims to detect the medication safety in medication suggestions and obtain the medication suggestions and user health portraits. The method comprises the following steps: acquiring a drug name in a drug use suggestion, and then aligning the drug name with a standard drug name in a drug knowledge base based on multiple alignment approaches; thirdly, through user portrait information and the aligned standard medicine names, an information graph at least containing disease entities, medicine entities and the incidence relation between the disease entities and the medicine entities is constructed; and based on the information graph, performing graph retrieval in a pre-constructed drug use knowledge graph, and determining whether the drug use suggestions have drug use safety risks or not by using drug use risk knowledge in the drug use knowledge graph. Therefore, the drug use risk in a complex scene can be identified.

This invention discloses a method for medication risk warning and dynamic intervention using a smart pillbox for elderly patients with chronic diseases, relating to the fields of intelligent medical devices and chronic disease management technology. The method first constructs four types of chronic disease knowledge graphs: medication behavior, physiological indicators, drug interactions, and environmental conditions. Then, it processes the graphs using a GNN module with a GRU-like gating mechanism, weightedly fusing multi-time-step features using a time-step attention mechanism, and integrating similar patient features based on Jaccard similarity to generate comprehensive risk features. During the training phase, the risk level and type are output after feature optimization using a collaborative attention mechanism. In the inference phase, a similarity propagation mechanism is used to address the cold-start problem for newly enrolled patients, matching a three-level intervention rule base to generate personalized strategies, and iteratively optimizing the model based on intervention feedback. This invention achieves proactive and precise warning and dynamic intervention, improving risk assessment performance and patient medication adherence in different scenarios.

The present invention discloses a method for early warning of patients' medication risks, which relates to the technical field of medication risk assessment. The method obtains multi-dimensional data such as the patient's age, past medical history, allergy history, clinical examination data, and prescription data, extracts chronic disease diagnosis information from them, and generates co-morbidity combination labels through standardized coding; calls a preset disease weight system, and calculates the co-morbidity severity score in combination with age factors; extracts metabolism-related indicators to construct a feature vector of the patient's drug metabolism ability; maps the drug combination, co-morbidity label, and metabolism feature vector to a clinical medication knowledge base to generate a comprehensive conflict detection result; generates corresponding early warning information based on the conflict detection result and a preset early warning rule base, and the rule base contains the mapping relationship between the conflict type and the early warning level. This method can achieve early warning of medication risks for the state of co-existence of multiple diseases in patients and improve medication safety.

This application provides a method, device, and electronic device for assessing medication risks during pregnancy. Through automated knowledge graph querying and risk calculation, a risk assessment report can be quickly generated, reducing the time and cognitive load required for doctors to manually review literature and memorize complex rules. Simultaneously, it can match the current stage of medication risk to the patient's specific gestational age and perform comprehensive, real-time risk assessment based on the pregnant patient's real-time physiological indicators. This avoids the shortcomings of risk assessments that fail to consider gestational changes and individual patient differences, improving the accuracy of risk assessment. Furthermore, by acquiring medication information and physiological indicators, it can continuously and in real-time calculate the risk of medication use based on the patient's actual medication behavior and changes in physiological state during medication. Based on the real-time risk assessment results, medication guidance can be provided to patients, enhancing their understanding of the medication regimen and reducing the risks of medication use during pregnancy.

The embodiment of the invention relates to the technical field of medical data processing, in particular to a nutrition preparation medication information processing method and device, equipment and a medium. The method mainly comprises the steps that in response to an auditing request for a medication prescription, clinical diagnosis data corresponding to the medication prescription are retrieved and obtained, disease keywords in the clinical diagnosis data are extracted, matching analysis is conducted on the disease keywords and preset parenteral nutrition indication information, and a matching result is obtained; when the matching result shows that matching succeeds, the medication prescription is audited according to the first medication standard, and when the matching result shows that matching fails, a second medication standard is determined according to the nutrition risk score, the gastrointestinal disorder information and a preset decision matrix, and the medication prescription is audited according to the second medication standard. By adopting the method, the prescription checking efficiency can be improved, the accuracy and rationality of the nutrition preparation medication prescription checking result can be improved, and the medication risk of the nutrition preparation medication prescription can be reduced.

The invention relates to the technical field of clinical medication, and discloses a clinical medication intelligent reminding system which comprises an information acquisition module, a data preprocessing module, a feature fusion module, a medication execution module and an optimization module. Through cooperative operation of an information acquisition module, a data preprocessing module and a feature fusion module, the system does not need to depend on a medicine identification technology, but binds a unique ID of a patient through an RFID wristband, and combines rapid processing of real-time physiological data through edge calculation, so that accurate association of patient information and a medication plan is realized, and the accuracy of the medication plan is improved. Meanwhile, the reminding sequence is dynamically optimized based on the improved Dijkstra algorithm, medicine identification errors are avoided, the timeliness and adaptability of clinical medication reminding are remarkably improved, and the medication risk caused by information mismatching is effectively reduced.

The invention relates to a method for applying a medical big language model of an integrated knowledge graph to drug analysis and diagnosis support. The method comprises the steps of performing streaming analysis and entity recognition on a structured pharmaceutical database file, generating a drug record set containing standardized biomedical entity annotations, and constructing a graph structure knowledge base and a vector index base in parallel; receiving a natural language query, extracting drugs and disease entities and mapping the drugs and disease entities into standardized identifiers; synchronously retrieving the structured evidence set and the text evidence set; assembling an enhanced prompt text containing the task instruction, the verified evidence and the query according to the template; and inputting the large language model subjected to biomedical task fine tuning, and generating a binding response. By adopting the method, the problems of static stiffness, pure language model illusion and insufficient interaction of a structured knowledge base of a traditional system can be solved, reliable decision assistance is provided for replacement treatment and interaction review under the condition of drug shortage, and the drug use risk is reduced.