279 results about "Drug interaction" patented technology

The invention discloses a tumor patient medication side effect management method and system based on big data analysis, and the method comprises the following steps: collecting and preprocessing patient data and drug data, and extracting patient features and drug features, the patient data comprising genome data provided by a patient; constructing a prediction model which comprises a time sequence analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model and is used for predicting risk scores of different types of side effects of the patient by taking the drug characteristics and the patient characteristics as input of the prediction model; inputting the basic information, the medication information and the genome data of the current patient into the trained prediction model to obtain risk assessment of various side effects of the current patient; according to the risk assessment result, personalized adjustment suggestions suitable for the patient are selected from a preset intervention scheme library and provided for the current patient. The scientificity and accuracy of administration side effect management in the treatment stage can be effectively improved.

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 provides an enhanced drug-drug interaction prediction method based on multi-modal drug characteristics. The method comprises the following steps: step 1, carrying out data processing on two medicines to obtain characteristic data of the two medicines, wherein the characteristic data comprises a three-dimensional molecular map, a molecular fingerprint and a one-dimensional SMIELS sequence; 2, based on the feature data of the two drugs, feature learning is conducted through a heterogeneous feature extraction network, and respective multi-modal feature embedding expressions of the two drugs are obtained; 3, performing feature fusion on the multi-modal feature embedding expressions of the two drugs to obtain respective fused features of the two drugs; and 4, performing fusion interaction on the fused features of the two drugs and the interaction category features, and predicting a fusion interaction result to obtain a prediction result. The method provided by the invention can integrate the multi-modal information of the drug molecules, does not depend on multivariate information and knowledge maps, and has better performance and wider applicability.

The invention relates to the technical field of drug interaction prediction, and discloses a drug interaction prediction method based on multilayer attention and elastic message passing, which comprises the following steps: firstly, extracting graph structure information of drug molecules from a 3D structure of the drug molecules, including associated features of atoms and chemical bonds; on the atomic feature level, learning updating and denoising of different atom and chemical bond features in drug molecules are achieved by using an elastic graph neural network and fusing the attention mechanism of Transform and combining a 3D position coding method based on the molecular mass center; on the molecular feature level, the attention mechanism is further utilized, and through supervised learning, comparative learning and structured learning, the potential interaction relationship of different drug molecules is mined, and potential network attacks are detected and defended; and interaction prediction of any two drug molecules i and j is realized. The results of four indexes of ACC, AUC, AP and F1 all reach 98% or above, and the method exceeds the most advanced method at present.

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.

Characterization of drug-drug interaction properties and pharmacological properties of maribavir is useful to inform potential drug-drug interactions and dosing strategies when administering with co-medications.

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 discloses a molecular structure characterization-based drug-drug interaction prediction method, which comprises the following steps of: acquiring drug molecule data and drug-drug interaction data, performing standardized preprocessing on the data, and constructing a drug molecule map according to the drug molecule data; inputting the drug molecule map into a model, extracting drug molecule multi-scale features through a multi-scale map convolutional network, and fusing the drug molecule multi-scale features through a dynamic attention mechanism to obtain drug molecule features; based on the drug molecular characteristics, through a full-connection neural network, learning the relationship between the drug molecular characteristics and the drug-drug interaction, and obtaining the prediction probability of the drug-drug interaction; and training the model by adopting a joint loss function according to the drug-drug interaction data and the prediction probability, and applying the trained model to drug-drug interaction prediction. And through the multi-scale image convolutional network, dynamic attention fusion and joint optimization, the prediction accuracy is significantly improved.

The invention provides an asymmetric drug interaction prediction method based on a diffusion diagram attention network, and relates to the technical field of drug interaction prediction.The method comprises the steps that a directed graph network is constructed by using chemical structure features of drug molecules, and features are extracted from drug applying and receiving perspectives through a two-way diagram attention network; therefore, the asymmetry between the drugs is effectively represented. Furthermore, a diffusion model is introduced to carry out noise injection and de-noising processing on a graph structure, so that the adaptability of the model to sparse data and the robustness of feature extraction are remarkably enhanced. Finally, multi-modal features are fused through a deep neural network, and high-precision prediction of drug interaction is realized. Compared with the prior art, the method has the advantages that the prediction accuracy exceeds that of the prior art, the dependence on labeled data is greatly reduced, the feasibility and generalization ability of the model in practical application are greatly improved, and a new technical path is provided for safety evaluation and precise medical research of drug combination therapy.

