1122 results about "Drug utilisation" patented technology

The invention relates to the field of antitumor drugs, in particular to a pharmaceutical composition of a KRAS G12C inhibitor and Ly2090314 and application of the pharmaceutical composition. According to the invention, the Ly2090314 and the KRAS G12C inhibitor are combined to play a synergistic effect, a DNA damage repair pathway is inhibited, a better inhibition effect is generated on tumor cells containing KRAS G12C mutation, the dosage required for achieving the same anti-tumor effect is greatly reduced, and the medication safety is improved.

The invention discloses an AI-based traditional Chinese medicine dialectical auxiliary diagnosis method and system and a medium thereof, and relates to the technical field of intelligent auxiliary diagnosis, and the method comprises the steps: collecting physiological monitoring data, tongue condition data and pulse condition signals of a patient, obtaining patient symptom information, mapping the patient symptom information to a preset traditional Chinese medicine pathogenesis classification model, and generating an initial symptom feature vector; calling a knowledge base containing traditional Chinese medicine prescription rules and compatibility medication taboo, performing data space-time alignment processing and dynamic combination calculation on the initial symptom feature vector, and generating a personalized candidate prescription set conforming to the traditional Chinese medicine compatibility taboo; according to dynamic combination calculation, a solution algorithm based on a constraint satisfaction problem is adopted, and multi-dimensional association rule matching is carried out in combination with the mapping relation between syndromes and modern medical symptoms; and outputting a prescription medication recommendation scheme containing traditional Chinese medicine compatibility, dosage and decoction methods based on the candidate prescription set. The method supports the formulation of an accurate personalized diagnosis and treatment scheme, and meets the requirements of high-quality traditional Chinese medicine personalized auxiliary diagnosis and treatment.

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 relates to the technical field of tumor treatment, in particular to an intelligent decision-making system for tumor precise targeted medication based on individual mutation information, which comprises a data processing layer, a variation annotation and function prediction layer, a knowledge base integration layer and a scheme decision-making engine and report visualization module. According to the intelligent decision-making system for tumor precise targeted medication based on individual mutation information, a rule engine and a prediction model are combined, dynamic priority ranking is output, multi-model fusion decision making is achieved, and clinical scene deep adaptation is achieved by predicting primary and secondary drug resistance, calculating liver and kidney function adjusting dosage and generating a combined medication time sequence scheme; through an individualized drug delivery scheme, combination drug use optimization is achieved, a visual clinical report is generated, clinical executable operation is further strengthened, and through algorithm quantification, a dynamic knowledge graph, AI auxiliary decision making and a clinical operation closed loop, the next-generation technical research direction of a tumor precise drug use system can be represented.

The invention provides a multi-source medical outpatient service data comprehensive management method and platform based on RPA, and the method comprises the steps: S1, designing a script based on robot process automation, and carrying out the data collection and storage of multiple systems; s2, constructing a knowledge graph # imgabs0 # according to the structured data set; s3, introducing a causal inference technology into the knowledge graph # imgabs1 #; s4, according to the abnormal mark knowledge graph # imgabs2, generating personalized medicine use optimization suggestions through joint learning of a graph structure and node attributes; and S5, implementing closed-loop management by executing optimization suggestions and dynamically updating system rules. According to the method, the defects of a traditional method in the aspects of data integration, drug use anomaly detection and optimization suggestion generation can be overcome.

The invention discloses a multi-modal fusion-based traditional Chinese medicine recommendation method, system and device and a medium, and the method comprises the steps: constructing a heterogeneous graph according to a core representative object and preprocessed traditional Chinese medicine multi-modal data; inputting the heterogeneous graph into a graph neural network for data fusion to obtain traditional Chinese medicine multi-modal fusion data; constructing a knowledge graph based on the traditional Chinese medicine multi-modal fusion data to obtain a traditional Chinese medicine knowledge graph; segmenting the traditional Chinese medicine knowledge graph into sub-graphs processed by multiple agents according to the drug effect and the drug use safety, and obtaining a medicinal material sub-graph associated with a drug effect agent and a safety sub-graph associated with a safety agent; based on the medicinal material sub-graph and the safety sub-graph, collaborative iteration optimization is carried out through a medicinal material combination strategy and a medicinal material safety checking strategy to select medicinal materials, and an optimal compound recommended by the traditional Chinese medicine is obtained; and inputting the optimal compound into the fine-tuned large language model, and determining a target compound recommended by the traditional Chinese medicine. The accuracy of traditional Chinese medicine recommendation can be improved.

