827 results about "Medication use" patented technology

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.

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.

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 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 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.

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 belongs to the technical field of artificial intelligence and medical information, and discloses a multi-round inquiry method and system based on a large language model and session state tracking. According to the method, extraction and synonym normalization are carried out for key medical elements, and high-confidence filling and conflict resolution are continuously completed in multiple rounds of conversations; and fusing the red flag symptom rule and model prediction, and carrying out hierarchical scoring and security constraint generation on individual risks. The information gain maximization serves as a target, and the next round of clarification problem is generated in a self-adaptive mode under the risk constraint; and through cooperation of a large language model and a knowledge base / knowledge graph, sorting and gate type calibration are carried out on candidate diseases and matched departments, and doctor-seeing suggestions, examination suggestions and medication precautions are generated. Finally, efficient understanding and multi-round reasoning of the unstructured symptom information are realized through joint supervision of the session state, the slot confidence and the risk hierarchy.

The invention relates to the technical field of hepatitis diagnosis and treatment management, and discloses a block chain-based hepatitis patient full-period management method. The method comprises the following steps: creating a patient identity chain in a block chain network, and storing patient registration data and initial diagnosis information; full-cycle diagnosis and treatment data of a patient are obtained and divided into clinical examination, iconography, medication records and life sign monitoring data. Performing multi-dimensional cleaning on the clinical examination data to generate a standardized data set; inputting the iconography data into a pre-trained hepatitis feature extraction model, and outputting a liver injury feature set; verifying the integrity of the medication record, and generating a non-tampering medication time sequence chain; comparing the life sign monitoring data with preset health reference parameters to generate a sign deviation coefficient set; according to the method, data safety and traceability are guaranteed through the block chain, diagnosis and treatment data quality is improved through multi-dimensional data processing, patient privacy is protected, and reliable support is provided for full-period management of hepatitis patients.

The invention discloses a low-water-solubility medicine crystal as well as a preparation method and application thereof. The compound Z pamoate crystal form I has large characteristic diffraction peaks at one or more positions with diffraction angles 2 theta of 9.9 + / -0.2 degrees, 10.7 + / -0.2 degrees, 12.1 + / -0.2 degrees, 14.9 + / -0.2 degrees, 17.3 + / -0.2 degrees, 19.4 + / -0.2 degrees, 20.7 + / -0.2 degrees and 23.1 + / -0.2 degrees. Compared with an amorphous compound Z pamoate, the crystal provided by the invention has the advantages of good stability, no crystal transformation phenomenon, no obvious increase of related substances and low solubility; the production process is simple, only water is used as a solvent in the whole process, and no organic solvent is used; when the long-acting slow-release anti-Parkinson medicine composition is prepared from the compound Z pamoate crystal form I, the medicine encapsulation efficiency is high, the burst release is low, the composition continuously releases the medicine in vivo for more than 2 weeks, the administration frequency of a patient can be reduced, and the medication compliance is improved.

The invention discloses a postoperative block analgesia curative effect evaluation system based on multi-modal data analysis, particularly relates to the technical field of medical information processing, and is used for solving the problem of poor analgesia evaluation standardization. An evaluation path of analgesia deviation quantification, state time sequence judgment and strategy generation is realized by constructing a structured comparison mechanism of an analgesia observation frame and an individualized analgesia baseline trajectory, pain, medication and activity deviations are dynamically calculated by fusing an anesthesia record, an analgesia pump log and clinical monitoring data, and an analgesia result is obtained. Typical states such as insufficient retardation effect are identified in combination with a time sequence inference model, a curative effect level and an early warning mark are automatically output, additional retardation, parameter adjustment and medicine remedy suggestions are generated according to a credibility level, and a state, disposal and result chain is constructed by continuously learning historical case data, so that the treatment efficiency is improved. The adaptive optimization of the baseline trajectory, the state threshold and the adjustment strategy is realized, and the individual matching performance, the response timeliness and the risk control capability of the analgesia strategy are improved.

The invention discloses an artificial intelligence-based whole doctor's advice reasonable medication auditing method and platform, and the method comprises the steps: integrating the structured data and unstructured text of a patient, carrying out the preprocessing according to a time sequence, determining a physiological safety range, and constructing a panoramic taboo doctor-seeing portrait; after deep information extraction and multi-source data fusion, a panoramic image of a patient is formed and is subjected to taboo comparison with medicine advised by a doctor, and a hard interception warning is triggered to avoid medicine conflict; further establishing a digital twinborn model, setting medical advices subjected to taboo comparison as a baseline scheme, simulating dynamic changes of physiological indexes within 72 hours, and assisting doctors to adjust drug doses in combination with clinical experience; finally, an individualized medication safety baseline is determined according to the simulation curve, and accurate simulation, dynamic early warning and scientific decision making are achieved. According to the scheme, the treatment safety and accuracy are remarkably improved, full-process intelligent medication auditing support is provided for complex patients, and the scheme has important clinical application value.

