2520results about "Drug references" patented technology

The invention discloses a ligand information generation model training method and device and a ligand information generation method and device, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring sample receptor information and sample ligand information, wherein the binding affinity between a ligand described by the sample ligand information and a receptor described by the sample receptor information is not less than a set affinity; denoising the reference noise data based on the sample receptor information through a to-be-trained neural network model to obtain predicted ligand information; determining a first loss for characterizing a difference between the sample ligand information and the predicted ligand information; and training the neural network model based on the first loss to obtain a ligand information generation model. The reference ligand information can be generated based on the reference receptor information through the ligand information generation model, and the binding affinity between the ligand described by the reference ligand information and the receptor described by the reference receptor information is high.

The invention relates to a drug-disease relationship prediction method and system based on a dual-channel fusion knowledge graph. The method comprises the following steps: S1, constructing a biomedical fusion knowledge graph; s2, constructing a drug-disease sub-graph based on the biomedicine fusion knowledge graph; s3, constructing a dual-channel adaptive fusion feature module, and embedding a drug-disease sub-graph; s4, performing enhanced splicing on the drug-disease sub-graphs in the dual-channel fusion knowledge graph pre-embedded network, importing an edge-node iterative updating learning mechanism, training a spliced sub-graph relation perception learning network, and obtaining enhanced sub-graph feature embedding; and S5, utilizing the trained enhanced subgraph features to embed, calculate and output the prediction probability of the drug-disease relationship. According to the method, the semantic representation capability of the knowledge graph and the topological modeling advantages of the graph neural network are integrated, and efficient and accurate prediction of the drug-disease relationship is realized through multi-modal feature interaction, sub-graph enhancement and a dynamic sub-graph learning mechanism.

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

The invention 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 cardiovascular disease treatment, and discloses a cardiovascular disease medical decision support method based on a large model, and the method comprises the steps: obtaining multi-modal cardiovascular disease medical data, constructing a multi-source heterogeneous data set, carrying out the preprocessing and feature fusion, and constructing a combined representation model based on the pre-training large model and the fusion of traditional and western medicine knowledge. And designing a hierarchical space-time diagram convolutional network to generate a dynamic physiological state code of the patient, and constructing a multi-target optimization decision tree to generate a treatment scheme containing western medicines, traditional Chinese medicine prescriptions and non-drug intervention. A federal learning framework is adopted to cooperatively train a local model, data security is guaranteed, a global model is optimized, and a curative effect attribution module is further arranged to correct decision deviation. According to the method, multi-modal data and traditional Chinese and western medicine knowledge are integrated, personalized treatment scheme generation is achieved, data safety is guaranteed, and the cardiovascular disease diagnosis and treatment level is improved.

The invention belongs to the technical field of medical resource intelligent management, and relates to a hospital comprehensive monitoring analysis method and platform based on artificial intelligence, and the method comprises the following steps: generating a medicine management basic data set; generating a primary prediction signal; adjusting a prediction weight according to the conflict level and outputting a reliable demand prediction instruction; a quantitative evaluation value reflecting the medicine turnover efficiency, the immediate pressure and the cost loss is calculated, and when the quantitative evaluation value exceeds a preset safety alert threshold value, an inventory abnormity alarm signal is generated; outputting an allocation strategy instruction containing the medicine flow direction, the allocation quantity and the time efficiency; and comparing a deviation value between the reliable demand prediction instruction and the allocation strategy instruction, and when the deviation value continuously exceeds a preset deviation threshold value, triggering an update prompt of the drug safety use limit rule base. The problem that demand prediction and clinical rule verification are separated in a traditional mode is solved.

The invention provides a method for drug screening and disease model construction of liver organoid, relates to the technical field of medical research, and adopts multi-modal data acquisition, fusion and intelligent analysis and quantum computing processing to optimize comprehensive application of data, so that the richness, accuracy and analysis efficiency of the data are remarkably improved, and the risk of drug screening and disease model construction of liver organoid is reduced. Multi-source data of genomics, transcriptomics and the like are collected comprehensively, an accurate research system of immune-hepatic disease interaction based on organoid is constructed, an in-vivo complex immune-hepatic disease microenvironment can be highly simulated, the proportion and activation state of immune cells are accurately regulated and controlled in an in-vitro model, functional and phenotypic changes of the immune cells are monitored in real time, and the immune-hepatic disease interaction accuracy is improved. According to the present invention, the interaction mechanism between the immune cells and the liver organ cells is deeply revealed, such that the accurate information basis is provided for the accurate determination of the drug action target, the development of the innovative drug for the immune check point or tumor microenvironment regulation is easily achieved, and the pertinence and the success rate of the drug research and development are substantially 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.