The present invention relates to the field of medical artificial intelligence technology, and discloses a pediatric procedural sedation medication regimen recommendation system based on multi-objective optimization, comprising: a data acquisition and preprocessing module, which is used to acquire multi-source heterogeneous clinical data and perform data cleaning and feature extraction to generate a multi-source heterogeneous clinical feature set; a multi-objective optimization decision module, which is used to establish a number of objective functions, and adopt an improved multi-objective optimization method to optimize and solve each objective function to obtain an optimal medication regimen; a medication regimen module, which is used to automatically generate a specific medication regimen based on a scheme generation method with time constraints according to the optimal medication regimen; a safety inspection module, which is used to perform a multi-level safety review on a specific medication regimen, and obtain a multi-level safety review result by calculating a feasibility judgment function through risk assessment; the present invention improves the scientificity and reliability of the medication regimen, so that each patient child can obtain a sedation regimen that best suits his or her individual characteristics.

The invention belongs to the technical field of medical informatization, and provides an anesthesia medication management system with biological recognition, comprising: a biological recognition module verifies the identities of medical staff and patients through a vein recognition technology; the multi-source data acquisition module acquires vital sign data and equipment parameters of a patient in real time to obtain multi-source data; the intelligent drug management module authorizes to store and take drugs according to the biological recognition result and generates an electronic anesthesia prescription; the processing decision module receives the multi-source data and generates a medicine adjusting instruction through a preset rule and an algorithm in combination with an electronic anesthesia prescription; the execution control module carries out secondary confirmation on the medicine adjusting instruction through biological recognition and then sends the medicine adjusting instruction to anesthetic infusion equipment to be executed; according to the method, through the electroencephalogram double-frequency index, the entropy index and the hemodynamic parameters, in combination with the anesthesia depth evaluation result and the personalized drug metabolism and effect model, the medication scheme is dynamically adjusted, the optimal infusion rate is generated, the anesthesia depth control is more accurate, and the fluctuation range is narrowed.

The invention discloses a chronic disease intervention safety detection system and method based on large model multi-agent cooperation, and relates to the technical field of natural language processing and multi-agent cooperation in the medical health field. A standardized interface and an adaptive cleaning algorithm are adopted to realize data desensitization, missing value filling and format unification, and a dynamically updated patient health portrait is constructed; complex tasks are decomposed based on a chain reasoning technology, special tools such as a drug interaction detection tool and a nutrition gap calculation engine are developed, and a clinical knowledge base is integrated for parallel analysis; suggestion conflicts are eliminated through a multi-agent debate mechanism, the priority is calculated in combination with a weight rule, manual auditing is triggered to process low-confidence disputes, and finally a structured health report containing medication adjustment, diet optimization and behavior intervention is generated. The limitation of a traditional method in data integration, cross-domain reasoning and conflict resolution is solved.

The invention relates to the technical field of medical information, and discloses an internet-based remote medical inquiry system, which comprises a data acquisition and quality assurance module for acquiring multi-source heterogeneous medical data and performing time sequence alignment, quality monitoring and intelligent repair interpolation; the feature extraction and fusion module is used for performing deep feature extraction, cross-modal semantic alignment and hierarchical attention fusion; the complication association reasoning module is used for obtaining a deep complication association reasoning result by adopting a graph attention network and multi-hop reasoning; the complication progress prediction module is used for constructing a complication progress prediction model and carrying out meta-learning enhancement and uncertainty quantification; the intelligent medication decision module is used for generating candidate schemes and screening a Pareto optimal scheme; the compliance management module is used for carrying out compliance causal inference and closed-loop optimization; the effect evaluation module is used for carrying out effect evaluation and dynamic optimization; according to the method, a compliance improvement mechanism is established through causal inference and reinforcement learning, and interpretable man-machine collaborative decision and closed-loop optimization management are realized.