The invention relates to the technical field of artificial intelligence, in particular to a machine learning-based double-J tube indwelling membranous fungus infection risk medication decision support method, and aims to solve the problems of inaccurate double-J tube indwelling membranous fungus infection risk assessment and medication decision experience. According to the method, the machine learning model for predicting infection risks, pathogenic bacteria and drug curative effects is constructed by collecting multi-dimensional data and performing feature engineering, and personalized medication suggestions and visual decision support are provided in combination with a knowledge base and a rule engine. By adopting the scheme, accurate risk prediction can be realized, drug curative effect evaluation can be optimized, drug use reasonability and safety can be ensured, and clinical decision-making efficiency can be improved.

The invention provides a medication monitoring device and a medication monitoring method, relates to the field of medical apparatuses and instruments, and solves the technical problem that whether a capsule rotates to release medicines or not is difficult to monitor in a capsule type dry powder inhalation used in a transversely placed inhaler. The medication monitoring device comprises a shell and a photoelectric sensor, the photoelectric sensor is arranged on the shell, and a light path of the photoelectric sensor is configured to penetrate through the air inlet of the inhaler and the medicine bin, so that the rotation state of the capsule in the medicine bin is judged by detecting the change of the light path; the photoelectric sensor and the air inlet of the inhaler are arranged in a spaced mode in space so as to allow the inlet air flow to pass through. The rotation state of the capsule in the medicine bin is judged by detecting the change of the light path, whether the capsule rotates or not can be directly monitored, the problems that in the prior art, whether the capsule rotates or not cannot be known, and medicine taking failure caused by improper posture of the capsule cannot be judged are solved, effectiveness of medicine taking monitoring is ensured, and medicine taking guidance is facilitated.

The invention relates to the technical field of medical health monitoring, and discloses a urine volume detection system based on a strain gauge type sensor, which comprises a urine collection container, a strain gauge type sensor, an electromagnetic valve, a main controller and an infrared human body sensor, the upper port of the urine collecting container is used for receiving urine, and the lower port of the urine collecting container is connected with the strain gauge type sensor; the strain gauge type sensor is used for detecting the weight change of the urine collection container, the urine volume detection system based on the strain gauge type sensor and the intelligent closestool overcome the defect that a traditional closestool cannot measure and record the urine volume, convenience is brought to patients who do not want to use a urinal or a pedestal pan and old people with poor eyesight, and the intelligent closestool is convenient to use. A user can know the urine volume when using the closestool or pouring urine into the urinal into the closestool, use is convenient and sanitary, accurate urine volume data helps a doctor to formulate a more accurate medication scheme for a patient, and the patient is prevented from seeing a doctor repeatedly and the health risk caused by medicine problems.

The invention provides a medication guidance system and method based on knowledge graph and vector retrieval fusion, and relates to the technical field of artificial intelligence, and the medication guidance system comprises an application access layer, a service interface layer and a mixed retrieval engine layer; the application access layer is used for receiving external demand information and transmitting the external demand information to the large model platform; the service interface layer is used for receiving a query instruction of a large model platform, transmitting the query instruction to the mixed retrieval engine layer and transmitting a retrieval result to the large model platform, so that the large model platform outputs the retrieval result; the mixed retrieval engine layer is used for generating a graph database query statement and a query semantic vector according to the query instruction, performing structured query on a graph database according to the graph database query statement, performing semantic similarity retrieval in a vector database based on the query semantic vector, and generating a graph retrieval result and a vector retrieval result to the large model platform. Through the medication guidance system, specialized medication guidance can be realized.

This invention relates to the field of traditional Chinese medicine (TCM) technology, and more particularly to the application of TCM compositions in the preparation of drugs for the prevention and treatment of diabetes. The TCM composition of this invention includes *Sedum sarmentosum*, seaweed, oyster shell, hawthorn, *Siegesbeckia orientalis*, *Schisandra chinensis*, and *Polygonum cuspidatum*. This invention has found that this composition can significantly reduce blood glucose levels in type 2 diabetic rats and increase serum insulin levels and insulin sensitivity index in rats. Moreover, the compound combination drug for treating diabetes is an existing TCM preparation, safe to use, and its preparation method is simple, rapid, efficient, and easy to operate. The prepared mixtures, tablets, capsules, and micro-pills are easy to carry and convenient to take.

