184 results about "Drug interaction" patented technology

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 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.

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 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 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 provides a method for constructing a drug interaction prediction model based on multi-view feature representation, and the method comprises the steps: obtaining the node embedding representation of each drug from a drug knowledge graph through a heterogeneous graph neural network, and obtaining a first feature vector of each drug pair; utilizing a graph attention neural network to obtain graph embedded representation of each drug from the drug molecular graph to obtain a second feature vector of each drug pair, and obtaining a third feature vector of each drug pair from the drug bipartite graph; the first feature vector, the second feature vector and the third feature vector of each drug pair serve as input, the real DDI of each drug pair serves as output, the prediction probability of the multi-layer perceptron is infinitely close to the real DDI of each drug pair by adjusting parameters of the multi-layer perceptron, and a drug interaction prediction model is obtained. According to the method, the distinction degree of the DDI features is improved by using multi-view feature representation, so that the model prediction robustness is enhanced, and meanwhile, the problem of overfitting when the DDI of a new drug is predicted is 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 present invention relates to the use of cannabidiol (CBD) in the treatment of patients with childhood-onset epilepsy syndromes who are concurrently taking the antiepileptic drug clobazam. When CBD is used in combination with clobazam caution should be taken. The patient may need to be cautioned and / or monitored for side effects of a drug-drug interaction 5 between the two medications. In particular the patient should be cautioned and / or monitored for the occurrence of pneumonia. In such a situation the dose of either the CBD and / or the clobazam may be required to be reduced.

The invention belongs to the technical field of medical information, and relates to a drug interaction screening method based on drug use records. The core structure comprises: a data integration module, which obtains a multi-source medication record of a patient in real time through an HL7 / FHIR interface and carries out structured processing; the metabolism engine calculation module is used for identifying a metabolism action mode based on the drug-metabolic enzyme influence relation database and quantifying a net effect value by adopting a dynamic time weighting algorithm; the risk decision module is used for integrating liver and kidney functions and gene polymorphism data and generating a three-dimensional risk matrix through a multi-factor prediction model; and the clinical output module is used for executing risk visualization early warning, alternative medicine recommendation and individualized monitoring scheme generation. Dynamic medication time sequence analysis, individualized risk quantification and clinical decision closed-loop management are realized, and medication safety is improved.

The present invention discloses a method for predicting drug-drug interactions based on secure multi-party computing, comprising the following steps: S1, obtaining a drug-drug interaction network, a drug-protein interaction network, a drug-disease association network, and a drug-side effect association network; S2, calculating Jaccard similarity based on different drug features to obtain similar features between all drugs, and using principal component analysis technology to reduce the dimensionality of all drug similarity features; S3, dividing each user's private feature data into four parts, encrypting them using secret sharing technology, and sending them to four servers using an additional secret sharing mechanism; S4, inputting the four parts of feature data into a preset private deep learning model to predict drug-drug interactions. The present invention enables high-quality collaboration between pharmaceutical companies and research institutions without leaking drug privacy information, thereby improving drug-drug interaction prediction.

The present invention relates to the field related to artificial intelligence and drug discovery. Specifically, a drug-drug interaction (DDI) prediction method based on RGDA-DDI is invented to solve the problem that the existing DDI prediction methods are not ideal. The method is divided into a data encoding module, a feature fusion module and a prediction module. The data encoding module is composed of multiple Residual-GAT submodules, each of which is composed of a graph attention layer, a Normalize layer and a SAGPooling layer, and is used to extract multi-scale features of the input drugs; the feature fusion module is composed of two dual-attention mechanism submodules, which are used for multi-scale drug feature fusion; finally, the fused features are input into the prediction module for prediction. This method overcomes the shortcomings of the existing DDI prediction methods, which lack modeling of multi-scale drug features and lack of mining of potential drug pairs (DDP) features. Experimental verification shows that the prediction performance of this method is better than the recently disclosed drug-drug interaction prediction method.

The invention belongs to the technical field of artificial intelligence and medical information processing, and particularly relates to a pluggable attribute forgetting method and system for drug recommendation. The method comprises the following steps: S1, constructing an erasing module for eliminating sensitive attribute information contained in an embedded vector; s2, constructing a loss function for guiding learning of an erasure module; s3, training an existing drug recommendation model by using the complete training set to obtain stable patient representation and drug interaction modeling ability; s4, inserting the learned erasing module into a patient embedding output path in the drug recommendation model; and S5, receiving the patient embedded information and the drug embedded information by using the interactive modeling ability obtained by the drug recommendation model, and carrying out interactive calculation to generate a recommendation prediction result. The method has the characteristics of effectively relieving the risk of safety information leakage in the drug recommendation system and improving the safety and credibility of the system in a real medical scene on the premise of ensuring the recommendation performance.

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 present disclosure relates to systems and methods for analyzing drug-drug and gene-drug interactions of a user and providing suggestions therefrom. The systems and methods may utilize a database containing a list of genes associated with a plurality of drugs based on clinical relevance of the genes to the effects of the plurality of drugs. The systems and methods may include recommending alternative drugs to a user based on any identified problematic drug-drug or gene-drug interactions.

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.

This invention discloses an antibacterial hydrogel with photothermal-triggered drug release properties, its preparation method, and its applications. The antibacterial hydrogel comprises a hydrogel matrix material, mesoporous polydopamine-encapsulated sea urchin gold nanoparticles loaded onto the matrix material, and a carried antibacterial drug. The hydrogel is polymerized from aldehyde-modified hyaluronic acid and carboxymethyl chitosan, whose Schiff base bonds endow the hydrogel with excellent self-healing properties and stable mechanical properties. The loaded mesoporous polydopamine-encapsulated sea urchin gold nanoparticles can effectively carry the antibacterial drug and simultaneously possess a photothermal heating effect, endowing the hydrogel with excellent antibacterial properties. Controllable drug release can be achieved under near-infrared light irradiation, realizing the synergistic antibacterial effect of photothermal-drug interaction, effectively killing periodontal pathogens, inhibiting periodontal tissue loss, and improving periodontitis.

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.