The invention relates to a nitric oxide data analysis method and system for bronchial asthma assessment. According to the method, exhaled air nitric oxide and pulmonary alveolar nitric oxide of a patient, omics data and clinical information are acquired in a standardized manner, and multi-dimensional features are constructed after data cleaning and fusion; comprise an inflammation level index, an inflammation region entropy, an inflammation synergy index, a glucocorticoid response factor, an infection-smoking synergy influence factor and a heredity-symptom distribution index. And outputting initial risk assessment based on an explainable elevator EBM model, and finally generating an asthma risk probability through a weighted integration formula to realize three-level layering: low / medium / high risk. The system dynamically associates treatment decisions, for example, the drug dosage and the monitoring frequency are improved when the risk is upgraded, and the medication scheme is optimized when the risk is degraded. According to the scheme, the limitation of traditional single marker static analysis is broken through, multiple mechanisms of dissection, immunity and pharmacology are fused, the evaluation precision and clinical applicability are remarkably improved, the acute attack rate is reduced, and accurate typing treatment is guided.

The invention provides a ward rotation evaluation and model construction method based on multi-modal information, and belongs to the technical field of deep learning. The method comprises the following steps: firstly, constructing a multi-modal clinical data set, integrating five types of heterogeneous data including operation records, vital sign time sequence data, medical images, medication records and nursing logs, labeling postoperative state labels of patients, and simulating a clinical real scene to generate a training set and a test set containing random missing modals; secondly, constructing a ward rotation evaluation model, wherein the model is used for improving and designing a traditional teacher-student network architecture into a double-path heterogeneous distillation model; meanwhile, designing a multi-task loss joint optimization strategy for aligning the characteristics of the teacher-student network and constraining the evaluation result of the teacher network, and finally deploying a trained model to an edge computing device, receiving a multi-modal data stream in real time and outputting a transfer suggestion and a risk score; the invention provides a brand new intelligent solution under a ward rotation scene, and improves the evaluation robustness of a multi-modal missing scene.

The invention relates to the technical field of health management of old people, and discloses a method and a system for predicting multiple diseases of old people. The method comprises the following steps: acquiring multi-modal health monitoring data of a target old person in a preset time period, wherein the multi-modal health monitoring data comprises physiological index time sequence data, a medication record sequence and a daily activity ability evaluation result; and performing cross-modal correlation analysis on the multi-modal health monitoring data to generate a disease interaction characteristic matrix containing metabolic disease correlation degree, circulatory system disease coordination index and neurodegenerative disease progress rate. And performing hierarchical clustering processing on the feature matrix by adopting a dynamic weight distribution algorithm, and outputting potential common disease combinations of the target old people and a priority score of each common disease combination. And generating a personalized intervention strategy set including a drug interaction avoidance scheme, a rehabilitation training intensity adjustment scheme and a nutrition intake ratio scheme according to the priority score.

The invention relates to the technical field of medication reminding, in particular to an intelligent reminding method for nursing medication. The method comprises the steps of obtaining a medication compliance coefficient according to the difference between a medication time interval of medication of a patient in a current time period and a preset medication time interval and the disorder condition of medication time of the patient; according to the correlation condition between the medication time interval of medication of the patient and the health index and the fluctuation condition of the medication time interval, the medication behavior sensitivity coefficient is obtained, and then the influence coefficient of the medication compliance behavior is determined. And determining a medication behavior deviation coefficient according to the difference between the proportioning amount of the medicines taken by the patient in the current time period and a preset proportioning amount, the fluctuation condition of the proportioning amount of each medicine, the medication compliance coefficient and the influence coefficient of the medication compliance behavior, and further adjusting the medication automatic reminding frequency. According to the invention, the self-adaptive adjustment of the medication reminding frequency of the patient is realized.

Systems and methods for providing recommended doses and instructions for pharmaceutical treatments, and more particularly systems and methods for measuring physiological and / or electrophysiological signals from a subject and providing recommended doses and instructions of pharmaceuticals based at least in part on the measured signals are described. Further, the recommended doses and instructions are based at least in part on a subject's disease(s), disorder(s), or injuries and / or symptom(s) of such, and / or side effects of medications or treatments the subject is receiving. The recommended doses and instructions are personally tailored to the subject to precisely and personally address the subject's needs, and may be based in part on correlation with database(s) comprising historical data of the subject and / or other subjects to treat the subject's movement disorders, injuries to the body or brain, cognitive diseases and disorders, sleep diseases and disorders, chronic pain, or other conditions.

