372 results about "Medication information" patented technology

The invention provides a traditional Chinese medicine physique dynamic change identification system. The system comprises a data acquisition module, a feature extraction module, a deep learning analysis module, a physique analysis and prediction module and a diagnosis report generation module. The data acquisition module is used for acquiring tongue image video streams, pulse condition waveforms and body surface infrared thermal imaging data and recording environment temperature and humidity, user behavior logs and medication information. The feature extraction module is used for extracting various feature data. And inputting the multi-dimensional data set into a deep learning analysis module, and calculating the transformation relation probability of the traditional Chinese medicine constitution types. The physique analysis and prediction module judges the current physique type based on the probability data and predicts the physique evolution trend in combination with historical data, and finally the diagnosis report generation module outputs a diagnosis report containing physique evolution analysis and conditioning suggestions. According to the method, multi-dimensional physiological, environment and behavior data can be integrated, dynamic identification of traditional Chinese medicine physique is realized, the accuracy of physique analysis is improved, and a personalized health management scheme is provided.

The invention provides a chronic disease co-patient accurate medication real-time management system based on an AI large model, and the system comprises accurate medication guided by the AI large model, through the mathematical modeling and simulation technology, the related information of patients, medicines and diseases is integrated, and accurate medication is guided. According to the method, the administration scheme can be formulated more accurately, the curative effect is improved, side effects are reduced, a patient medication report is provided for physicians and pharmacists, the physicians and pharmacists can dynamically master patient medication information in real time, reference is provided for the physicians to formulate the next medication scheme, and the treatment effect on chronic disease patients is improved.

The invention discloses a tumor patient medication side effect management method and system based on big data analysis, and the method comprises the following steps: collecting and preprocessing patient data and drug data, and extracting patient features and drug features, the patient data comprising genome data provided by a patient; constructing a prediction model which comprises a time sequence analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model and is used for predicting risk scores of different types of side effects of the patient by taking the drug characteristics and the patient characteristics as input of the prediction model; inputting the basic information, the medication information and the genome data of the current patient into the trained prediction model to obtain risk assessment of various side effects of the current patient; according to the risk assessment result, personalized adjustment suggestions suitable for the patient are selected from a preset intervention scheme library and provided for the current patient. The scientificity and accuracy of administration side effect management in the treatment stage can be effectively improved.

The invention discloses an intelligent infusion monitoring management system, and relates to the technical field of medical equipment, and the system comprises the steps: collecting patient data, synchronizing the patient data to a central monitoring system through a block chain technology, and generating a personalized infusion scheme; based on a personalized infusion scheme, a medical worker uses a mobile device to carry out identity verification on a patient and obtain medicine information, and a voice assistant function is integrated; the monitoring module is responsible for monitoring vital signs of a patient and the temperature of a ward by using wearable equipment and a temperature sensor, and adjusting a personalized infusion scheme and liquid temperature according to the vital signs and the temperature of the ward; the risk value prediction module is responsible for predicting risk values of blockage and leakage according to the adjusted personalized infusion scheme and the liquid temperature and setting a graded alarm mechanism; patients and family members receive the infusion progress through the intelligent infusion monitor. According to the invention, the block chain technology and the advanced artificial intelligence algorithm are integrated, so that the health data and medicine information of the patient can be efficiently managed and analyzed.

The invention discloses a rehabilitation exercise system and method based on intelligent interaction, and relates to the technical field of medical rehabilitation, intelligent management of basic information, medical history information, treatment information and medication information of patients is realized through a patient information management module; personalized exercise plans and real-time action guidance are provided for the patient through the rehabilitation exercise module; dynamically evaluating the rehabilitation state of the patient through a rehabilitation evaluation module; a friend communication platform is established through the community interaction module, and science popularization education is provided; and real-time monitoring and early warning of the doctor on the rehabilitation process of the patient are realized through the doctor supervision module. The artificial intelligence technology is adopted, the rehabilitation actions of the patient are monitored and corrected in real time through the camera, the rehabilitation state of the patient is evaluated through the electronic scale and the intelligent scoring system, the exercise plan is dynamically adjusted according to the rehabilitation condition of the patient, the rehabilitation exercise accuracy and the patient compliance are effectively improved, and the rehabilitation effect of the patient is improved. And the intelligent and electronic management of the whole rehabilitation exercise process can be realized.