Methods, apparatus, systems, and articles of manufacture are disclosed for generation and application of models for therapeutic prediction and processing. A balance of precision and recall can be applied to at least one of a toxicity-related model or an efficacy-related model to configure immunotherapy treatment of a patient and / or cohort of patients. Model output can be evaluated differently depending on a determine patient selection criterion to trigger different actions.

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 invention discloses a construction method of a traditional Chinese medicine alert question-answering system based on a large language model. The method comprises the following steps: establishing a traditional Chinese medicine knowledge base; constructing a retrieval enhancement generation model PV-RAG oriented to drug alert, and training and optimizing the PV-RAG model by taking the traditional Chinese medicine knowledge base as a training set; the PV-RAG model comprises a retrieval module, a text extraction module, a query rewriting module and a generation module; and constructing an intelligent question and answer application end, and generating a traditional Chinese medicine alert answer according to a natural language question input by a user. According to the traditional Chinese medicine alert question-answering system based on the large language model and the retrieval enhancement generation technology, deep semantic understanding of traditional Chinese medicine literatures is achieved through the text embedding model, multi-source data such as traditional Chinese medicine specifications, traditional Chinese medicine classics and clinical research literatures are effectively integrated, and the traditional Chinese medicine alert question-answering system is established. And rich knowledge support is provided for traditional Chinese medicine alert questions and answers.

The invention discloses an optimization analysis system for simulating an anesthesia process, relates to the technical field of anesthesia simulation optimization analysis, and is used for solving the problem of inaccurate anesthesia individualized simulation decision. By constructing an anesthesia simulation system fusing a pharmacokinetic and pharmacodynamic model and a physiological coupling model, dynamic process simulation under individual physiological states and operative characteristics is realized, state evolution trajectories of an induction period, a maintenance period and a resuscitation period can be accurately deduced, and by introducing deep learning calculation and stage continuity constraints, the accuracy of the state evolution trajectories of the induction period, the maintenance period and the resuscitation period is improved. The method comprises the following steps of: establishing a multi-path drug delivery and ventilation strategy, setting a risk-efficiency scoring function, completing state tracking and index evaluation of the whole process, generating a strategy scoring set with confidence information, further supporting an abnormal probability judgment and risk triggering mechanism by the system, and outputting key monitoring parameters and coping strategies, so as to improve the simulation accuracy and stability; therefore, closed-loop control of strategy optimization and risk early warning is realized, and the credibility of the anaesthesia simulation process 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 related to artificial intelligence, in particular to an intelligent medical diagnosis engine based on an AI large model, which comprises an auxiliary diagnosis module for collecting data such as an electronic medical record and a medical image of a patient and predicting an auxiliary result by using an integrated learning method; the drug recommendation module is used for analyzing drug recommendation data by using an AI large model and predicting potential curative effects and side effects of new drugs; and the operation simulation module is used for constructing an operation environment and a human body model in combination with a virtual reality technology. A large amount of medical data can be quickly processed and analyzed through auxiliary diagnosis, the diagnosis time is shortened, gene, physiological and pathological multi-omics data of a patient can be integrated by reducing unnecessary invasive examination and misdiagnosis and drug recommendation, a personalized drug treatment scheme is provided for the patient, and the patient experience is improved. Comprehensive and accurate diagnosis basis and treatment suggestions are provided for doctors; by accurately simulating a real operation scene including mechanical and optical characteristics, a more real training experience is provided for doctors and medical students.

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 discloses a drug interaction prediction method based on an interaction enhancement graph self-attention mechanism, and belongs to the technical field of drug interaction prediction, and the method comprises the steps: carrying out the modeling of a drug under a local view and a global view, so as to obtain a molecular graph and a motif graph; designing an interaction-enhanced graph Transform module, respectively updating node embedding in two views of the molecular graph through an interaction-enhanced attention mechanism, and fusing through a multi-channel self-adaptive fusion module; enabling the extracted features to pass through a feedforward neural network, a residual connection layer and a normalization layer to obtain a final molecular representation of a molecular graph and a motif graph; designing a dynamic interaction scaling prediction module, and calculating a DDI score; and constructing a drug interaction prediction model, designing a loss function, and optimizing model parameters. According to the invention, the accuracy and stability of DDI prediction can be significantly improved.

