43 results about "Drug Databases" patented technology

The invention relates to the technical field of medical information, in particular to a medical data conjoint analysis system based on medical knowledge graph driving. The system comprises a medical knowledge graph construction module, a medical data quality evaluation module, a medical feature engineering module and a medical knowledge driven analysis module, and heterogeneous knowledge graph modeling can be performed by integrating medical clinical guidelines, biomedical literatures, a drug database and historical medical data so as to generate a medical heterogeneous knowledge graph; obtaining new medical clinical test information and carrying out delayed contradictory learning update to generate a medical dynamic update knowledge graph; the method comprises the following steps: obtaining multi-modal medical data, carrying out quality verification evaluation and medical feature engineering analysis, carrying out knowledge path joint driving analysis at the same time, generating a decision support reasoning path corresponding to medical clinical knowledge, and outputting a corresponding medical knowledge path confidence coefficient. According to the method, fusion reasoning among cross-source data can be realized by constructing the multi-modal medical knowledge graph.

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 point reading equipment, and discloses a medicine query method, point reading equipment and a storage medium, the medicine query method comprises the following steps: in response to a query instruction for a medicine, under the condition that the query instruction is a voice query instruction input by a user, performing voice conversion on the voice query instruction to obtain a voice query text; performing semantic analysis on the voice query text to determine a target entity and a semantic relationship queried by the user; performing lightweight query in a drug database according to the obtained semantic relationship and the target entity to obtain a query result associated with the drug; and synthesizing a character answer according to the query result, and outputting the character answer in a voice form. According to the drug query method, the user can obtain accurate drug information and clear answering guidance, drug use errors caused by misunderstanding of a specification are avoided, and the wrong drug use rate is reduced.

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.

The invention discloses preposed evaluation software for a parenteral nutrition prescription, relates to the field of parenteral nutrition support treatment, and relates to a whole process of parenteral nutrition. The software comprises a medicine database part, a patient information area, a parenteral nutrition prescription area, a substance and energy recommendation and judgment area, a stability judgment area and an infusion mode and time suggestion area. A doctor can be helped to make the most suitable parenteral nutrition prescription according to the information and the illness state of a patient, and the stability after mixing according to the prescription can be guaranteed. And the most suitable infusion mode and the minimum infusion time suggestion are given. Due to the fact that parenteral nutrition ingredients are complex, the requirements for stability and patient adaptability are high, the calculation workload is large, if calculation is conducted through a formula according to the requirement of a guide, time is long, and errors are prone to occurring, the software writes in the calculation formula recommended by the guide, the whole process of parenteral nutrition calculation is achieved, and energy intake standards, electrolyte intake standards and intake standards of all nutrient substances are included. And the stability is judged, a plurality of different calculation modes are provided, and more choices are provided.

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, which relates to the technical field of medicine management, discloses an intelligent management method for the expiration date of a rescue medicine, comprising the following steps: obtaining priority data of rescue medicines at an early warning level and an emergency early warning level through a medicine database, and carrying out analysis and calculation in combination with the remaining expiration date and the expiration date weight index of the rescue medicines; obtaining a dynamic priority index of the rescue medicines, and sequencing the use priorities of the rescue medicines; according to the invention, by analyzing and calculating the remaining expiration date of the medicine and establishing the expiration date weight index model, the early warning level of the medicine is scientifically divided into three levels of safety, early warning and emergency early warning, so that the accuracy of medicine expiration date management is remarkably improved; and furthermore, through a dynamic priority index algorithm, in combination with multi-dimensional data such as the remaining validity period, the effectiveness weight index, the use frequency and the stock of the medicines, intelligent sorting of the use priorities of the rescue medicines is realized, and the priority use of the high-risk and high-value medicines is ensured.

