82 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 discloses a blockchain-based intelligent tracing and risk early warning system for medicine supply data, and relates to the technical field of data processing, and the system comprises the steps: carrying out the circulation node extraction of a medicine supply chain, and obtaining a plurality of medicine supply participants; performing distributed block deployment to obtain a medicine supply block chain; acquiring a medicine database, and acquiring a medicine supply data set; performing classification identification and mapping encryption, and uploading to the medicine supply block chain to obtain a block chain distributed account book; obtaining an access smart contract, and obtaining target drug supply data; and performing risk response early warning on the target drug based on the target drug supply data. The technical problems that in the prior art, traditional Chinese medicine supply data tracing is difficult and inaccurate, and potential risks of medicine supply are difficult to find and accurately evaluate in advance are solved, and the technical effects that accurate and rapid tracing of target medicine supply data is achieved, the potential supply risks are found in time, and effective response and early warning are carried out are achieved.

The invention relates to the field of intelligent medical services, and discloses an intelligent medicine shopping guide and recommendation system for a drugstore, which comprises a user information collection module, a medicine database module, a symptom matching module, a recommendation optimization module and a result output module, the user information collection module is used for receiving health data and personalized requirements input by a user and converting the health data and the personalized requirements into structured user feature data; the drug database module stores components, indications, prices and drug interaction information of drugs, and constructs a drug interaction knowledge graph for representing an interaction relationship between drugs and a risk thereof. By introducing a drug interaction knowledge graph, the interaction risk between drugs is dynamically evaluated, and the risk information is fused into a recommendation decision. Compared with a recommendation method which only depends on static rule matching in the prior art, the problem that potential drug conflicts in recommendation results are difficult to find is effectively solved, and the drug use safety is improved.

The invention relates to the technical field of precision medical treatment, in particular to an interpretation system and method for tumor precision medication and a storage medium, and the system comprises a data collection module which is used for collecting gene data, clinical data and drug response data of tumor patients from a plurality of data sources; the data standardization processing module is used for carrying out formatting and standardization processing on the data, so that the data formats are consistent, and effective processing and analysis can be carried out in subsequent modules; the gene data analysis module is used for performing variation analysis according to the gene data of the patient, identifying a gene mutation site and generating a drug sensitivity analysis result; and the drug screening module is used for screening drugs matched with the gene variation sites of the patient from a drug database according to the gene data analysis result, and providing drug dosage recommendation. By adopting the technical scheme of combining a quantum optimization technology and gene data analysis, the technical effect of remarkably improving the drug screening and dose recommendation precision is achieved.

The invention relates to the technical field of artificial intelligence, discloses a medication consultation method and device based on a large language model, electronic equipment, a computer readable storage medium and a computer program product, and is used for solving the problems that a medication recommendation result generated by a medication consultation method in the prior art is not strict enough; and more suitable drugs cannot be recommended to the user. The method comprises the following steps: receiving and calling a large language model to understand a first medication consultation question, and rewriting the first medication consultation question to generate a second medication consultation question; calling a large language model to perform text semantic analysis on the second medication consultation question, and extracting medication consultation keywords; searching medicine information matched with the medication consultation keyword in a pre-constructed medicine database to obtain a medicine result set; and performing deep semantic analysis based on the drug result set and the second medication consultation question, performing optimization sorting on the drug information, obtaining a target drug, and generating a suggestion reply of medication consultation.

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.

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.

The invention relates to the technical field of drug therapy, in particular to a DRG paid drug therapy scheme analysis and evaluation method, device and system and a medium, and the method comprises the steps: obtaining the medical record information of an inpatient, the medical record information comprises the basic information, diagnosis information and treatment scheme information of the patient; matching a corresponding treatment cost standard in a DRG cost library based on the medical record information; matching a corresponding treatment drug combination in a drug database based on the treatment cost standard and the treatment scheme information; obtaining validity data of each drug in the therapeutic drug combination, and calculating the validity of the drug combination; and generating a drug treatment scheme recommendation sequence based on the drug combination validity, then obtaining historical drug use data of a patient, and correcting the recommendation sequence based on the historical drug use data to obtain a final recommendation scheme. Therefore, the effect of the DRG pharmaceutical composition on the patient can be comprehensively evaluated, and the effectiveness of treatment 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.