The invention discloses an intelligent patient monitoring and predicting clinical data analysis system, which belongs to the field of medical analysis and comprises a real-time monitoring module, a data acquisition module and a knowledge base building module. According to the method, the nursing quality of the patient can be improved, medical resources can be effectively saved, the processing capacity of complex medical data is enhanced, the scientificity and rationality of clinical decisions are improved, a personalized and timely health management scheme is provided for the patient, a hospital can be more prospective and initiative in disease management, and the disease management efficiency is improved. Therefore, the overall medical service level is improved, uncertainty and patient individualized demand differences existing in clinical data can be effectively handled, and scheme suggestions with higher diversity and flexibility are provided for medical staff during decision making, so that the whole system has real-time performance, and long-term effect optimization across time dimensions can be realized; the treatment effect and experience of the patient can be improved, and a scientific basis is provided for reasonable distribution of medical resources.

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

The invention discloses a whole-process patient self-control analgesia management platform and method based on artificial intelligence, and relates to the related field of analgesia management, and the platform comprises a multi-source data interaction module which is used for building a time sequence standard data set; the information configuration module is used for starting the self-control analgesia pump; the AI pain evaluation module is used for generating a pain scoring result; and the identification early warning module is used for generating graded early warning. The technical problems of insufficient safety and unstable effect in the analgesia management process due to dependence on a fixed dose threshold and lagged artificial pain assessment in traditional patient self-control analgesia management are solved, the whole-process pain management platform based on artificial intelligence is constructed, multi-source data can be analyzed in real time, and the pain management efficiency is improved. And the pain state of the patient is dynamically evaluated, and the parameters of the analgesia pump are automatically adjusted, so that the technical effects of personalized analgesia management and safety and effectiveness in the analgesia process are realized.

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 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 an adverse drug reaction event recognition method and system based on multivariate knowledge mixed retrieval enhancement, and the method comprises the steps: extracting drug entities from a to-be-recognized clinical disease course record, segmenting the disease course record, and obtaining a drug entity set and a sentence set; for each extracted drug entity, retrieving drug concept knowledge having a hyponymy relationship with the drug entity and drug adverse reaction knowledge having an adverse reaction relationship with the drug entity in a pre-constructed multivariate knowledge base; for each sentence obtained through segmentation, searching suspected adverse drug reaction events meeting a first similarity requirement and drug field text knowledge meeting a second similarity requirement in a pre-constructed multivariate knowledge base; and finally, calling a large language model, taking all the retrieved knowledge as reference knowledge, and identifying the adverse drug reaction event from the to-be-identified clinical disease course record. According to the invention, the accuracy and reliability of adverse drug reaction event identification can be improved.

The invention relates to the technical field of artificial intelligence, and discloses a traditional Chinese medicine diabetes dry eye treatment difference evaluation system and method based on artificial intelligence, and the system comprises a four-diagnosis integrated module, a syndrome modeling module, an intelligent decision module, a curative effect feedback module, a knowledge base management module and a self-adaptive learning module. Multi-mode traditional Chinese medicine four-diagnosis data of tongue condition, pulse condition and eye diagnosis and modern physiological parameters of metabolic indexes are fused, a deep learning method is adopted for standardization processing, the problem that traditional Chinese medicine syndrome differentiation depends on subjective experience is solved, traditional Chinese medicine syndrome feature extraction of diabetes dry eye has objectivity and quantifiability, and the traditional Chinese medicine syndrome feature extraction efficiency is improved. The comprehensiveness and the accuracy of the diagnosis basis are improved; and the curative effect feedback module compares a prediction result with revival curative effect data in real time, and the knowledge base management module is linked to carry out case feature matching and strategy optimization, so that continuous optimization and long-term reliability of the treatment decision model are ensured.

The invention discloses a drug relocation method and system based on subgraph perception and a mixed graph neural network, and the method comprises the steps: generating a corresponding dynamic drug-disease associated subgraph # imgabs1 # for a target drug-disease pair in a drug-disease associated network # imgabs0 #; aiming at the medicine nodes and the disease nodes in the # imgabs2, respectively constructing a medicine sub-graph # imgabs3 # and a disease sub-graph # imgabs4 #; performing deep feature extraction on # imgabs5 by applying a multi-head dynamic graph attention neural network model, and performing feature extraction on # imgabs6 and # imgabs7 on the basis of fusion of a GCN model and a GAT model to obtain final feature representations of # imgabs8, # imgabs9 and # imgabs10; and fusing the three, inputting the fused three into a drug-disease association prediction model based on a multi-layer perceptron (MLP) network, and predicting the association relationship of the target drug-disease pair. According to the invention, efficient and accurate drug-disease association prediction can be realized.

