31 results about "Pharmaceutical Databases" patented technology

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 internet-of-things digital intelligent medicine and medicine interaction virtual-real fusion terminal equipment, and aims to solve the problem of medicine selection deviation caused by insufficient medical knowledge of a user in a traditional medicine purchasing process. The device obtains user symptoms through multi-mode interaction (voice, touch screen and video), generates a combined medication scheme based on a cloud medical knowledge base and a medicine database, and introduces a doctor, pharmacist and licensed pharmacist auditing mechanism to guarantee scientific medication and safe medication of the user. The system supports four payment modes and two medicine taking modes (self-pick-up / delivery), realizes full-process intelligentization from symptom analysis to medicine delivery, and adapts to full-process digitalization from body organ part-part symptom to medicine delivery in local dialects. According to the method, the user can be guided to accurately purchase medicines with scientific and safe medication according to the disease symptoms, the correspondence between the medicines purchased by the user and the disease symptoms is improved, the risk of wrong medication of the user is reduced, and the accuracy of scientific and professional medication is improved.

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

The present invention relates to the field of precision medicine technology, specifically a system, method, and storage medium for interpreting precise tumor medication, including: a data acquisition module for collecting genetic data, clinical data, and drug response data of tumor patients from multiple data sources; a data standardization processing module for formatting and standardizing the data to ensure a consistent format and enable effective processing and analysis in subsequent modules; a genetic data analysis module for performing variation analysis based on the patient's genetic data, identifying gene mutation sites, and generating drug sensitivity analysis results; and a drug screening module for screening drugs that match the patient's gene mutation sites from a drug database based on the genetic data analysis results, and providing drug dosage recommendations. By adopting a technical solution that combines quantum optimization technology with genetic data analysis, the technical effect of significantly improving the accuracy of drug screening and dosage recommendations is achieved.

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

The invention discloses a cerebral apoplexy thrombolysis patient evaluation method, device and equipment and a storage medium, and the evaluation method comprises the steps: obtaining patient information, obtaining the weight data of a patient from a record database, reading the type of a drug, obtaining a dose coefficient and a conversion factor from a drug database, dose data of the thrombolytic drug is calculated through a first formula and is sent to the display module to be displayed, rapid conversion of the administration dose is achieved, calculation errors of the thrombolytic drug dose are avoided, then blood pressure data, heart rate data and blood oxygen saturation data of a patient are obtained in real time, and the accuracy of thrombolytic drug dose calculation is improved. And judging whether the vital signs are correspondingly located in the first range, the second range and the third range or not, and if not, sending out an alarm signal, so as to accurately judge the vital sign data of the thrombolysis patient, timely discover abnormity and send out an early warning.

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.

A system and method for feedback-driven automated drug discovery which combines machine learning algorithms with automated research facilities and equipment to make the process of drug discovery more data driven and less reliant on intuitive decision-making by experts. In an embodiment, the system comprises automated research equipment configured to perform automated assays of chemical compounds, a data platform comprising drug databases and an analysis engine, a bioactivity and de novo modules operating on the data platform, and a retrosynthesis system operating on the drug discovery platform, all configured in a feedback loop that drives drug discovery by using the outcome of assays performed on the automated research equipment to feed the bioactivity module and retrosynthesis systems, which identify new molecules for testing by the automated research equipment.

The invention discloses a target function enrichment analysis method for intervening plateau encephaledema based on ethnic drugs. The method comprises the following steps: acquiring a plateau encephaledema database; the plateau encephaledema database comprises N pieces of plateau encephaledema data information; processing the plateau encephaledema database to obtain plateau encephaledema target spot information; acquiring a ethnic drug database; processing the ethnic drug database to obtain potential target information of ethnic drugs; matching the plateau encephaledema target information with the ethnic drug potential target information to obtain associated target information; processing the associated target spot to obtain a plateau encephaledema network model; processing the plateau encephaledema network model to obtain a key target spot and a signal path; and carrying out experimental verification on the key target spot and the signal path. The invention provides a theoretical basis and a new treatment strategy for prevention and treatment of plateau encephaledema.

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.

A method and apparatus for predicting drug-target interactions based on a representation alignment space. The method initializes the protein representation dimension, drug representation dimension, common embedding representation dimension, the number of multilayer perceptron layers, and batch dimension parameters. Multiple single-source network data are obtained from multiple pharmaceutical databases and compared to construct a biomedical heterogeneous network. Based on a contrastive learning strategy, protein target and drug representations are integrated into a common embedding representation space, and a model is trained using DTI associations. The protein target and its corresponding drug representations are input into the trained model for DTI prediction, obtaining the most relevant drugs. By constructing a biomedical heterogeneous network, the present invention facilitates the prediction of DTI associations associated with unknown drugs. Furthermore, by establishing an embedding representation alignment framework, the method utilizes high-quality representation methods to characterize biomedical networks and biological entities, while also addressing the issue of sparse training data using contrastive loss.

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 invention provides a medicine database platform establishment method based on medicinal plant and compound information. The method comprises the following steps: 1, inputting a disease A; 2, querying the disease A input in the first step in a drug database platform; step 2, judging whether the disease A has a corresponding medicinal plant in a medicine database platform; step 3, inputting a medicinal plant B; 4, querying the medicinal plant B input in the step 3 in a medicine database platform; step 4, judging whether the medicinal plant B has a corresponding compound in the drug database platform; step 5, inputting a compound C; 6, querying the compound C input in the step 5 in a drug database platform; whether the compound C has the corresponding medicine in the medicine database platform or not is judged, perfect information can be inquired clearly, completely and comprehensively in a continuous retrieval, enrichment and perfection mode, and the method has important research significance and use value.

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.