The invention discloses a tetra-fused ring compound as well as a preparation method, a pharmaceutical composition and application thereof. The invention specifically discloses a tetra-fused ring compound as shown in a formula I or pharmaceutically acceptable salts thereof. The compound disclosed by the invention can be used for effectively treating depression, and has good pharmacokinetic properties and relatively small drug interaction. # imgabs0 #

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 intelligent follow-up visit, in particular to an intelligent follow-up visit system and method for daytime tumor patients, a medium and equipment, and the system comprises a physiological monitoring module, a drug interaction analysis module, a side effect mapping module, a health risk early warning module and an intelligent inquiry optimization module. According to the method, through continuous monitoring and real-time comparison of the physiological data of the patient and effective archiving of data fluctuation in a key period, instant and accurate reference is provided for clinical decision making, deep drug interaction analysis enables drug adjustment to be more targeted, unsuitable drug combinations are reduced, and therefore potential health risks of the patient are relieved; through side effect mapping, the system can predict and prevent potential side effects, the treatment safety is guaranteed, the risk early warning module enables the system to identify major health risks in the first time, the response speed of emergency medical events is improved, and the system can be used for improving the treatment efficiency by analyzing the interaction history of patients and optimizing the inquiry process. And the medical experience of the patient is more targeted and practical.

The invention provides a drug pair interaction prediction method and device based on soft mask double-view learning, and relates to the technical field of drug interaction prediction. The method comprises the following steps: obtaining molecular view data of two to-be-predicted drugs and an interaction relationship type thereof to form a triple, and generating a bipartite graph of a drug pair; inputting the triple and the bipartite graph into an MSMDL encoder, and outputting new node embedding of corresponding drugs; wherein the MSMDL encoder has K layers, the output result of each layer is respectively input into the MSMDL encoder of the next layer and the self-attention graph pooling layer, and graph-level representation is generated through the self-attention graph pooling layer to obtain substructure feature embedding; embedding the substructure features of each layer to serve as input of a common attention mechanism layer, and obtaining importance of interaction between the substructure feature embedding of each layer; and finally, outputting the DDI prediction probability through a decoder. According to the invention, local and global information of the drug is integrated, and the accuracy of drug pair interaction prediction is improved.

The invention discloses a drug interaction prediction method based on a double-view graph neural network, and the method comprises the steps: extracting features in a single drug view in a self-adaptive manner through a directional message passing neural network with a substructure attention mechanism, and extracting interaction features between the drug and the view through a graph attention network; and fusing to generate drug substructure features, so as to more accurately predict the interaction between drugs. And through the common attention mechanism module, drug interaction is converted into pairwise interaction between drug substructures, so that the capability of capturing drug molecular characteristics is greatly enhanced, and the prediction precision is improved. The invention can make full use of the interaction information between drugs and the interaction information of chemical substructures, studies the classification and identification problem of drug interaction prediction, and provides an efficient drug interaction prediction method.

The invention discloses a drug interaction prediction method based on multi-dimensional features, and relates to the technical field of drug interaction prediction, and the method comprises the following steps: S1, collecting multi-dimensional feature information of drugs from authoritative databases DrubBank, KEGG and Pubchemm, and obtaining drug features; meanwhile, real clinical case data are collected and recorded as clinical data. According to the drug interaction prediction method based on the multi-dimensional features, through ensemble learning and model fusion, by means of complementary advantages of different models, reasonable screening and weighted features, the models can keep relatively stable performance in different data sets and application scenes, model prediction deviation caused by data fluctuation or feature change is reduced, and the prediction accuracy of drug interaction is improved. The generalization ability of the model is enhanced, the applicability and reliability of the model in actual drug interaction prediction are improved, and the probability that a single model generates a false positive result is reduced.

