132 results about "Drug interaction" patented technology

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 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 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 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 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 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.

To provide a method and device for predicting plasma concentration transition in combination of multiple drugs, by easily determining Ki, fm in vivo, and making them into a database, since, there has been dynamic prediction using a physiologic medicine speed theory (PBPK) model, as a prediction method of a drug interaction for medical products, however, prediction using an inhibition constant (Ki) of in vitro data and a contribution ratio (fm) of a metabolic enzyme, do not always match increase of clinical AUC.SOLUTION: There is provided a method for determining a parameter of a compartment model from a pharmacokinetic parameter of an interacted drug and interaction drug, then converting the compartment parameter into a PBPK model parameter, then determining by simulation using the PBPK model, a Ki value and fm being AUC increase ratios observed clinically. The acquired parameter is made into a database, and simulation of blood concentration transition of the interacted drug and interaction drug is performed.SELECTED DRAWING: Figure 1

The invention discloses a method and system for analyzing a bacterium-drug interaction panoramic dynamic mechanism by using a causal enlightenment hypergraph neural network, and electronic equipment, and belongs to the technical field of artificial intelligence and biomedical informatics. The method comprises the following steps: constructing a disease / syndrome / disease-bacterium / microorganism / metabolite-prescription / drug / component interlocking hypergraph; analyzing a bacteria-drug interaction dynamic mechanism based on a dynamic hypergraph network algorithm; optimizing the hypergraph neural network based on the prescription compatibility rule and the traditional Chinese medicine dosage; and predicting the microbial marker based on the causal enlightenment hypergraph neural network. According to the method, a multi-entity high-order relation is captured through an interlocking hypergraph structure, a time sliding window and an attenuation mechanism are introduced to analyze dynamic evolution, the traditional Chinese medicine dosage is used as an attention priori optimization network, and causal reasoning is achieved through alternate optimization of an intervention discovery and response prediction module. In a public data set test, the performance evaluation index is obviously superior to that in the prior art, and the problem of analyzing the panoramic dynamic mechanism of the bacterium-drug interaction is effectively solved.

The invention relates to the technical field of elderly chronic disease medication management, and discloses an elderly chronic disease medication compliance intelligent reminding and management system. The system comprises an individualized medication modeling unit for establishing an individual pharmacodynamic model containing medication taboo and dosage rules based on an electronic health record of a patient and pharmacokinetic parameters; the multi-modal behavior sensing unit is used for collecting physiological data and movement tracks through wearable equipment, and generating a medication behavior sequence in combination with the record of the intelligent medicine box; the compliance deviation analysis unit is used for comparing an expected behavior with an actual behavior to calculate indexes such as a time matching degree and dose deviation; the risk decision engine unit is used for outputting an intervention level and a reminding strategy in combination with the deviation index, the physiological state and drug interaction; and the cross-platform collaborative execution unit triggers multi-channel reminding, medicine box locking and emergency contact notification. According to the system, the whole medication management process is intelligent, and the medication compliance and safety of the elderly patients are improved.

The invention relates to the technical field of medical information, and discloses a clinical medication data management system based on DRGs. The system comprises a data acquisition module, a mode construction module, a deviation identification module, a cause association module and a strategy generation module. According to the system, medicine use details and medicine use time track data under each DRG group are extracted and integrated, and a typical medicine use mode map is constructed to serve as a behavior reference. In real-time monitoring, the system performs multi-dimensional comparison on new case data and an atlas, and identifies drug use sequence, dosage and cost abnormity. The system performs intelligent attribution on the deviation based on a clinical medication knowledge graph, traverses drug interaction, treatment specifications and cost influence paths, forms a structured cause association network, and generates medication adjustment suggestions according to matching. According to the scheme, closed-loop management from clinical medication behavior modeling to deviation depth analysis and intervention strategy generation is realized, and medication monitoring fineness and decision support effectiveness are improved.

