23 results about "Pharmaceutical Databases" patented technology

A system is provided.SOLUTION: A system comprising: means for receiving clinical test data and medical record information; means for obtaining drug information from a drug database; means for classifying data using a specific algorithm and extracting features; means for recommending optimal drugs based on the features; and means for generating and transmitting a recommended drug list.SELECTED DRAWING: Figure 1

An object of a system according to an embodiment is to provide side effect information of a drug described in a prescription quickly and in detail.SOLUTION: A system includes a prescription information inputting unit, a generation AI, an information acquiring unit, and an information providing unit. The prescription information input unit inputs prescription information. The generating AI analyzes the prescription information inputted by the prescription information inputting unit. The information acquisition unit acquires information from a plurality of medicine databases. The information providing unit provides detailed side effect information based on the information acquired by the information acquisition unit.SELECTED DRAWING: Figure 1

Disclosed are methods for assessing severity, determining future risk, matching with a drug treatment, and measuring response to treatment, for memory dysfunction, Alzheimer's disease, and cognitive decline. Also disclosed are new methods of use for drugs and natural compounds repurposed for use in improving memory, as well as for preventing and treating memory disorders, Alzheimer's disease and cognitive decline. All the above-mentioned methods are computer assisted methods analyzing the expression of panels of genes, clinical measures, and drug databases. A universal approach in everybody, as well as a personalized approaches by gender, and by diagnosis, are disclosed.

The invention relates to the field of drug utilization value analysis, and particularly discloses a data stream-based drug utilization value analysis system, which comprises a data layer, an application layer and a user layer, according to the method, the multi-source drug data stream is constructed and stored in the multi-source drug database, and based on systematic management of drug information, drug use errors caused by drug name confusion, dosage errors and dosage form confusion can be reduced, the drug use safety is improved, and the method is beneficial to quickly and accurately retrieving and querying specific types of drug information; by integrating the elderly physiological features and the drug feature information, the drug utilization value analysis model is constructed, the elderly physiological features and the drug features are comprehensively considered, the utilization value of the drug on the elderly can be comprehensively evaluated, and the influence of the drug on the elderly patient can be predicted more accurately.

A system for redistributing medication including a computing device configured to receive a communication regarding a medication donation from a donor, wherein the communication includes donor medication information regarding the medication donation. The computing device further configured to verify the medication donation including verifying the identity of the donated medication. Verifying the identity of the donated medication includes collecting actual medication information. The computing device also configured to enter final medication information corresponding to the medication donation into the medication database. The computing device configured to match the donated medication to a recipient selected from a recipient database as a function of a set of associated recipient data.

The application discloses application of an HTRA1 inhibitor in preparation of a drug for treating pancreatitis-cancer transformation, and belongs to the technical field of medicines.The HTRA1 inhibitor is screened through a drug database, the HTRA1 inhibitor is used to treat a pancreatic cancer cell line and a mouse pancreatitis-cancer transformation in-vivo model, it is proved that the HTRA1 inhibitor Carfilzomib can inhibit the malignant phenotype of Panc1 cells in vitro, and can inhibit the transformation of a KC mouse stimulated by cerulean into a pancreatic ductal adenocarcinoma in vivo, and the application provides a basis for using the HTRA1 inhibitor as the drug for treating pancreatitis-cancer transformation.

The invention discloses a drug interaction prediction method based on similarity network fusion, and relates to the technical field of biomedicine, and the method comprises the steps: integrating drug data sets in a plurality of well-known drug databases, and extracting drug feature data; calculating similarity according to features extracted from the drug data set, and constructing a similarity matrix; the entropy value of each similarity matrix and the pairwise distance between the similarity matrixes are calculated, and the similarity matrix with the maximum information amount is preferentially selected; combining the selected similarity matrixes by adopting a similarity network fusion technology to obtain a fused similarity matrix; constructing an interaction matrix according to the obtained fusion similarity matrix; and training a convolutional neural network (CNN) model by using the fusion similarity matrix and the interaction matrix of the drug, and realizing drug interaction prediction on test data by using the trained CNN model. By combining the similar network fusion technology and the convolutional neural network model, the accuracy and reliability of drug interaction prediction are remarkably improved.

The invention discloses a prescription drug interaction auditing method and system based on a knowledge graph, and relates to the technical field of medical information, and the method comprises the steps: constructing a drug ontology model, executing multi-source knowledge extraction from a drug specification, a clinical guide, a medical literature, a drug database and hospital historical prescription data, performing knowledge fusion to generate uniform medicine knowledge representation, constructing a medicine knowledge graph containing western medicine, traditional Chinese medicine and Chinese and western medicine combined action sub-graphs, performing prescription medicine identification, performing pairwise pairing analysis and multi-medicine combined cascade effect analysis on prescription medicines, and calculating a personalized medication risk value in combination with patient information. According to the method, the multi-drug combination cascade amplification effect and the Chinese and western medicine combination risk can be identified, and personalized drug use risk assessment is realized.