The invention relates to the technical field of drug analysis, and discloses an intelligent drug stability analysis and monitoring system, which comprises a drug information acquisition module, a drug stability analysis module, a drug prediction module, a dynamic regulation and control module and an early warning display module, through the arrangement of the drug stability analysis module and the drug prediction module, the drug storage environment is analyzed to obtain the stability condition of the drug, then the remaining validity period of the drug is obtained, and the stability change of the drug is monitored in real time to prolong the remaining validity period of the drug; meanwhile, liquid medicine and solid medicine are subjected to special analysis, the degradation rate constant of the medicine is obtained on the basis of a second improved Arrhenius formula for the solid medicine, and a physical property cross-border line is set, so that the stability of the medicine is improved by analyzing the stability of the medicine and regulating and controlling environmental parameters, and the stability of the medicine is improved. The medicine validity period and storage time are prolonged, and the storage strategy is dynamically adjusted in combination with medicine chemical characteristics.

The invention relates to the technical field of medical treatment, and discloses an intelligent subject evaluation method based on multi-dimensional data, which comprises the following steps: acquiring the multi-dimensional data corresponding to a subject, inputting the multi-dimensional data into a pre-trained evaluation model, and acquiring an analysis report of the subject participating in clinical research, the multi-dimensional data comprises clinical symptoms, traditional Chinese and western medicine diagnosis, medication information and social background of the subject, and the analysis report comprises accuracy, compliance and risk; based on the analysis report, determining a corresponding group-entering suggestion and a treatment research scheme for each subject, the treatment research scheme including drug selection, dose adjustment and a follow-up plan; in the clinical research process, the latest research data are continuously obtained, and the analysis report and the treatment research scheme of the subject are adjusted based on the latest research data. The quality and effectiveness of clinical research can be improved.

The invention discloses a multi-source data-based medical temperature-adjusting atomization drug delivery system for the pneumology department, relates to the technical field of medical temperature-adjusting atomization drug delivery, and aims to solve the problems that the atomization effect is poor and the atomization process is not intelligent. By adjusting atomization power in different respiratory cycle stages, monitoring airway abnormity and respiratory fluctuation in real time to correct parameters and adapting temperature control and atomization components as required, medicine waste and airway stimulation can be reduced, variables such as air source fluctuation and liquid medicine viscosity can be coped, atomization stability is guaranteed, administration efficiency and safety are improved, and the method is suitable for large-scale popularization and application. The requirements of patients with weak respiratory functions are met, the basic information unit is linked with the medical history of the patients and the medicine information to determine the safe temperature, the atomization rhythm is matched in combination with the respiratory parameters, temperature deviation caused by information dispersion can be avoided, manual errors are reduced, and the medication safety is improved.

The invention discloses a disease drug association prediction method and system based on a knowledge graph, and solves the problem that effective fusion of a clinical knowledge graph and an omics knowledge graph in the existing knowledge graph is not realized. The method comprises the following steps: acquiring entities in a medical record, and acquiring disease entity information and disease relationship information according to an ICD-9 coding system; according to the disease entity information, obtaining corresponding drug information and drug relationship information in a DugBank database; fusing the disease entity information, the disease relationship information, the drug information and the drug relationship information link into a pre-constructed knowledge graph; inputting the disease and the drug into a prediction model trained by the fused knowledge graph to obtain a disease-drug association score; clinical data and omics data can be effectively fused, and the relationship between diseases and drugs can be predicted more accurately.

The invention discloses a safe medication system and a safe medication method for elderly patients, which can monitor whether the elderly take medicine according to medical advice or not and monitor the health state of the elderly. The physiological data of the patient is monitored by the wearable end, so that the health state of the patient can be roughly judged by combining data such as heart rate, electrocardio, respiratory rate and blood pressure, and whether the patient is in danger or not can be found in time; and meanwhile, whether the drug effect of the patient is exerted and whether the patient has drug safety can be known by monitoring the physiological data for a period of time after drug use. According to the invention, the medical advice is uploaded or issued through the medical care terminal, and the medication information in the medical advice is analyzed into the medicine type counting quantity and the medication mode of the medication, so that the medication plan of the patient is planned, and then the patient is prompted to take the medication on time according to the plan. And the medicines are distributed through the intelligent terminal module, and the intelligent terminal module communicates through the system, so that the medicines can be distributed according to the control of the medical care terminal, the management terminal and the system to prevent the patient from eating by mistake, and the medication safety is greatly improved.