The invention relates to a drug combination data processing method and system based on organ adverse reactions, and the method comprises the steps: carrying out the graph neural network fusion of multi-source patient medical data, and carrying out the cross-scale integration of organ function parameters, metabolic characteristics and hemodynamic data; extracting a core biomarker by utilizing principal component analysis and organ specificity screening, and generating a feature vector representing organ reaction; constructing a nonlinear drug interaction model by combining a dose-time sequence and in-vitro experimental data, and quantifying the strength of synergy and antagonism; gene expression profiles, pathological states and lifestyle data are introduced for triple dynamic calibration, and individualized adverse reaction probability optimization is achieved; monte Carlo sampling is adopted to simulate an organ toxicity scene, and intelligent classification decision is performed on a high-risk combination scheme based on a supervision rule base. The method breaks through the limitation of data islands, static evaluation and individualization deficiency in the traditional technology, and significantly improves the prediction precision and clinical applicability of the drug combination safety.

The application discloses a DDI prediction method based on a Siamese structure and graph contrast learning, comprising the following steps: collecting drug-drug interaction text data and physical and chemical property data files and targeting relationship data files of drugs; extracting the physical and chemical property characteristics and the targeting relationship characteristics of the drugs, fusing the characteristics to obtain initial characteristics based on the physical and chemical properties and the targeting relationship; calculating a drug-drug interaction adjacency matrix, combining the initial characteristics to construct a drug-drug interaction heterogeneous graph; inputting the heterogeneous graph into a Siamese structure-based graph contrast learning model to learn and obtain embedding characteristics of drug nodes; and using a link prediction method to calculate the score of edges between any two drug nodes. The application can alleviate the problem that the drug targeting relationship characteristics and the drug-drug interaction text data are difficult to be used alone when the data are sparse and have an impact on the performance of the model, improves the accuracy of drug-drug interaction prediction, and can be applied to identifying potential interactions between drugs.

Provided are an amphotericin B amide derivative and a use thereof. The amphotericin B amide derivative is as shown in formula (I). The compound has good antifungal activity, is effective against various fungi, has good water solubility, is adapted to be developed into an injection dosage form, has excellent metabolic stability in animals (such as mice, rats, dogs, and monkeys), has a long half-life period, can effectively reduce the frequency of administration, has weak inhibition on CYP enzymes, and has a low risk of drug interaction.

This invention relates to a cinnabar-2-oxide compound, its preparation method, and its uses. The cinnabar-2-oxide compound is shown in formula (I). By oxidizing a nitrogen atom to an N-oxide and then combining it with a heteroaryl group, the above-mentioned cinnabar-2-oxide compound achieves good α2-GABAA receptor affinity and positive regulatory activity. Simultaneously, it has the potential to significantly reduce PXR (pregnane X receptor) activation, thereby reducing drug-drug interactions and improving drug efficacy or safety.

The invention provides an amphotericin B amide derivative and application thereof. The amphotericin B amide derivative is shown as a formula (I), has good antifungal activity, is effective to various fungi, has good water solubility, is suitable for being developed into injections, has excellent metabolic stability in animal bodies (such as mice, rats, dogs and monkeys), is long in half-life period, can effectively reduce the administration frequency, is weak in CYP enzyme inhibition, and can be used for preparing the antifungal drugs. The drug interaction risk is low.

The present disclosure is directed to systems and methods for analyzing drug-drug and gene-drug interactions for a user and providing recommendations accordingly. The systems and methods can utilize a database comprising a list of genes are associated with a plurality of drugs based on clinical relevance of the gene to the actions of the plurality of drugs. The systems and methods can include recommending alternative drugs to users based on any identified problematic drug-drug or gene-drug interactions.

This invention provides a drug interaction prediction method based on a bidirectional cross-view attention network, belonging to the field of drug interaction prediction technology. The method first constructs a Morgan fingerprint similarity view of the drug, an original DDI view, and a multi-scale diffusion view based on personalized PageRank. Then, a graph convolutional network with shared weights is used to co-encode the multiple views, generating a unified drug embedding representation. Finally, a bidirectional cross-view attention mechanism is used to achieve fine-grained interaction and alignment between the structural and attribute views, and interaction prediction is completed via a multilayer perceptron. This invention effectively alleviates the sparsity problem of the DDI network through multi-scale topology enhancement and achieves complementary enhancement between views using bidirectional cross-view attention, significantly improving the accuracy and generalization ability of drug interaction prediction. It can provide reliable technical support for drug development screening and clinical combined drug safety assessment.