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 belongs to the technical field of intelligent medical treatment, and provides a drug recommendation method and system based on feature fusion and graph construction, and the method comprises the steps: carrying out the local feature extraction and global feature extraction of a diagnosis sequence, an operation sequence and a drug sequence, fusing the local feature extraction and the global feature extraction, and carrying out the global feature extraction; obtaining a diagnostic representation, a surgical representation and a drug representation; by fusing local feature extraction and global feature extraction, the health condition of the patient can be represented more comprehensively; according to the electronic health record data set, establishing an electronic health record graph, a drug interaction graph and a drug sensitivity graph, obtaining a drug representation by using the established electronic health record graph, drug interaction graph and drug sensitivity graph and a preset graph attention network, and fusing the drug representation with the drug representation obtained by feature extraction to obtain a drug representation; weight contributions of different medical events in recommendation decisions can be displayed, doctors are helped to understand logic behind recommendation, and the model has remarkable result interpretation ability.

The invention discloses a drug recommendation model construction method, a drug recommendation method, a drug recommendation device and drug recommendation equipment, and belongs to the technical field of drug recommendation. The method comprises the following steps: constructing an EDRMM model; then constructing a loss function including binary cross entropy loss, multi-label prediction loss, drug interaction loss and regularization constraint terms; constructing a training sample set; and then training an EDRMM model by using the training sample set and the loss function to obtain a trained EDRMM model as a drug recommendation model. The fine-grained electronic health record data, the historical electronic health record selection process and the regularization constraint term are introduced, noise of the historical attribute data is reduced, the DDI occurrence risk rate is better controlled, and the drug recommendation quality is improved.

The invention relates to the technical field of medical information, in particular to a prompt-based large language model drug recommendation method, which comprises the following steps of: constructing a drug interaction graph, coding the drug interaction graph through a graph convolutional network, generating embedded representation of a DDI graph, and generating a dynamic prompt according to an electronic health record of a patient. The DDI graph embedded representation and the dynamic prompt are input into a pre-trained large language model, drug recommendation for the patient is generated, a coding scheme supporting a non-transitive DDI graph is introduced, the rejection relation between drugs is captured by modifying a graph convolutional network, drug conflicts are effectively avoided, and in addition, the drug recommendation efficiency is improved. Compared with the prior art, a dynamic prompting method is designed, targeted medicine prompts are generated by comparing the health condition of the current patient with historical treatment records and calculating the similarity, the prompts are integrated, LLM can better consider medicine interaction and historical treatment information of the patient, and individuation and safety of medicine recommendation are improved.

The invention discloses an intelligent VTE prevention and control system, and the system comprises a risk assessment module which is used for obtaining a risk index of VTE according to a risk factor; the preventive measure recommendation module is used for recommending preventive measures according to the clinical data; the VTE early warning module is used for acquiring an early warning index of the VTE according to the iconography data, the laboratory examination result and the clinical symptoms; and the treatment scheme optimization module is used for adjusting the treatment scheme according to the individual characteristics, the treatment reaction and the drug interaction, and helping a doctor to provide an auxiliary reference result for the illness state of the patient and subsequent treatment.

The invention relates to the technical field of drug safety evaluation, in particular to a multi-center combined evaluation method and system for drug safety based on a graph neural network and federated learning. Known and unknown drug interaction is systematically predicted based on a drug multi-relation knowledge graph and a graph neural network, a key path of DDI is identified through a graph attention mechanism, a molecular mechanism of the interaction is revealed, and natural language interpretation is generated. And meanwhile, through privacy protection and multi-center cooperation, a federal learning architecture is utilized to break data islands and improve the external effectiveness of an evaluation conclusion on the premise of protecting patient privacy and meeting data compliance requirements. The heterogeneity of data of different mechanisms is effectively evaluated through distribution deviation detection, deviation caused by blind extrapolation is avoided, a real-world evidence methodology report and a data traceability auditing clue are automatically generated, the requirement of a supervision mechanism for real-world evidence quality is met, medicine supervision decision is supported, and medicine research, development and review are accelerated.