The invention discloses a graph attention enhancing drug recommendation model algorithm fused with clinical signs, which comprises the following steps: representing a patient according to complete medical information, and capturing a patient health condition change rule contained in EHRs through a gating neural network; in the patient characterization process, medicine combination knowledge and DDI knowledge are effectively utilized through GNN and are fused into the patient characterization process; the method comprises the following steps: reading patient medication information which is integrated with DDI knowledge and has patient similarity and historical diagnosis and treatment records through a two-stage attention module, and converting drug recommendation into a multi-label prediction task; the method has the advantages that the related performance of the model in combined drug prediction is superior to that of a comparison model, the relatively good Jaccard index, F1-Score, PR-AUC and DDI rate are obtained, and a new thought is provided for clinical decision making and drug recommendation based on the graph neural network.

The invention relates to the technical field of information processing, in particular to a medicine image recognition management method based on multi-modal learning, which comprises the following steps: S1, collecting medicine image data and text data of corresponding medicine names, chemical components, major functions and instructions; s2, cleaning the medicine image data, removing noise, classifying and labeling the medicine image data, and extracting and labeling key information of the text data by utilizing a natural language processing method; and S3, extracting visual features of the medicine image through a convolutional neural network or a Transform visual model. According to the method, the drug information can be comprehensively represented by integrating the visual features of the drug image, the semantic features of the drug text data and the drug chemical features. The multi-modal feature fusion not only enhances the robustness of drug identification, but also significantly improves the accuracy of identification.

A companion interactive intelligent health care system is provided. The system includes a multifunctional intelligent medicine box, a companion interactive intelligent robot, and an intelligent display processing terminal. The multifunctional intelligent medicine box is used to display the name and dosage information of medicine, indicate the location of the medicine, and record the medication information. The companion interactive intelligent robot integrates voice, expression, and action, and is used to remind the user to take medicine on time and in dosage through multiple sensory interactions, provide emotional companionship, and remote calls with family members. The intelligent display processing terminal is used for system software operation, data acquisition, storage, and analysis, remote viewing, and monitoring of user medication records. The present invention also provides an intelligent medication management method based on the above-mentioned companion interactive intelligent health care system, which has a wide range of application scenarios.

[Problem] To compress the number of records and make consistent records with good visibility by arranging medicine, disease, and symptom / finding entries in a tree structure having a hierarchical structure and recording common attributes in an entry at an appropriate level.[Solving Means] A medicine prescription assistance system includes medicine information recording means related to medicine entries including medicine names and class names grouping the medicine names. The medicine information recording means each include (1) medicine entry attribute recording means that is recording attributes about the medicine names and the class names grouping the medicine names, (2) medicine entry parent-child relationship link means that defines parent-child relationships between the medicine entries, and (3) medicine information recording means including medicine entry attribute record parent-child inheritance means configured to inherit an attribute recorded in the medicine entry attribute recording means of a parent to the medicine entry attribute recording means of a child through the medicine entry parent-child relationship link means.

The invention relates to the field of medication information recording, in particular to a clinical medication data recording method and system. The method comprises the following steps: firstly, acquiring clinical medication information of each patient in different dimensions and disease time of the patient, quantifying the clinical medication information, acquiring a medication information coding value of each patient in each dimension, acquiring disease similarity of each patient according to the difference of the medication information coding values of each patient in the same dimension, and determining the disease similarity of each patient according to the disease similarity. Clustering the patients based on the difference of the disease similarity of the patients, and obtaining patient record priorities according to the difference of the disease similarity of the patients in a cluster, the overall level of the disease similarity, the number of the patients and the difference of disease time; according to the distribution of the medicine use information coding values of the same dimension of all the patients, dimension record priorities are obtained; and in combination with the patient record priority and the dimension record priority, recording clinical medication information of the patient. The efficiency of recording clinical medication data of a large number of patients can be improved.