The application discloses a drug interaction prediction method based on DS evidence theory. First, multi-modal data of drugs are acquired to construct a drug interaction dataset; then, multi-modal features of the drugs are extracted as inputs of a deep neural network, a multi-modal drug interaction prediction model is constructed, and the prediction model is trained; then, the trained prediction model is used to predict drug interactions. The prediction model uses Dirichlet distribution to calculate classification probability and uncertainty value, and obtains multi-modal prediction results and overall uncertainty value through Dempster-Shafer evidence theory fusion. Compared with existing prediction methods, the application uses multi-modal drug data, has a lower prediction error, can provide meaningful uncertainty information for guiding multi-modal prediction fusion and expressing prediction confidence, and has higher reliability and robustness.

This invention discloses an artificial intelligence-based method for predicting drug interactions based on external entity association mapping, comprising the following components: (1) Extraction of intrinsic properties of drug molecules: The spatial geometry and atomic properties of molecules are processed using a Uni-Mol pre-trained model to obtain drug molecule characterization. (2) External entity association mapping: Projection blocks are constructed using a knowledge graph, and complex association patterns of external entities corresponding to drugs are captured through graph neural networks and global perception logic. (3) Deep fusion of multi-source heterogeneous information: Information is efficiently integrated using attention mechanisms and multilayer perceptrons to form the final fused characterization. (4) Prediction and decoding of interactions: The specific types of interactions are accurately decoded and predicted using a decoder architecture. (5) Interactive visualization and decision support: Visualization tools and AI decision support are provided to help researchers reduce trial-and-error costs and make scientific decisions.

The present invention relates to the use of cannabidiol (CBD) in the treatment of patients with childhood-onset epilepsy who are concurrently taking caffeine. Where the CBD is used in combination with caffeine, caution should be taken. For example, the dose of either the CBD and / or caffeine 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 95% 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 discloses a multi-channel knowledge graph attention network drug interaction prediction method, system and device and a medium, belongs to the technical field of graph neural network and drug interaction prediction, and aims to solve the technical problem of how to capture implicit information of drug interaction and improve the accuracy of drug interaction prediction. According to the technical scheme, the method comprises the following steps: constructing a drug knowledge graph; the method comprises the following steps: acquiring drug-chemical entity information, drug-substructure information, drug-drug information and molecular structure information of a to-be-predicted drug; respectively constructing a drug-chemical entity knowledge graph, a drug-substructure knowledge graph, a drug-drug knowledge graph and a molecular structure knowledge graph by using the drug-chemical entity information, the drug-substructure information, the drug-drug information and the molecular structure information; extracting drug characteristics; fusing drug characteristics; predicting a drug interaction event; and predicting and optimizing.

Disclosed herein are systems and methods for reducing medication error caused by a drug-drug interaction between Compound 1 (E7766) and an OATP inhibitor. Methods for monitoring the exposure of Compound 1 in a patient and preventing overexposure of Compound 1 in a patient are also disclosed.

The application discloses a brain hemorrhage perioperative medication scheme intelligent recommendation method and system, acquires physiological parameters and operation time sequence characteristics of a patient, constructs a patient risk image, carries out risk matching with a drug knowledge base to generate a candidate drug set, extracts drug attribute characteristics to determine a drug function grouping, carries out adaptability scoring based on the patient risk image to generate a grouping adaptability matrix, carries out drug interaction analysis to identify synergistic pairs and antagonistic conflict pairs, carries out combination optimization on the synergistic pairs to generate a synergistic scheme, carries out conflict exclusion screening on the antagonistic conflict pairs to generate safety constraints, constructs a medication scheme topology, carries out dose sensitivity anomaly detection and correction to generate a correction factor to generate a time-sharing drug administration scheme, carries out real-time monitoring to carry out perfusion evaluation to determine a safety level and trigger dose adjustment, forms an optimized medication scheme, and realizes intelligent recommendation of perioperative medication.