The present invention discloses a real-time medicine box recognition method and system based on deep learning, belonging to the field of medicine management technology. The method comprises the following steps: setting up a camera above the medicine box to collect image information of the medicine box's appearance; using a pre-trained medicine box recognition model based on Yolov5 to obtain medicine box edge and appearance-based medicine box recognition results; performing text detection using a pre-trained DBNet-based text detection model to obtain the area of the medicine box text; performing text recognition using a pre-trained DenseNet and CTC-based text recognition model to identify the text information of the medicine box; and performing a low-threshold fuzzy search and matching on each word or phrase in the text recognition result in turn in a medicine database. The present invention can effectively solve the problem of manual verification required when dispensing medicines in pharmacies. Moreover, the present method greatly ensures the accuracy of recognition and reduces the workload of pharmacy workers.

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.

The present invention relates to an infusion pump system which enables reliable, low-error workflows without complicated retrofit or integration of barcode scanners and the like. The infusion device is provided with a certain number, especially a plurality of infusion pumps; a central data processing device; a number, in particular a plurality of pharmaceutical containers, each infusion pump communicating with the data processing device and having a connection or receptacle for one of the pharmaceutical containers; the data processing device comprises a drug database having a plurality of drug data items; the corresponding medicine data items are associated with short confirmation codes; a respective pharmaceutical container is associated with one of the medication data items and is identified or marked with a respective short acknowledgement code; a respective infusion pump is associated with an input element for inputting one of the short acknowledgement codes; a respective infusion pump has a configuration unit which retrieves relevant drug data items from a drug database or a local copy thereof depending on a short acknowledgement code entered at an input element and activates drug-specific settings or pump functions configured therein.

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 discloses a medicine checking and automatic distributing method based on machine vision. The method comprises the steps that a medicine rechecking area is determined, an image of a medicine sorted by a pharmacist is obtained through collection equipment, the feature vector of the medicine is extracted, a medicine recognition model is constructed, the feature vector is input into the model for recognition, medicine recognition information is obtained, and the medicine rechecking area is used for checking the medicine. The method comprises the following steps: acquiring the position of an acquisition device, comparing the information with data in a medicine database, and determining the automatic distribution of medicines or feeding back abnormal information according to the comparison result, so that the position of the acquisition device is reasonably set, the medicine feature vector processing flow is optimized, and an automatic distribution and abnormal feedback mechanism is introduced; the accuracy of medicine identification and the robustness of the system are remarkably improved, errors in manual operation are effectively reduced, and the efficiency and safety of medicine checking and distribution are improved.

The present invention relates to an intelligent drug classification system, which relates to the field of artificial intelligence technology. The system mainly includes: obtaining the outer packaging image of the drug to be classified through the graphic acquisition device; determining whether a drug identifier uniquely corresponding to the drug to be classified can be obtained according to the outer packaging image; if a drug identifier uniquely corresponding to the drug to be classified can be obtained, obtaining the detailed drug data corresponding to the drug identifier according to the drug database; if a drug identifier uniquely corresponding to the drug to be classified cannot be obtained, obtaining the instruction manual image of the drug to be classified; determining the corresponding detailed drug data according to the instruction manual image; processing the detailed drug data based on the user-defined classification basis to obtain the classification result corresponding to the drug to be classified.

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 present invention provides a drug detection method and system based on big data, the method comprising: detecting the target production drug corresponding to the liquid drug production line in real time, and detecting the target drug information corresponding to the target production drug in real time in the preset drug database; when it is detected in real time that the target production drug is transferred to the first preset detection area, judging in real time whether the production dosage of the target production drug meets the first preset requirement according to the first preset rule; if so, adding a first label to the target production drug, and transferring the target production drug to the second preset detection area, and judging in real time whether the production components of the target production drug meet the second preset requirement according to the second preset rule; if so, adding a second label to the target production drug, so as to complete the detection of the target production drug. The present invention can complete the detection of liquid drugs in an all-round way, and correspondingly improve the work efficiency.