The invention belongs to the technical field of medical equipment, and particularly relates to a portable tuberculosis drug aerosol inhalation device. The device comprises a medicine storage chamber, an outflow channel and a controller, the top of the medicine storage chamber is provided with a medicine tank groove with a conical pricking pipe, and one-step medicine filling through placing and pressing can be achieved; the respiratory rhythm of a user is identified through lip contact and a respiratory airflow sensor, and the atomizer is synchronously controlled to start and stop; the system has the functions of liquid medicine identity recognition, heating temperature control and self-adaptive power adjustment based on liquid medicine storage, efficient, safe and comfortable tuberculosis medicine inhalation treatment is achieved, and the medication compliance and the treatment effect are remarkably 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 data processing, and provides an intelligent community health management system for senile chronic disease patients based on a radar map, and the system comprises the steps: regularly obtaining the monitoring data of a plurality of health indexes of the senile chronic disease patients; obtaining standardized data of each health index of the patient; determining the illness state reflecting degree of each health index of the patient; generating a radar map of each time of monitoring of the patient; determining the intervention degree of each monitoring of the patient according to the difference of the adjacent monitoring radar maps of the same patient and the change trend of the continuous monitoring radar maps; the medication adjustment degree of the patient is obtained, and then the illness state change degree of the patient is obtained; based on the disease change degree and the medication adjustment degree of the patient, obtaining health management suggestions for the patient; and the hospital timely shares the health management suggestions to the community after obtaining the health management suggestions of the patient. The objective of the invention is to solve the problem that health state monitoring management is affected by difference of radar map expressions caused by different illness states of patients.

The embodiment of the invention discloses a hospital infection aggregation risk early warning method and system based on patient medication intensity. The method comprises the following steps: acquiring basic information, medication records and inspection results of a patient; cleaning and standardizing data; based on the medication conditions in the short-term and long-term windows in the processing result, calculating medication intensity, and marking high-risk patients to obtain target patient data; performing rolling accumulation statistics on the target patient data, and determining a risk level through quartile anomaly detection or an inter-day mutation threshold method according to a statistical result; different early warning channels are automatically triggered according to the risk levels, and differential intervention measures are implemented. By implementing the method provided by the embodiment of the invention, the accuracy and the response speed of hospital infection prevention and control can be effectively improved, so that the infection risk and the medical cost of a patient are remarkably reduced, and the problem that the prior art lacks an effective means for performing real-time monitoring and individualized evaluation on the hospital infection risk is solved; and the technical problems of delayed prevention and control measures and low resource utilization efficiency are solved.

The invention provides an epinephrine automatic injection patch and method for cardiovascular first aid, and the injection patch comprises a flexible biosensing layer which obtains the subcutaneous impedance value and vital sign parameters of a patient; the microneedle array is used for adjusting the penetration depth based on the subcutaneous impedance value and controlling the deformation quantity through a piezoelectric actuator; the micro-fluidic pulse drug delivery module is connected with the micro-needle array and is used for executing pulse type drug injection according to the penetration depth; and the control module is used for judging an autonomous circulation recovery state based on the vital sign parameters and triggering the microfluidic pulse administration module to stop injection. According to the application, by constructing a first-aid closed loop of'multi-mode physical sign dynamic monitoring-self-adaptive microneedle administration-ROSC intelligent termination ', the response speed of epinephrine injection is increased, the bioavailability is improved, the risk of excessive medication is reduced, and the gold rescue efficiency and the neurological function prognosis rate of pre-hospital cardiac arrest patients are remarkably improved.

The invention discloses an upatinib emulsifiable paste and a preparation method thereof, the emulsifiable paste is directly smeared on an affected part, administration and use are convenient, diseases are rapidly relieved, the number of treatment times is reduced, the influence of a gastrointestinal tract absorption way on a medicine is reduced, toxic and side effects of gastrointestinal tract administration are reduced, the upatinib emulsifiable paste fills the market blank, administration of a patient is facilitated, and the curative effect of the upatinib emulsifiable paste is improved. And the market prospect is wide.

The invention relates to the technical field of intelligent medical treatment, in particular to a chronic disease follow-up visit data management method based on artificial intelligence, which comprises the following steps: collecting physiological parameter data, behavior activity data and medication record data of a patient through a medical equipment terminal; performing time sequence standardization processing on the collected data to generate a data set divided according to a preset time period; setting a first abnormal threshold value based on a clinical medicine guide, and setting a second abnormal threshold value based on historical data statistical analysis; when the data exceeds a first abnormal threshold value, generating a red early warning identifier, and when the data exceeds a second abnormal threshold value, generating an orange early warning identifier; and executing grading response according to the early warning grade. According to the method, a double-threshold cooperation mechanism and clinical threshold and statistical boundary cross validation are utilized, personalized chronic disease data management and dynamic adjustment can be carried out on different patients, monitoring of the patients can be accurately adjusted, and the medical diagnosis effect of the chronic disease patients is improved.