The invention relates to the technical field of anesthesia, and discloses a personalized anesthesia scheme generation method, which comprises the following steps: S1, extracting and coding patient features; s2, modeling a pharmacokinetic model; s3, drug concentration prediction; s4, applying a PD model; s5, performing uncertainty quantification; s6, predicting and outputting; s7, updating and feeding back parameters; s8, verifying the model; in the personalized anesthesia scheme generation process, firstly, effective feature extraction and coding are carried out on multi-modal data of a patient; according to the personalized anesthesia scheme generation method, efficient feature extraction is carried out on multi-modal data (such as electronic health records, genome information and real-time monitored vital signs) through VAE and other deep learning methods, and the differences of patients in the aspects of physique, drug metabolism capability, gene background and the like can be fully described; therefore, the optimal anesthetic medication strategy is customized for each patient.

The invention relates to the technical field of intelligent medical treatment, and discloses a medical large model driven cross-department collaborative prescription generation method and system. According to the method, electronic medical records of patients and prescription data of multiple departments are acquired, key information is structurally extracted to generate directional vectors, and transfer learning and fine adjustment are performed by using historical conflict cases and a drug knowledge graph based on a medical basic large model and an AI chip, so that a collaborative prescription model is constructed. The model can identify drug incompatibility and dosage risks among departments and generate a prescription suggestion set. A doctor can dynamically correct a prescription based on feedback, a collaborative report containing a medication time sequence, a monitoring index and an emergency scheme is generated after multiple rounds of collaborative optimization, and the safety and effectiveness of multi-department combined medication of complex diseases are remarkably improved.

The invention discloses a medication assisting system and device based on symptom-medicine two-way verification, and belongs to the technical field of intelligent medical treatment. The system comprises a data preprocessing module, an entity extraction and alignment module, an atlas retrieval module, a case retrieval module and a bidirectional verification and auxiliary decision module. The system extracts a medical entity through an AC automaton rule engine and a knowledge enhanced BERT model, and realizes mapping with a knowledge graph entity by adopting a two-stage term alignment strategy; performing knowledge graph query by adopting an operation chain generation mechanism driven by cue words to obtain a candidate drug set; meanwhile, similar cases are retrieved based on vector similarity, and an associated drug set is generated; and finally, fusing external medicine knowledge base data, executing a bidirectional verification and completion strategy, and outputting medicine recommendation with clinical safety and coverage range. The invention further provides a device structure for realizing the functions of the system, and the accuracy and interpretability of clinical medication aid decision making are effectively improved.

The invention relates to the technical field of treatment of chronic diseases of old people, and discloses a metabolic characteristic-based medicine dosage optimization method for chronic diseases of old people. The method comprises the following steps: acquiring drug metabolism parameters (including a plasma concentration peak value, a half-life period curve and the like) and organ function data (including a hepatocyte metabolism rate, a glomerular filtration rate and the like) of a plurality of monitoring nodes, arranging the drug metabolism parameters and the organ function data into a time sequence input vector, and extracting a dynamic feature vector by using a time convolution network and an adaptive filter network; performing feature crossing, pharmacokinetic constraint correction and feature enhancement processing, performing fusion to generate a joint feature vector, inputting the joint feature vector into a dose decision model to obtain an adjustment coefficient, and generating a drug dose interval with a safety threshold in combination with a historical drug use record. According to the method, multi-dimensional data integration and dynamic modeling are realized, the accuracy and safety of chronic disease medication of old people are improved, and the method is suitable for individualized treatment.