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 present invention discloses a clinical trial drug access management method and system based on a smart medicine cabinet, the method comprising: establishing a drug access database; in the drug storage stage, analyzing the historical drug access data to determine the predicted number of door openings in a first preset time interval; judging whether the predicted number of door openings is less than the preset door opening threshold; if so, performing constant temperature control on the inside of the smart medicine cabinet based on the recommended temperature; if not, determining the first temperature and the second temperature by drawing a first temperature change curve; determining the first temperature interval based on the first temperature and the second temperature, and performing temperature control on the inside of the smart medicine cabinet based on the first temperature interval; in the drug access stage, controlling the air outlet device to perform auxiliary temperature control, and giving an early warning reminder based on the access duration; when the access is completed, resetting the first preset time interval based on the access completion time. The present invention reduces the problems of over-temperature failure of clinical trial drugs by regulating the internal temperature of the medicine cabinet.

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 present application provides a recommendation method, a recommendation device, an electronic device, and a storage medium, belonging to the fields of digital medicine and artificial intelligence technologies. The method includes: obtaining target object data, where the target object data includes the medical consultation disease data and the medical consultation prescription data of the target object, and the medical consultation prescription data includes the image data of the outer packaging of the medicine; performing character recognition processing on the image data through an image recognition model to obtain target medicine text data; performing vectorization processing on the target medicine text data to obtain a target medicine text vector; performing content matching processing on the target medicine text vector and preset reference medicine information to obtain target medicine information; inputting the target medicine information into a medicine database for query processing to obtain target medication data; and performing integration processing on the target medication data, the medical consultation disease data, and the target medicine information to obtain recommended medication data. The present application can improve the accuracy of medication recommendation.

The invention discloses a document relationship extraction-oriented adaptive data correction method. The method comprises the following steps of: obtaining a drug database; training a classifier for identifying potential relation facts, including positive training and negative training; a self-adaptive threshold is introduced for each relation fact, false positive is filtered in a self-adaptive mode, and false negative is marked again; iteratively executing de-noising training and data revision based on confidence coefficient; and the interaction between the medicines is identified and output, so that side effects or adverse reactions caused by improper medication are avoided. According to the invention, the distinguishing capability of relation facts is enhanced, and the influence of noisy annotations is reduced; self-adaptive data revision is introduced to revise the relation fact of long-tail distribution; iterative training mines valuable relational facts from a noisy dataset.

The invention relates to an intelligent medicine classification system, and relates to the technical field of artificial intelligence. The system mainly comprises the following steps: acquiring an outer package image of a to-be-classified medicine through a graph acquisition device; determining whether a medicine identifier uniquely corresponding to the to-be-classified medicine can be obtained or not according to the outer package image; if the drug identifier uniquely corresponding to the to-be-classified drug can be obtained, obtaining drug detailed data corresponding to the drug identifier according to the drug database; if the drug identifier uniquely corresponding to the to-be-classified drug cannot be acquired, acquiring a specification image of the to-be-classified drug; determining corresponding detailed medicine data according to the specification image; and processing the detailed data of the medicine based on a classification basis defined by a user to obtain a classification result corresponding to the to-be-classified medicine.

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 provides a multi-object drug traceability code identification and checking method and system based on visual identification. The method comprises the following specific steps: obtaining a multi-angle drug package image of a multi-object drug based on visual identification; extracting a traceability code area on the multi-angle medicine packaging image; and decoding the traceability code in the traceability code area to obtain traceability code information, and checking the traceability code information with the drug database. Through a multi-angle image acquisition technology and an intelligent algorithm and in combination with bar code automatic identification and drug information matching functions, drug checking and traceability code identification can be rapidly and accurately completed, and the manual operation intensity and the error rate are significantly reduced.