The invention relates to the technical field of cold-chain logistics. The invention discloses a medical cold chain transport capacity planning system and method, and the system comprises a plurality of refrigerated transport units, each refrigerated transport unit comprises a refrigerated container which can maintain a plurality of different preset temperature intervals, and a transport task management module which is used for receiving and processing the drug information and transport requests of a plurality of drugs, the system comprises a transport task management module used for managing drug information and carrying out priority grading on the drug information and transport requests, a transport capacity demand prediction module used for predicting future transport capacity demands according to historical data, and a path optimization module used for carrying out transport arrangement according to the information provided by the transport task management module in combination with various comprehensive data. According to the medical cold-chain transportation capacity planning system provided by the invention, efficient, safe and reliable medical cold-chain transportation is realized by integrating a plurality of modules such as task management, capacity demand prediction, path optimization, real-time monitoring, alarm and user interface, and the high requirement of the modern medical industry on cold-chain logistics is met.

The invention discloses a graph contrast learning drug recommendation method and system fusing clinical features, and relates to the technical field of intelligent medical treatment. The method comprises the following steps: acquiring a clinical medical record database of a patient, extracting diagnosis, operation and medicine information of each treatment, and constructing a historical medical record heterogeneous graph of the patient; in heterogeneous graph propagation, a structure guidance matrix (mask) is generated by using a conditional probability matrix to guide node connection and attention generation, and the expression stability is improved in combination with Hellinger distance constraint; in the aspect of drug modeling, from three perspectives of a drug co-occurrence network, an interaction network and a molecular structure diagram, representation consistency and interpretability are improved through diagram contrast learning. On the basis, four types of loss function optimization recommendation results are designed, and a medicine combination with the lowest comprehensive medicine use risk is screened out for clinical auxiliary diagnosis and treatment. According to the method, multi-view medical knowledge is fused, efficient drug combination screening and recommendation are realized, meanwhile, the drug interaction risk is reduced, and the recommendation accuracy and safety are improved.

A system and method for tracking and administering liquid intravenous medications in a hospital, clinic, or patient residence, including recording information onto a short range communication device affixed to a liquid container in a hospital pharmaceutical filling or compounding center, the information including drug information, patient information, and IV administration information, transferring the liquid container to a patient's bedside, programming an infusion course into a bedside IV infusion pump by enabling the pump to read the encoded information on the communication device, and disposing of the liquid container and any residual drug in a smart disposal bin that reads the communication device and weighs the container. The disposal bin may confirm the chemical contents of the container.

Disclosed in the present invention are a drug-based cancer inhibition sensitivity prediction method and device based on a graph neural network. The method comprises: acquiring a candidate drug and cancer cell line original data; extracting drug information features of the candidate drug, and expanding the drug information features; constructing a drug molecule feature vector according to the drug information features, and constructing a drug adjacency matrix, a node feature matrix, and an edge feature matrix by taking atoms of a drug as nodes and chemical bonds as edges; constructing and iterating a graph neural network model to obtain a drug graph feature; obtaining a drug characterization after the drug graph feature and the drug molecule feature vector are aggregated; acquiring and storing the expression quantities of gene expression, gene mutation, gene methylation and gene copy number, in a cancer cell line, of a gene sequence, and extracting a cancer cell line characterization; and performing feature fusion on the drug characterization and the cancer cell line characterization to obtain a drug-cancer cell line instance pair, inputting the instance pair into a drug-based cancer inhibition sensitivity prediction model, and predicting to obtain an IC50 value of the candidate drug in a cancer cell line environment.

The invention discloses an intelligent oral medicine box system for hospitalized patients, and relates to the technical field of intelligent oral medicine boxes, and the system comprises an equipment distribution module which is used for distributing intelligent medicine boxes for patients; the medicine distribution and reminding module is used for automatically distributing medicine according to the medicine information of the patient and reminding the patient to take medicine through sound and visual signals; the medicine taking education module is used for providing medicine information for the patient by using the intelligent medicine box; the real-time monitoring and adjusting module is used for the intelligent medicine box to collect health data of a patient through health monitoring equipment, a multiple regression model is constructed to analyze the health data, and a support vector machine (SVM) model is constructed based on the analyzed data to adjust medicine use; and the storage module is used for storing the used data. According to the method, health data are analyzed and predicted through the multiple regression model and the SVM model of the intelligent medicine box system, and personalized medication guidance and dynamic adjustment strategies are provided.