The invention aims to provide an acne precise treatment and drug delivery system based on multi-modal data and feedback control. The system comprises an image acquisition and identification module, which carries out acne focus identification on an obtained skin image based on a deep learning algorithm, and extracts focus feature data; the sensor acquisition module is used for acquiring skin physiological parameters and environmental parameters related to acnes; the multi-modal fusion module carries out fusion processing on the focus characteristic data, the skin physiological parameters and the environmental parameters to generate current acne state parameters; the digital twinborn prediction module constructs a digital twinborn model of the patient according to the current acne state parameters and predicts the development trend of acnes; the individualized treatment decision module formulates an individualized treatment scheme and a corresponding administration dosage according to a prediction result of the digital twinning prediction module; and a set of system capable of realizing multi-source data acquisition, automatic evaluation, prediction analysis and personalized treatment scheme recommendation of the acne of the patient is constructed.

The invention discloses an internet-of-things senile disease nursing system supporting equipment state monitoring, and relates to the technical field of internet-of-things senile nursing. An acquisition module realizes multi-index monitoring by using a flexible sensor and a non-invasive technology; the edge computing unit fuses the neuromorphic architecture and federal learning; the equipment monitoring module predicts faults in combination with acoustic emission and thermal imaging technologies; the health assessment engine carries out disease early warning through causal reasoning and a knowledge graph; the early warning system realizes hierarchical response and multi-terminal linkage; and the block chain platform ensures data security and sharing. Through multi-module collaborative innovation, precise collection of elderly health data and real-time monitoring of equipment states are realized, diseases and equipment faults are early warned in advance, an intelligent evaluation engine can predict complications, multi-modal early warning improves response efficiency, a block chain technology guarantees data security, medical resource sharing is supported, and the system is suitable for popularization and application. And the safety, timeliness and intelligent degree of senile disease nursing are comprehensively improved.

The invention discloses a drug-drug interaction prediction method based on a drug flow sub-graph, and relates to the technical field of bioinformatics, and the method comprises the steps: data preparation: collecting a reference data set including drug-drug interaction, and introducing an external knowledge graph for adaptability preprocessing; constructing a model: constructing a drug-drug interaction prediction model, and training by using the preprocessed reference data set; and effect prediction: inputting a target drug into the drug-drug interaction prediction model, and outputting a drug-drug interaction prediction result through the drug-drug interaction prediction model. The structure and semantic information of the drug flow sub-graph are fully utilized, accurate prediction of drug interaction is realized, and the prediction efficiency is improved. And the interpretability of the drug-drug interaction prediction model is improved.

Some embodiments relate to computerized methods and systems dedicated to automatic health risks stratification of large population of patients for determining a respective health risk of the patients in the population, which enables to identify patients that exhibit high risk of a medical-care event (e.g., hospitalization risk) and provide urgent interventions to avoid or reduce the likelihood of the occurrence of such an event.

The invention discloses an intelligent medicine decocting control method and device based on a traditional Chinese medicine e-commerce platform and electronic equipment, and relates to the field of data processing. According to the method, order information on a traditional Chinese medicine e-commerce platform is obtained, the order information comprises traditional Chinese medicine prescriptions and patient information, and the traditional Chinese medicine prescriptions comprise the types and dosages of traditional Chinese medicinal materials; analyzing the patient information to obtain the personal constitution category of the patient; on the basis of the individual physique category of the patient, the type and dosage of each traditional Chinese medicinal material are adjusted, and a personalized decoction scheme is obtained; and sending the personalized decoction scheme to an optimal intelligent medicine decocting dealer associated with the traditional Chinese medicine e-commerce platform, so that the optimal intelligent medicine decocting dealer automatically completes putting and decoction of each traditional Chinese medicinal material according to the received personalized decoction scheme, thereby obtaining a liquid medicine meeting the personal constitution category. By implementing the technical scheme provided by the invention, the efficiency of medicine decocting management is improved.

The invention provides a drug response prediction method based on a gene relation network and a drug substructure. The drug response prediction method comprises the following steps: performing feature extraction on three multi-modal features of gene expression, copy number variation and mutation of a cell line by adopting an enhanced graph attention network; acquiring a substructure of the medicine by using a gating message passing neural network, and extracting features by enhancing a graph attention network; constructing two complementary graph structures, namely an atom-bond graph and a bond-angle graph, and performing feature extraction and message passing on the two graphs by using a graph convolutional neural network so as to extract 3D features of the medicine; the characteristics of the drug and the cell line are integrated through concat operation, and drug response prediction is carried out through mlp. According to the drug response prediction method provided by the invention, cell line features are extracted by establishing and using three kinds of omics data with different emphasis, and drug feature extraction is performed by using a drug molecular map, a drug 3D structure, a drug molecular fingerprint and a drug substructure relationship.