The invention relates to the field of intelligent medical services, and discloses an intelligent medicine shopping guide and recommendation system for a drugstore, which comprises a user information collection module, a medicine database module, a symptom matching module, a recommendation optimization module and a result output module, the user information collection module is used for receiving health data and personalized requirements input by a user and converting the health data and the personalized requirements into structured user feature data; the drug database module stores components, indications, prices and drug interaction information of drugs, and constructs a drug interaction knowledge graph for representing an interaction relationship between drugs and a risk thereof. By introducing a drug interaction knowledge graph, the interaction risk between drugs is dynamically evaluated, and the risk information is fused into a recommendation decision. Compared with a recommendation method which only depends on static rule matching in the prior art, the problem that potential drug conflicts in recommendation results are difficult to find is effectively solved, and the drug use safety is improved.

The invention relates to the technical field of medical report generation, in particular to an intelligent anesthesia record sheet automatic generation system. According to the system, patient information is automatically recognized through the OCR unit, and real-time collection and structured storage of physical sign data, medication records and surgical operation are achieved in combination with a medical equipment interface and a voice recognition technology; time sequence data analysis and multi-source data fusion modeling are adopted to construct a medical event causal relationship graph so as to improve the accuracy of anomaly recognition and patient sign evaluation; based on a time axis construction unit and a structured template engine, an anesthesia record list is automatically analyzed and filled, and the manual input workload is greatly reduced; and finally, in combination with block chain evidence storage and a multi-level encryption technology, the security and non-tampering property of the anesthesia record list are ensured, the reliability of medical data management is improved, the recording, monitoring and storage efficiency of the anesthesia process is comprehensively optimized, and the medical quality and the patient safety are improved.

The invention discloses a hospital national class medicine electronic prescription circulation management method and system, and the method comprises the steps: generating standardized electronic prescription data through obtaining the medicine information of a patient, and judging the inventory condition of a hospital through a medicine inventory matching algorithm. And if there is no inventory, transmitting the prescription data to a drugstore system, matching the medicines by adopting an automatic identification technology, and completing allocation. Afterwards, a logistics distribution algorithm is applied to determine an optimal distribution scheme, and real-time distribution information is updated to a patient side and a hospital side. The patient can acquire the distribution information through the mobile application program, select the distribution mode and confirm receiving after receiving the medicine, so that closed-loop management of prescription circulation is realized. According to the invention, links such as electronic prescription, intelligent allocation, logistics distribution and the like are integrated, so that the prescription circulation efficiency is improved, the medication experience of patients is optimized, and the whole-process intelligent management from prescription extraction to distribution is realized.

The invention discloses a multi-source data-based medical temperature-adjusting atomization drug delivery system for the pneumology department, relates to the technical field of medical temperature-adjusting atomization drug delivery, and aims to solve the problems that the atomization effect is poor and the atomization process is not intelligent. By adjusting atomization power in different respiratory cycle stages, monitoring airway abnormity and respiratory fluctuation in real time to correct parameters and adapting temperature control and atomization components as required, medicine waste and airway stimulation can be reduced, variables such as air source fluctuation and liquid medicine viscosity can be coped, atomization stability is guaranteed, administration efficiency and safety are improved, and the method is suitable for large-scale popularization and application. The requirements of patients with weak respiratory functions are met, the basic information unit is linked with the medical history of the patients and the medicine information to determine the safe temperature, the atomization rhythm is matched in combination with the respiratory parameters, temperature deviation caused by information dispersion can be avoided, manual errors are reduced, and the medication safety is improved.

The invention relates to the technical field of medical health management, and discloses an endocrine patient health management system based on big data. According to the system, a multi-source data acquisition module is adopted to acquire heterogeneous health data including physiological monitoring indexes, medication record data, medical image reports and the like, and living habit logs can also be included. The physiological feature noise reduction module is used for extracting steady-state physiological features, the multi-modal fusion module is used for generating a focus dynamic map, and the medication feedback analysis module is used for generating a drug response mode evolution sequence. The data are input into a multi-dimensional decision model, and a health management scheme is output by a personalized scheme generation module. And the living habit analysis module analyzes the living habit logs to generate influence factors, and participates in making a health management scheme. The system realizes multi-source data fusion analysis, can provide a personalized health management scheme for an endocrine patient, improves the disease diagnosis accuracy and treatment effect, and improves the life quality of the patient.