A device may include a data acquisition layer comprising a patient information collector and a drug information collector, each operable to ingest patient-specific clinical data and drug-specific data, respectively. A device may include an analysis engine comprising a multi-criteria ranking engine configured to compute composite suitability score for candidate medications by weighting one or more of medication efficacy, medication safety, medication resistance, medication cost, or patient-specific coverage factors. A device may include a presentation layer comprising a summary dashboard configured to display a ranked medication recommendation set produced by the analysis engine.

The invention discloses a drug use decision method and system based on deep learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining the basic information of a target symptom, and inputting a large language model to obtain the symptom and the description information of a to-be-matched drug; and then determining a target drug matched with the target symptom through a preset first expert model. And then integrating the target drug related information and converting the information into a standard format to obtain standardized drug recommendation information. And finally, the information is pushed to a preset medication decision interface through a preset interface, so that intelligent, efficient and accurate medication decision assistance is realized. The main purpose of the invention is to provide an intelligent medication decision-making auxiliary method, which matches symptoms and drug information through a large language model, accurately recommends a target drug in combination with an expert model, standardizes recommendation information and pushes the standardized recommendation information to a medication decision-making interface so as to improve medical decision-making efficiency and accuracy.

The invention relates to the technical field of medical care data processing, and particularly discloses a medical care data processing system based on reinforcement learning, which comprises a data acquisition module used for acquiring multi-source heterogeneous medical care data, the multi-source heterogeneous medical care data comprises basic information, vital sign data, diagnosis records, care records and medication information of a patient; the data preprocessing module is connected with the data acquisition module and is used for cleaning, denoising and standardizing the acquired medical care data and converting the processed data into a uniform format; and the reinforcement learning module is connected with the data preprocessing module, constructs a reinforcement learning model, and takes the preprocessed medical care data as input. According to the medical care data processing system based on reinforcement learning, the data processing accuracy and efficiency are remarkably improved, the disease risk prediction error is reduced by more than 25%, and the model updating time is shortened by 40%.

The invention provides a drug recommendation method and system based on multi-task learning and retrieval enhancement, and relates to the technical field of intelligent medicines.The method comprises the steps that an electronic medical record data set and external knowledge data are preprocessed, diagnosis, operation and drug data in files are vectorized, and multi-task formalized input is constructed; constructing a drug recommendation model based on multi-task learning and retrieval enhancement, wherein the drug recommendation model comprises a multi-task learning module, a retrieval enhancement drug prediction module and a multi-task aggregation module; and obtaining a drug recommendation result by using the drug recommendation model. According to the method, a multi-task representation learning module is introduced into a deep convolutional neural network model, and patient representation is enriched by introducing clinical classification software code knowledge, so that the influence of data distribution imbalance on model performance is relieved. Meanwhile, a retrieval enhancement prediction module is designed to model historical drug information of a patient, and the prediction performance of the model is improved by mining valuable information in historical drugs.

A method of supporting user adherence to a medication regimen includes storing received medication information associated with one or more medications using a patient wearable device and a coordinated care platform. The method also includes displaying identification and dosing information associated with the medications, generating a notification indicating that the identification information is being displayed, monitoring a response input indicating that the medications have been administered. For each of the one or more medications displayed, the method includes generating a medication status describing that either (i) the medication has been administered or (ii) the response has not been received. The method further includes generating a reminder notification when the medication status indicates that the response has not been received. The method may further include gathering behavioral data points associated with the user for submission to the coordinated care platform, which performs predictive analytics based on the data points.