Disclosed are methods for assessing severity, determining future risk, matching with a drug treatment, and measuring response to treatment, for stress disorders. Also disclosed are new methods for use for drugs and natural compounds repurposed for use in reducing stress severity, as well as for preventing and treating stress disorders. All the above mentioned methods are computer assisted methods analyzing the expression of panels of genes, clinical measures, and drug databases. A universal approach in everybody, as well as a personalized approaches by gender, and by diagnosis, are disclosed.

The invention relates to an accurate medication time recommendation method and system based on a multi-organization rhythm map. The method comprises the following steps: constructing a drug database and a rhythm expression database; acquiring a drug identifier input by a user, performing retrieval in a database, performing rhythm feature analysis based on a retrieval result, and generating a drug-target rhythm analysis report; based on the drug information queried by the two-stage serial query architecture, querying all biological pathways and related genes in which the drug participates, carrying out standardized conversion on gene identifiers, carrying out rhythm feature analysis, and generating a drug-pathway rhythm analysis report; and taking the generated analysis report as input, carrying out semantic integration and reasoning through a locally deployed large language model, and automatically generating an optimal medication time suggestion. Compared with the prior art, the method has the advantages that batch rhythm characteristic analysis of drug target genes and related pathway genes is realized, the analysis efficiency and repeatability are greatly improved, and the decision intelligence level is improved.

The present invention relates to: a compound for inhibiting SOCS3 activity; a pharmaceutical composition for treating allergic diseases, comprising same as an active ingredient; and the like. More specifically, compounds capable of binding were derived from a drug database through AI-based SOCS3 binding site simulation, and it has been confirmed that these compounds actually inhibit SOCS3 and increase JAK2 kinase activity and the degree of STAT3 phosphorylation, and thus can be used for preventing or treating allergic diseases.

The invention relates to a method for applying a medical big language model of an integrated knowledge graph to drug analysis and diagnosis support. The method comprises the steps of performing streaming analysis and entity recognition on a structured pharmaceutical database file, generating a drug record set containing standardized biomedical entity annotations, and constructing a graph structure knowledge base and a vector index base in parallel; receiving a natural language query, extracting drugs and disease entities and mapping the drugs and disease entities into standardized identifiers; synchronously retrieving the structured evidence set and the text evidence set; assembling an enhanced prompt text containing the task instruction, the verified evidence and the query according to the template; and inputting the large language model subjected to biomedical task fine tuning, and generating a binding response. By adopting the method, the problems of static stiffness, pure language model illusion and insufficient interaction of a structured knowledge base of a traditional system can be solved, reliable decision assistance is provided for replacement treatment and interaction review under the condition of drug shortage, and the drug use risk is reduced.

The invention relates to the technical field of drug knowledge maps, and provides a drug database knowledge map construction system, which comprises a data acquisition module, an analysis module, a map module, a risk assessment module and a verification module, key feature data information and map abnormal feature parameter information of target compound drugs of various compound structure types in a drug database are collected, drug key feature factors and map abnormal feature characterization values are analyzed, and whether system operation meets the standard or not is determined through a map module; and if not, starting a verification module based on the map structure risk tendency type, and adopting a corresponding processing mode based on the determined reason for not meeting the standard. By converting discrete drug data into a computable and explorable relation network, a structured knowledge graph is provided for drug research and development and clinical research, and system efficiency and accuracy are improved.

The application discloses an infusion pump control method and product, relates to the field of intelligent medical instruments, and combines a pre-stored knowledge base in a central controller to verify and calculate infusion parameters; and further generates a target infusion scheme, which includes the step of presetting a threshold value. The traditional parameter checking relying on the memory and experience of medical staff is converted into automatic completion by system software and logical verification based on an authoritative drug database. Through sensors (flow, pressure, etc.), the external environment is sensed in real time, compared with the expected model in the internal "target scheme", and the output (peristaltic pump driving instruction) is dynamically adjusted. This significantly improves the accuracy, stability and adaptability to individual differences / environmental changes of infusion, makes the infusion process from mechanization to intelligence, can be uniformly monitored and managed through remote control and monitoring of the infusion pump, and improves intelligent network processing capability.

The invention discloses an asymmetric drug-drug interaction prediction method and system based on a graph network, and the method comprises the steps: obtaining drug data based on a comprehensive drug database, carrying out the data preprocessing, and constructing drug-DDI graph data and drug-drug graph data; introducing a graph attention network and a heterogeneous graph neural network, and constructing a heterogeneous graph neural network asymmetric DDI prediction model; and based on the heterogeneous graph neural network asymmetric DDI prediction model, performing drug interaction probability prediction on the drug-DDI graph data and the drug-drug graph data to obtain an asymmetric drug-drug interaction prediction result. The method can generate the interaction probability, achieves the precise prediction of the asymmetric DDI, and improves the accuracy and reliability of an asymmetric drug-drug interaction prediction result. The asymmetric drug-drug interaction prediction method and system based on the graph network can be widely applied to the technical field of drug interaction.