The invention relates to the technical field of medical information processing, in particular to a type 2 diabetes precise typing and dynamic treatment system which comprises a clinical feature extraction module, a metabolism dynamic modeling module, a precise typing module, a treatment parameter generation module and a dynamic treatment output module. Wherein the clinical feature extraction module is used for collecting a multi-dimensional clinical data set of a patient for continuous 14 days; the metabolism dynamic modeling module is used for extracting blood glucose fluctuation characteristics and pancreas islet function compensation characteristics; the precise typing module is used for dividing metabolism-driven subtypes to which the patients belong through a clustering algorithm; the treatment parameter generation module is used for mapping a subtype-treatment rule base and outputting a matched personalized treatment parameter combination; and the dynamic treatment output module outputs a dynamic treatment scheme. According to the invention, through a dynamic treatment mechanism based on typing, linkage control of individual difference identification and real-time adjustment of the medication scheme of the type 2 diabetes patients is realized, and the accuracy and adaptability of treatment are improved.

The invention relates to the technical field of information processing, in particular to a medicine conflict automatic detection and prescription optimization system for old multi-disease patients. According to the system, a data management module is used for collecting individual data of patients and group data of co-diseased reference groups and receiving a planned medication scheme; the parameter calculation module is used for identifying a target medicine combination with time overlapping in the patient medication record and the planned medication scheme; determining a basic risk index according to the group medication response data, the patient medication record and the work and rest text data; determining a lag risk coefficient according to the physiological indexes and the drug metabolism data; determining group medication associated risk parameters according to atypical reactions and group medication records in the medication reaction data; the risk fusion module is used for determining a drug use conflict risk value based on the basic risk index, the lagging risk coefficient and the group drug use associated risk parameter; and the prescription optimization module is used for generating an optimal medication scheme, so that the generated optimal scheme is more suitable for the actual life of the patient.

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 provides a drug dosage adjusting system and method based on blood concentration prediction, and belongs to the field of medical artificial intelligence, and the method comprises the steps: obtaining a standard structured data code based on a preset historical clinical database; performing standard structured data coding based on the predicted feature factor set to obtain a predicted feature factor set and a time point combination; constructing a classification prediction model and a regression prediction model based on the prediction feature factor set; a to-be-predicted blood sample is collected in a prediction characteristic factor set time point combination mode, and a blood concentration prediction result is obtained based on the to-be-predicted blood sample of the prediction characteristic factor set, the prediction characteristic factor set classification prediction model and the regression prediction model; and adjusting the dosage according to the blood concentration prediction result. According to the invention, individual differences are analyzed individually, personalized medication dosage adjustment suggestions are generated, and the medication dosage adjustment reliability is improved.

The invention discloses an intelligent medication consultation method and device based on a large model. The method comprises the following steps: acquiring user input information; and under the condition that the user input information is a text, performing feature extraction on the text by adopting a pre-training language model BGE to obtain a first feature representation, performing block retrieval on the electronic specification of the medicine based on the first feature representation, and performing context integration on a block retrieval result to obtain a second feature representation of the electronic specification of the medicine. Generating medication consultation information for responding to the information input by the user; under the condition that the user input information is a picture, medicine box detection is conducted on the picture, a target medicine box is recognized, cross-modal feature extraction is conducted on the basis of the target medicine box, second feature representation is obtained, cross-modal retrieval is conducted on the basis of the second feature representation, and medicine use consultation information used for responding to the user input information is generated. The technical problems of low safety, accuracy and convenience of medication management in the prior art are solved.

The invention provides a dynamic medication dosage optimization method fused with reinforcement learning. The method comprises the following steps: acquiring a first physiological index parameter of a target patient; obtaining a first diagnosis report of the target patient, wherein the first diagnosis report comprises basic information of the target patient and first disease information of the target patient; the first disease information comprises a first disease type and first disease description information; determining a first reinforcement learning algorithm corresponding to the first disease type; determining a first control parameter of the first reinforcement learning algorithm according to the first physiological index parameter and the basic information; and performing operation on the first disease description information through the first reinforcement learning algorithm and the first control parameter to obtain a first medication dosage parameter. Based on the application, the drug effect can be consistent with the physical condition of a patient, and poor drug effect and excessive side effects caused by too strong drug effect are avoided.