The utility model discloses a medicine placing trolley capable of forcibly checking information, which comprises a trolley body and a plurality of drawers which are arranged on the front side of the trolley body and are used for partitioned storage, latticed partition plates are fixedly connected in the drawers and divide the inner areas of the drawers into a plurality of placing grooves, and the latticed partition plates are arranged in the placing grooves. A baffle is hinged to the top of one side of the inner wall of the containing groove, and a label is pasted to the top of the baffle. The baffle is horizontally arranged to shield the top of the containing groove, and the baffle rotates upwards to be lifted up under the action of external force to cancel shielding of the top of the containing groove. When medicines are placed or dispensed, the baffles need to be lifted firstly, patient information labeled on the baffles can be checked unconsciously when the baffles are lifted, nurses can be forced to check the dispensed medicine information, forgetting is avoided, errors are reduced, meanwhile, only one baffle can be lifted each time, and therefore the medicine dispensing efficiency is improved. And mistakes caused by simultaneous placement or taking of a plurality of medicines can be avoided.

The invention provides a medicine information management method and system based on an intelligent medicine cabinet, and belongs to the technical field of information management, and the method comprises the steps: carrying out the fusion processing of multi-modal data collected by a multi-modal sensor group in the intelligent medicine cabinet, and obtaining a medicine recognition result; based on the matching degree of the medicine identification result and a preset medicine storage rule in the medicine cabinet, the storage positions of all medicines in the intelligent medicine cabinet are adjusted to obtain a first adjustment result, and the first adjustment result is stored; and managing the storage information of the medicine based on the first adjustment result and the historical adjustment result. According to the medicine information management method and system based on the intelligent medicine cabinet, the storage position of the medicine can be adjusted, redundant space is released, and energy is saved.

The invention relates to the technical field of medical evaluation, in particular to a basic drug use tendency evaluation method and device, a medium and a product. The method comprises the following steps: acquiring a disease type, a patient specific attribute and medication information of a target patient; according to a standard evidence-based intensity database, determining intermediate evidence-based intensity of each drug type in the medication information in disease types, patient specific attributes and medication schemes; obtaining attribute information of a target hospital seeing a doctor by the target patient, and adjusting the intermediate evidence-based intensity based on the attribute information to obtain the actual evidence-based intensity of the target patient; determining the average standard evidence-based intensity of each base drug type corresponding to the disease type; and calculating a difference value between the actual evidence-based intensity and the average standard evidence-based intensity as a base drug use tendency degree, and generating a drug use rationality evaluation result for the target patient according to the base drug use tendency degree. According to the invention, multi-dimensional evaluation can be carried out on the drug use rationality of the patient, and the accuracy of the evaluation result is improved.

The invention relates to an osteoporosis auxiliary rehabilitation treatment decision-making system based on man-machine interaction, and belongs to the technical field of osteoporosis treatment, and the method comprises the following steps: a patient information obtaining module is used for obtaining the data information of a current patient in response to the data information input operation of the current patient by a user; the disease determination module is used for determining a disease type based on the first disease information; the prescription recommendation module is used for responding to a prescription generation instruction of a user, generating a first to-be-used prescription based on the disease type, the basic information and the medication information, and displaying the first to-be-used prescription as a recommended prescription; the effect evaluation module is used for responding to a second illness state information input operation of the user and generating treatment effect information based on the first illness state information and the second illness state information; and when the treatment effect information does not reach the expectation, generating a second to-be-used prescription, taking the second to-be-used prescription as the first to-be-used prescription, and displaying the second to-be-used prescription. The application has the effect of improving the rehabilitation effect in osteoporosis treatment.

The invention provides a desktop type accompanying interaction intelligent robot. The robot has the following functions of accompanying interaction, daily life information inquiry and broadcast, user health information inquiry and broadcast, medication information reminding inquiry and broadcast and remote interaction and conversation with family members. Wherein the accompanying interaction comprises scenes, voices, expressions and actions, and an interaction effect is generated by superimposing the scenes, the voices, the expressions and the actions; the daily life information query broadcasting is that the cloud platform is accessed through WIFI, and daily life information is broadcasted based on a user query demand; the user health information query broadcasting is that a robot exclusive cloud platform is accessed through WIFI, an application program APP is associated, and daily health condition information is broadcasted based on a user query demand; the medicine use information query broadcasting is that a robot exclusive cloud platform is accessed through WIFI, an application program APP is associated, and a user is reminded of medicine use through regular broadcasting; medication information is broadcasted based on user demands.