The present invention relates to the use of cannabidiol (CBD) in the treatment of patients with childhood-onset epilepsy who are concurrently taking immunosuppressant drugs. In particular the immunosuppressant drug is a calcineurin inhibitor. More particularly the immunosuppressant drug is tacrolimus. Where the CBD is used in combination with an immunosuppressant drug, caution should be taken. For example, the dose of either the CBD and / or the immunosuppressant drug may be required to be reduced. Moreover, the patient may need to be monitored for side effects of said drug-drug interaction. Preferably the CBD used is in the form of a highly purified extract of cannabis such that the CBD is present at greater than 98% of the total extract (w / w) and the other components of the extract are characterised. In particular the cannabinoid tetrahydrocannabinol (THC) has been substantially removed, to a level of not more than 0.15% (w / w) and the propyl analogue of CBD, cannabidivarin, (CBDV) is present in amounts of up to 1%. Alternatively, the CBD may be a synthetically produced CBD.

The invention belongs to the technical field of artificial intelligence algorithm and bioinformatics crossing, and relates to a drug interaction prediction method based on multi-modal molecular characterization. According to the method, through the edge perception GCNII architecture and the Hop2Token multi-hop coding mechanism, effective modeling of atomic-level and bond-level local environments and a cross-substructure high-order dependency relationship in drug molecules is realized, and the accuracy and robustness of drug interaction prediction are improved. According to the method, Mol2Vec and MolT5 cross-modal molecular characterization is integrated, fusion of molecular overall semantics and substructure grammar semantic association is achieved, and the generalization ability of the model to complex molecules and unknown medicine combinations is remarkably improved. According to the method, the dynamic feature screening algorithm driven by the SHAP value of the artificial intelligence technology is adopted for biological verification, the feature redundancy problem is effectively solved, the molecular biological information analysis processing calculation efficiency and the model transparency are improved, and the traceability of the prediction process is guaranteed.

The invention discloses a graph contrast learning drug recommendation method and system fusing clinical features, and relates to the technical field of intelligent medical treatment. The method comprises the following steps: acquiring a clinical medical record database of a patient, extracting diagnosis, operation and medicine information of each treatment, and constructing a historical medical record heterogeneous graph of the patient; in heterogeneous graph propagation, a structure guidance matrix (mask) is generated by using a conditional probability matrix to guide node connection and attention generation, and the expression stability is improved in combination with Hellinger distance constraint; in the aspect of drug modeling, from three perspectives of a drug co-occurrence network, an interaction network and a molecular structure diagram, representation consistency and interpretability are improved through diagram contrast learning. On the basis, four types of loss function optimization recommendation results are designed, and a medicine combination with the lowest comprehensive medicine use risk is screened out for clinical auxiliary diagnosis and treatment. According to the method, multi-view medical knowledge is fused, efficient drug combination screening and recommendation are realized, meanwhile, the drug interaction risk is reduced, and the recommendation accuracy and safety are improved.

The present invention discloses a method and system for managing the side effects of medication for tumor patients based on big data analysis, the method comprising the following steps: collecting and preprocessing patient data and drug data, extracting patient characteristics and drug characteristics, wherein the patient data includes genomic data provided by the patient; constructing a prediction model, including a time series analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model, for using drug characteristics and patient characteristics as inputs of the prediction model to predict the risk score of different types of side effects in the patient; inputting the basic information, medication information and genomic data of the current patient into the trained prediction model to obtain a risk assessment of various side effects in the current patient; according to the risk assessment results, selecting a personalized adjustment suggestion suitable for the patient from a preset intervention program library and providing it to the current patient. The present invention can effectively improve the scientificity and accuracy of the management of medication side effects during the treatment stage.