The invention discloses a method for adjusting administration dosage according to adverse reaction risk based on a joint model, and is applied to the field of intelligent medical treatment. In the prior art, the relationship between the sirolimus steady-state valley concentration and the specific adverse reaction cannot be quantified, so that the defect of lack of accurate guidance for clinical medication is caused. Through systematic research, the method determines the individual target valley concentration range through a BKMR model for the first time, obtains individual pharmacokinetic parameters of a patient through a group pharmacokinetic model, and finally determines the medication time and the medication dosage of the patient through Bayesian negative feedback simulation, and the medication time and the medication dosage are used for adjusting a medication scheme. According to the invention, prospective risk prediction of adverse reactions of sirolimus is realized, the treatment safety of children with vascular diseases is remarkably improved, and long-term prognosis of patients is improved.

A cylindrical storage-organizer-dispenser device for solid dose medications, arranged in a 7-day by time-interval-dose matrix, specifically invented to offer a simple, user-friendly, and secure way for people who need to take multiple daily medications on a weekly basis. The device permits to visually see what medications have been already taken and implicitly indexes to the next upcoming dose; this helps to avoid missing a dose or taking dangerous double doses. Additionally the device provides secure storage for emergency medication, specific patient information and / or medication instructions, and includes a feature to securely carry it around.

The invention relates to the technical field of old-age service management, in particular to an integrated old-age service management system based on end-cloud collaboration, and aims to solve the problems that in the prior art, multi-source data of physical signs, medication and living habits cannot be integrated to predict the health change trend, abnormal fluctuation of four high cannot be perceived in advance, short-term risks cannot be pre-judged, and the service quality is poor. And a personalized intervention scheme cannot be formulated in combination with reinforcement learning. Multi-source data of physical signs, medication and living habits are integrated through the chronic disease management module, LSTM is used for predicting the health change trend, four-high abnormal fluctuation is perceived in advance, short-term risks are pre-judged, a personalized intervention scheme is formulated in combination with reinforcement learning, diet and exercise suggestions and medication reminding are covered, and the system has the advantages of being simple in structure and convenient to use. User compliance is improved by outputting custom prompts through natural languages, the system forms a complete closed loop, the system is suitable for long-term management of diabetes and hypertension diseases, the risk of acute events is reduced, and the self-management ability of patients is enhanced.

The invention relates to the technical field of intelligent medical treatment and remote medical treatment, and discloses a Parkinson's disease patient home information management system based on an internet hospital. Comprising the following steps: (1) a multi-source illness state acquisition module for monitoring illness states in real time by combining wearable equipment and a mobile terminal through a diary recording template and scale self-evaluation based on an internet hospital; (2) a medication management module which comprises medication reminding, illness change and side effect recording; (3) a data analysis and early warning module which uses incremental learning and model fine tuning technologies, optimizes a prediction model, analyzes the correlation between medication parameters and illness state changes, and realizes early warning of illness state deterioration; and (4) a remote collaborative diagnosis and treatment module which constructs a visual data platform, establishes a two-way communication channel and realizes immediate intervention. The system is based on an internet hospital, and an intelligent solution is provided for long-range management of Parkinson's disease by innovatively constructing a'monitoring-analysis-early warning-intervention 'closed-loop management system.

The invention belongs to the field of intelligent medical treatment, and particularly relates to an individualized administration method for treating a PID child patient by combining sirolimus with voriconazole. The method comprises the following steps: acquiring the age of a PID child patient; based on the age of the PID child patient, determining the dosage of sirolimus when the voriconam is combined, and if the age of the PID child patient is in a [1, 6] year interval, the dosage is [0.100, 0.330] mg / QD; if the age is in the interval of [6, 13], the dosage is [0.280, 0.540] mg / QD. According to the method, the PPK model is formulated for the first time when the PID child patient is combined with the VRC in the SRL mode, the optimal administration dosage is explored based on PPK when the PID child patient is combined with the VRC in the SRL mode, and simple medication dosage guidance is provided for child patients of different age groups; individualized and customized SRL medication guidance can be provided for individuals through a posterior Bayesian method.

The invention discloses a drug use knowledge intelligent question answering system and method combined with a knowledge graph, belongs to the technical field of drug use management, and solves the problems that only single-path matching is supported and complex multi-hop query cannot be processed when an answer is determined by adopting similarity matching between a regularized question and a template library in an existing method; in order to solve the problem that personalized accurate recommendation cannot be realized according to actual conditions of different patients, the method comprises the steps of performing multi-modal analysis on drug use demands of the patients, generating a structured demand query graph, pre-constructing a drug use question and answer model based on a dynamic knowledge graph, and performing multi-dimensional parallel evaluation on a candidate answer set. According to the method, multi-modal analysis is carried out on the medication demand of the patient, and multi-dimensional parallel evaluation is carried out on the candidate answer set, so that complex multi-hop query can be processed, diversified demands of the patient can be met more comprehensively, the personalized degree and practicability of the answers can be improved, and personalized accurate recommendation can be realized according to actual conditions of different patients.