The invention belongs to the technical field of molecular biology and drug screening, and provides a screening method of capsaicin targeting SOCS5-RBMX protein interaction and application of the capsaicin, an SOCS5-RBMX protein complex structure is analyzed, and SOCS5-RBMX binding structural domains and SOCS5-RBMX binding key sites are determined; the effect of inhibiting protein binding is verified through point mutation of the key sites; determining a binding pocket, taking a drug in the ZINC22 small molecule drug database and a drug approved by FDA as ligand molecules, and taking the binding pocket as a docking region for virtual screening to obtain a compound; the obtained compound is screened through AMDET, and the screened medicine for inhibiting SOCS5-RBMX binding is capsaicin; capsaicin is used for carrying out in-vivo and in-vitro experiments for further screening verification; according to the method, the structure of the SOCS5-RBMX protein complex is analyzed, the binding structural domain and the key site are determined, virtual screening and experimental verification are carried out on the basis of the binding structural domain and the key site, the medicine for inhibiting SOCS5-RBMX binding can be accurately screened out, and the accuracy and efficiency of medicine screening are improved.

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.

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.

This invention proposes a drug recommendation method, system, and storage medium based on a large language model and user psychological inference. The method includes: acquiring the user's purchasing needs input during dialogue interaction; using a large language model to identify the completeness of multiple predetermined attributes of the drugs in the current dialogue; generating corresponding query conditions based on the complete values ​​of each predetermined attribute; retrieving a candidate drug set from a drug database; outputting user psychological need tags from a psychological need inference model using a step-by-step reasoning approach based on the user's historical behavior data, dialogue history, and environmental parameters; inputting the user's psychological need tags, the product characteristics and merchant characteristics of each candidate drug in the candidate drug set, and the user profile into the large language model for candidate drug set filtering, determining the top recommended drugs, and generating corresponding recommendation reasons. This invention can significantly improve the matching accuracy between recommendation results and user psychology, and improve the accuracy of drug search.

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 invention discloses a medicine use identification printing system for clinical medical treatment and an identification printer, and relates to the technical field of identification printing, the system comprises a remote input module used for establishing communication interaction with a handheld terminal, setting identification printing content and printing time based on remote control instruction input, and feeding back printing progress; the local input module is used for inputting based on a local control instruction and setting identification printing parameters as required; the networking configuration module is used for being in real-time butt joint with a hospital information system, acquiring clinical medical medication data and accessing a standardized medicine database updated in real time; and the printing control module is used for matching the format of the identification template, dynamically monitoring and adjusting the printing queue and outputting different types of identification labels based on the control instruction. According to the invention, through deep fusion of a modular hardware architecture and intelligent control logic, the operation efficiency, accuracy and compliance of clinical medical drug use identifier printing are significantly improved.

This invention provides a virtual reality-based drug retrieval training method, system, terminal, and medium, specifically relating to the fields of medical information technology and biomedical engineering technology. The solution includes: constructing several initial drug packaging models based on preset multimodal drug characteristics; constructing a drug database based on the initial drug packaging models and key feature information of several drugs, including target drugs and interfering drugs; rendering a virtual pharmacy environment using virtual reality equipment based on the drug database; and, in response to receiving a retrieval instruction for at least one target drug, performing human-computer interaction using the virtual reality equipment based on the retrieval instruction and the virtual pharmacy environment to obtain the retrieval result. This solution, by constructing a drug database with diverse packaging types and using virtual reality technology to create a realistic virtual pharmacy scene, enables medical personnel to perform immersive drug retrieval operations and training in a virtual environment, thereby improving the efficiency of drug retrieval for medical personnel.