According to the present invention, a system: acquires a dataset composed of gene expression data of a sample belonging to either of two groups; generates a gene network representing interactions between a plurality of genes by referring to a pathway database; selects one gene from among the plurality of genes; extracts a path by following the gene network downstream with the selected gene as a starting point; calculates path scores by executing enrichment analysis by using, as inputs, a list of genes included in the path and the dataset; generates a ranked gene list by ranking the plurality of genes on the basis of the path scores of the plurality of genes; and calculates drug scores by executing the enrichment analysis by using, as inputs, a list of target genes of drugs stored in a drug database and the ranked gene list.

The invention belongs to the technical field of medical nutrition management, and particularly relates to a nutrition intake difference value analysis and quantitative matching system under a complex chronic common disease scene, which comprises a common disease nutrition demand knowledge base module, a common disease nutrition demand knowledge base module, a common disease nutrition demand knowledge base module, a common disease nutrition demand knowledge base module, a common disease nutrition demand knowledge base module and a common disease nutrition demand knowledge base module, setting a nutritional requirement priority; and the medicine-nutrition interaction adaptation module is used for generating a final nutrition constraint which overlaps the medicine influence by integrating a clinical chronic disease medicine database, automatically synchronizing a medicine use record of a patient, identifying a taboo nutrient corresponding to a medicine and recommending a collaborative nutrient, and performing risk early warning and replacement recommendation on a conflict scheme. The co-disease classification system is constructed according to the organ system and the disease severity, and the multi-dimensional nutrition constraint rule is matched for each type of co-diseases, so that the multi-dimensional nutrition constraint accurate adaptation in a complex co-disease scene is realized, and the limitation of nutrition management of a single disease is broken through.

Target genes are identified with high accuracy and the effectiveness of drugs is evaluated. [Solution] The system acquires a dataset consisting of gene expression data of samples belonging to one of two groups, references a pathway database to generate a gene network representing the interactions of multiple genes, selects one gene from the multiple genes, traces the gene network downstream starting from the selected gene to extract a path, calculates a path score by performing enrichment analysis using as input a list of genes included in the path and the dataset, ranks the multiple genes based on the path scores of the multiple genes to generate a ranked gene list, and calculates a drug score by performing enrichment analysis using as input a list of genes that are targets of drugs stored in a drug database and the ranked gene list.

An information provision device 1 has: an information acquisition unit 131 that acquires health information indicating a health condition of a user; a query creation unit 133 that creates a medication database search query for searching for a medication that is suitable for the user, by inputting first instruction text together with the health information into a first large language model; a medication specification unit 134 that specifies one or more medications corresponding to the health information, by using the search query to search the medication database; and an output unit 135 that outputs medication information relating to the medication which was specified by the medication specification unit 134.

The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a large language model language inference analysis, and relates to the technical field of medical diagnosis. The application discloses an online medical inquiry method based on a

The application discloses a kind of based on artificial intelligence's medication strategy generation system design method, it is related to wisdom medical treatment and biological information technology field, including: the drug database comprising drug physicochemical property and therapeutic window parameter and the patient database comprising patient dynamic and static information;Utilize the decision model of the Transformer architecture and output the candidate drug set according to patient symptoms;Optimization model is input to the candidate set and patient characteristics, generate optimal medication strategy by calculating and minimizing preset multidimensional loss function;Run monitoring model to compare real-time blood drug concentration and therapeutic window parameter, predict change trend and generate real-time revised medication strategy.The application solves the subjectivity problem of traditional medical treatment relying on experience through "decision-optimization-monitoring" closed loop, realizes the personalized precision dosing and dynamic adjustment of considering efficacy, safety, economy and targeting.

The invention relates to the technical field of medical data management, in particular to a medicine reforming method and system based on a medical end and a patient end, and the method comprises the following steps: constructing a medicine database based on medicine use information, synchronously updating management configuration, extracting standard parameters, screening abnormal medicines, generating medicine use suggestions, and notifying related personnel. And optimizing a drug index path and a permission log, analyzing a trend and use stability, and obtaining a drug use trend analysis conclusion. According to the invention, through integration of the medication records and extraction of the standard parameters, identification and prompt of dose conflicts, time overlapping, repeated medication and interaction are realized, medication use stability is analyzed based on abnormal data, medication in high-frequency and fluctuation regions is optimized, compliance review is carried out in combination with permission records, information processing precision is improved, and accuracy of medication management is improved. Dynamic adjustment of an index path is promoted, a prediction data basis is established based on a use trend, wrong record accumulation caused by information lag is avoided, and standardized management and full-process visual tracking of medication information are realized.

The application relates to the technical field of drug knowledge graph, and provides a knowledge graph construction system of a drug database, which comprises a data acquisition module, an analysis module, a graph module, a risk assessment module and a verification module; key characteristic data information and graph abnormal characteristic parameter information of target compound drugs of various compound structure types in the drug database are acquired; drug key characteristic factors and graph abnormal characteristic representation values are analyzed; whether the system operation conforms to the standard is determined through the graph module; if the standard is not met, the verification module is started based on the graph structure risk tendency type; and corresponding processing modes are adopted based on the determined reasons for not meeting the standard. Discrete drug data is converted into a calculable and explorable relationship network, a structured knowledge graph is provided for drug research and development and clinical research, and the system efficiency and precision are improved.