The embodiment of the invention discloses a drug-drug interaction prediction method based on multi-modal semantic alignment, and relates to the technical field of biomedical prediction. The drug-drug interaction prediction method based on multi-modal semantic alignment aims at solving the technical problem that the DDI classification effect of a model is poor. Comprising the following steps: acquiring a text feature set, a molecular structure feature set and a subgraph feature set of a SemEval-2013Task 9 data set; performing feature fusion on the sub-graph feature set and the molecular structure feature set through a sub-graph-molecule cross attention module, and then inputting the feature fused sub-graph feature set and the molecular structure feature set together with the text feature set into a text guide fusion module to obtain a text fusion feature set; and splicing the text feature set, the molecular structure feature set and the sub-graph feature set, performing residual connection processing and splicing processing on the text feature set, the molecular structure feature set and the sub-graph feature set, and performing layer normalization processing to obtain target multi-modal fusion data.

The invention discloses a drug interaction prediction method and system based on a graph neural network and multiple attention mechanisms. The method comprises the following steps: converting an SMILES sequence of a drug pair into graph data, and obtaining an embedding matrix of the drug pair by utilizing a multi-layer GIN operation; extracting a self-attention feature matrix of the drug pair from the embedding matrix of the drug through a self-attention mechanism encoder, calculating a cosine similarity matrix of the drug pair, and obtaining a self-attention feature vector of the drug pair after leveling operation; processing the embedded matrix of the drug pair through an interactive attention mechanism encoder to obtain a feature matrix with interactive attention coefficients, performing average pooling operation on the interactive attention coefficient feature matrixes of the two drugs, and then performing standardization to obtain an interactive feature vector of the drug pair; and carrying out splicing operation on the self-attention feature vectors and the interaction feature vectors of the drug pairs, and inputting a multi-layer perceptron to predict the interaction between the drug pairs. According to the invention, the interaction between drugs can be better predicted.

The invention relates to a drug interaction prediction method and system based on multi-view comparative learning, and belongs to the technical field of natural language processing. According to the method, two channels of a drug molecular map and a biomedical knowledge map are constructed in parallel, structural and semantic features are extracted by using a pre-trained heterogeneous map neural network, and multi-view comparative learning guided by information gain is introduced for joint optimization, so that the generalization ability and robustness of the model to unknown drug pairs are enhanced. According to the method, system evaluation is carried out on the performance of the system in two types of prediction tasks (multi-type and multi-label) and three prediction scenes. Experimental results show that the method has excellent performance in all tasks and scenes. Further case analysis also verifies the effectiveness of the system in predicting the interaction type of the unseen drug pair.

The invention relates to the technical field of medical health and artificial intelligence, and discloses an AI reminding method for a gastroenterology department enteroscope patient to take a laxative, comprising the following steps: S1, guiding the patient to enter an AI interaction interface through a two-dimensional code or an applet entry; s2, generating a personalized medication scheme according to the basic information and the real-time data input by the patient; s3, ensuring that the patient completes an intestinal tract preparation process through a staged guidance and real-time reminding function; s4, performing abnormal reaction identification according to real-time feedback data input by the patient, and adjusting subsequent operation suggestions; and S5, screening potential drug interaction through the integrated drug database, and providing a safe drug use prompt. Personalized data such as age, weight, basic diseases, daily medication and daily work and rest of a patient are collected through the personal information input module, the medication scheme generation module calls a preset algorithm to calculate the optimal medication dosage, medication time nodes are determined, and diet suggestions are generated.

The invention relates to the technical field of intelligent medicine recommendation, and provides a medicine recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the time sequence coding processing of the current treatment information of a patient, and generating a semantic vector of the current treatment information; performing multi-message propagation processing on the heterogeneous medical graph based on a graph convolutional neural network to determine knowledge memory vectors of various medical entities, and determining knowledge vectors of the current doctor seeing information based on the knowledge memory vectors; determining fusion information according to the semantic vector and the knowledge vector; and inputting the fusion information into a drug recommendation model, carrying out probability calculation processing that new drugs are used and probability calculation processing that historical drugs are reserved and used on the fusion information, and based on the determined newly-added use probability of each drug, the reserved use probability of each historical drug and an adjacent matrix of drug interaction, carrying out drug recommendation. And determining the drug combination recommended to the patient. The safety of medicine combination is improved, and the accuracy of medicine recommendation is effectively improved.