The invention discloses a medicine filling dose automatic identification method and system, and relates to the technical field of production automation, a medicine main database and a main learning model are established, and the main learning model differentiates a corresponding derived learning model according to a medicine type; generating a lightweight learning model for each production batch of each type of medicine based on the derived learning model of each type of medicine, performing adaptive compensation adjustment on parameters of each level of the lightweight learning model based on difference information of the production batches, and fusing each level by outputting a corresponding dynamic fusion coefficient; and monitoring the operation process of each lightweight learning model based on a preset task trigger event, carrying out parameter retraining on the lightweight learning model by sharing the difference characteristic parameters, and carrying out canned dose real-time monitoring identification by using the retrained lightweight learning model. Automatic high-precision monitoring and intelligent optimization in the medicine production process are achieved, and the production efficiency and the product quality are 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 embodiment of the invention discloses a tumor guide medication information generation system and method, electronic equipment and a medium. The system comprises a data acquisition server used for acquiring a tumor guide medication information set; carrying out classification processing on the obtained tumor guide medicine recommendation query statement information set; the cue word generation server is used for generating a content extraction cue word information set; the medication information analysis server is used for performing information analysis processing on the tumor guide medication information set; the tumor drug use database construction server is used for performing drug knowledge extraction processing on the tumor guide drug use analysis information set to obtain a tumor drug use knowledge base; the tumor medication retrieval server is used for generating a tumor guide recommended medication information set and sending the tumor guide recommended medication information set to the user interaction terminal; and the user interaction terminal is used for terminal display. According to the embodiment, the construction time of the tumor drug knowledge base and the display waiting time of the drug recommendation information can be shortened, the waste of display and transmission resources is reduced, and the load of each server is reduced.

The invention provides an intelligent hospital medicine checking system based on image recognition. The intelligent hospital medicine checking system comprises a medicine image acquisition module, a medicine feature analysis module, a medicine information matching module and a checking strategy adjustment module, the medicine image acquisition module is used for extracting visible light and near infrared spectrum characteristics of medicine packages in the multi-angle visible light image sequence of the medicine to be checked; the drug feature analysis module performs multi-scale feature fusion on the surface image sequence to generate a composite feature vector; the medicine information matching module compares the composite feature vector with a standard feature template of a hospital medicine database layer by layer, and outputs a medicine identity verification result and a batch difference identifier; and the checking strategy adjustment module dynamically adjusts the image acquisition parameters and the feature comparison threshold according to the verification result, and triggers a secondary checking process for the medicines with the batch difference identifiers exceeding the preset fault tolerance range. According to the invention, the change of medicine checking from manual leading to intelligent automation is realized, and the workload of medical staff in the medicine checking link 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 present invention provides a pharmacological similarity detection method and system in the field of biomedical information technology combined with a neural network. The method includes: step S1, obtaining a large number of drug names to construct a data set to train the created pharmacological similarity detection model; step S2, the server creates a drug database, allocates an account number and a key to each detection client and stores it in a password table, pre-sets the key into the detection client, and sets an authentication interface; step S3, obtains a similarity detection request sent by the detection client through the authentication interface and performs authentication; step S4, respectively detects the drug name to be detected carried by the similarity detection request through the pharmacological similarity detection model and the drug database, and obtains the first and second similarity detection sub-results; step S5, generates a similarity detection report based on the first and second similarity detection sub-results. The advantage of the present invention is that it greatly improves the accuracy and reliability of pharmacological similarity detection.

The embodiment of the application provides a kind of drug database model construction method and device, electronic equipment and storage medium, belong to artificial intelligence technical field.The method comprises: obtaining drug knowledge text;The field extraction is carried out to drug knowledge text, and the drug attribute field of at least two drug attribute dimensions is obtained;Obtain database simulation model;Wherein, database simulation model includes at least two simulation coordinate axes, and simulation coordinate axis includes multiple simulation intervals;According to the simulation coordinate axis of drug attribute dimension, target coordinate axis is obtained, and according to the simulation interval of target coordinate axis of drug attribute field of drug attribute dimension, target interval is obtained, drug attribute field is stored to target interval, and target database model is obtained.The embodiment of the application can improve the reuse rate of drug knowledge, to realize more efficient, accurate knowledge transmission.

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.

The invention provides a medicine order database table processing method and device, equipment and a medium. The method comprises the steps of obtaining a database table formed by various medicine order data in a target medicine database; sorting the order data according to the key values of the order data in the database table to obtain a target database table; performing apportionment processing on first target field data in each row of medicine data included in the order data to obtain a target order; and outputting a database table comprising the plurality of target orders. According to the scheme of the invention, the rationality of the allocation of the related data of the large-scale drug order data is realized, the interference of invalid data is avoided, and the calculation complexity is reduced.