This invention discloses a dosage analysis method for anesthetic drugs during surgery in patients with hypertension, hyperlipidemia, and hyperglycemia. This method integrates multi-source heterogeneous health data from the patient over the past five years to construct a personalized, structured health record. During anesthesia induction, based on the patient's individualized pharmacokinetic model and assessed drug interaction risks, it calculates and recommends precisely adjusted induction doses. During anesthesia maintenance, it innovatively decodes specific EEG signals to establish a dynamic mapping relationship between neurophysiological characteristics and pharmacokinetics. Based on EEG characteristics reflecting drug sensitivity and pain perception, it automatically and in real-time adjusts the predicted concentration of the target drug, achieving closed-loop precise control based on direct feedback from the central nervous system. The system automatically identifies all medications used by the patient, generates a drug interaction risk assessment report using a massive knowledge base, and dynamically updates the risk level and sends warnings during surgery based on real-time vital signs.

The invention belongs to the technical field of medication safety, and discloses a children medication evaluation method, and the children medication evaluation comprises the following specific operation steps: S1, checking the safety of a medicine: checking the toxicity and adverse events of the medicine according to the medication condition of a child, and particularly paying attention to the special reaction of the child; according to the invention, the body data of the child patient is imported into the evaluation system, so that the body data is compared with the internal information of the data management module, and then similar cases are found and compared with the medication conditions, so that the situation that the cases are far away from the medication conditions is avoided; meanwhile, the medicine use condition of medical staff is checked through the medicine information module and the clinical test management module, the situation that whether medicine conflicts exist or not and potential safety hazards are caused is avoided, the treatment condition is connected through feedback after medicine use of a patient, and data in the data management module are compared; therefore, the medication condition of children can be evaluated conveniently.

The invention discloses an adverse reaction recognition and quick response system and method in an infusion process, and solves the problems that an individual feature similarity model and an individual-drug interaction prediction model in an existing method are static prediction models based on historical data, and dynamic changes cannot be captured in real time; the infusion monitoring method comprises the following steps: acquiring infusion monitoring information in real time; the adverse reaction recognition model recognizes and analyzes the abnormal monitoring data based on risk clustering analysis, calls an adverse reaction strategy in a response database, and triggers an adverse reaction pushing instruction; according to the system, effective infusion monitoring information can be obtained in real time in the infusion process, deep processing such as filtering and noise reduction and wave band periodical detection can be conducted on multi-source infusion monitoring information, dynamic changes in the infusion process are captured in real time through the adverse reaction recognition model, adverse reactions are found in time, and the accuracy of infusion monitoring is improved. And real-time monitoring and rapid response to adverse reactions are realized.

The invention discloses a drug-drug interaction prediction method based on a time step attention graph neural network. The existing graph neural network (GNN)-based method has the limitations of drug node original semantic deviation and insufficient new drug feature representation. A time step attention graph neural network (TSA-GNN) is improved through two mechanisms: 1, a time step attention mechanism allocates adaptive weights for intermediate representation of neighbor aggregation, and balances original semantic reservation and new information acquisition; and 2, a drug similarity propagation mechanism: integrating drug similarity characteristics during training, and effectively representing new drugs through first-order and second-order similarities during reasoning. Experiments show that the advantages of the TSA-GNN are gradually increased along with the increase of the proportion of the new medicine: the AUPR in a hot start scene reaches 0.9803 (superior to 0.9786 of MKG-FENN), the AUPR in a semi-cold start scene reaches 0.7210 (compared with 0.7049), and the AUPR in a complete cold start scene reaches 0.4555 (compared with 0.4162), so that an effective solution is provided for the clinical cold start problem, and the improvement of the safety evaluation efficiency of the new medicine is facilitated.