247 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 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 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 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.

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 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.

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 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 belongs to the technical field of medicine classification, and discloses a medicine classification method, device and equipment based on clinical test forbidden medicine. The method comprises the following steps: determining a corresponding disease type and an action mechanism according to input drug information of a drug to be queried; the method comprises the following steps: mapping drug information obtained by processing historical drug information to a high-dimensional feature space through a Gaussian kernel function, obtaining a drug information data set after spatial transformation through kernel principal component analysis, and constructing a drug classification model based on the drug information data set by using a Gaussian Bayesian algorithm. And finally, determining a drug classification corresponding to the to-be-queried drug through the drug classification model. And a corresponding alarm prompt is output when it is detected that medication is forbidden. By means of the mode, medicine classification is more targeted, the accuracy of medicine classification is improved, and therefore the method better fits a clinical test research scheme, the workload of research doctors can be effectively reduced, risks in the clinical test process can be reduced, and the clinical research quality can be improved.

The invention relates to the technical field of intelligent medical treatment, and discloses a control method of an intelligent medicine box and the intelligent medicine box, and the method comprises the following steps: the intelligent medicine box reads configuration data in a configurator, and carries out medication plan configuration on the intelligent medicine box based on the configuration data; when the medicine taking reminding time corresponding to the medicine taking plan arrives, displaying and prompting the medicine taking information through a display screen of the intelligent medicine box, performing sound prompting on the medicine taking information through sound output equipment of the intelligent medicine box, and performing light prompting through a medicine storage bin matched with the medicine taking information in the intelligent medicine box; medicine taking information corresponding to the intelligent medicine box is obtained, and a medicine taking record is determined based on the medicine taking information; and generating a medicine taking report according to the medicine taking plan and the medicine taking record.

The invention, which relates to the technical field of the medical care information equipment, discloses an infusion inspection Bluetooth prompter comprising a Bluetooth prompter module, a PDA terminal module, an information interaction module and an intelligent prompting module. The Bluetooth prompter module is made of a medical-grade material, is provided with a shell with an adjustable buckle, is internally provided with a power supply component which is used for Bluetooth communication, unique identification, state indication and wireless charging, and is used for being mounted on an infusion tube and communicating with an external device PDA (Personal Digital Assistant). Personal information of a patient, information of a medicine being infused and unique identification information of the corresponding Bluetooth prompter module are accurately associated and bound through the information interaction module, a one-to-one corresponding relation is formed and is properly stored, and the situation that in the complex environment that multiple persons are infused at the same time, the infusion efficiency is greatly improved is effectively avoided. Nursing errors caused by untimely inspection of nursing infusion and infusion pumps, leakage of infusion inspection and simultaneous operation of various kinds of infusion or infusion pumps due to information confusion are avoided.

A deep learning method, system, device, and medium for drug recommendation are disclosed. The method includes: establishing drug representations; establishing patient representations; and generating drug prediction results. These three steps first utilize an RNN to establish patient representations, then integrate patient diagnostic information, treatment information, and historical medication information, employing dual attention to assign weights at different element levels in patient history and single visits, and finally using a neural network for drug recommendation. Furthermore, patient similarity can be incorporated for drug recommendation, effectively improving the accuracy and safety of drug recommendations. The system, device, and medium, through the storage and utilization of relevant functional modules, realize drug recommendation using deep learning methods. As a medical auxiliary tool, it can significantly improve the discrimination quality and work efficiency of pathologists.

This invention belongs to the field of medical information technology and discloses an intelligent detection system and method for drug compatibility risks. The method includes: establishing and maintaining a multi-dimensional drug compatibility knowledge base based on a graph database; automatically acquiring drug information to be executed and retrieving the target patient's hospital-wide medication history through a standardized medical information interaction interface; calculating the estimated blood drug concentration of each historical drug at the current moment based on a pharmacokinetic compartment model to construct an effective drug list; performing combination analysis on the drugs to be executed and the effective drug list, and executing a multi-level compatibility risk detection algorithm, wherein the detection algorithm includes multi-drug combination cascade reaction risk prediction based on a graph neural network model; and generating and outputting graded early warning information containing risk levels and specific descriptions based on the detection results. This invention achieves dynamic, accurate, and real-time detection of complex multi-drug combination risks, significantly improving medication safety.

To highly accurately predict effects of a drug administered to a subject.SOLUTION: A prediction system includes an acquisition unit and a prediction unit. The acquisition unit acquires subject information including a medical image showing bones of a subject. The prediction unit outputs drug prediction information regarding effects of drugs acting on at least one of bone formation and bone absorption, using a drug effect prediction model for predicting effects of drugs on the basis of input information including the subject information and drug information on drugs.SELECTED DRAWING: Figure 3

This invention discloses an intelligent batch dispensing device for mother liquor powder, comprising: a dispensing frame assembly including a first accommodating part and a second accommodating part; a frame rotation assembly including a rotation drive assembly and a rotation mounting base; a first feeding mechanism including a powder bottle feeding assembly and a powder bottle translation assembly; and a second feeding mechanism including a mother liquor bottle feeding assembly and a mother liquor bottle translation assembly. According to this invention, the intelligent batch dispensing device for mother liquor powder saves dispensing space due to the vertical arrangement of the first and second accommodating parts; a drug detection device is provided to read and identify the fixture and drug information; and the frame rotation assembly drives the rotation of the dispensing frame assembly, enabling the powder bottle translation assembly or the mother liquor bottle translation assembly to dispense the drug quickly and accurately, thereby improving dispensing efficiency.

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 invention provides a risk determination method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. In some embodiments of the invention, health state information, medication information, time biological characteristics and drug molecular map structural characteristics are input into a health risk prediction model to obtain a health risk prediction result; under the condition that the health risk prediction result is that the risk exists, determining medication adjustment information according to medication information and a medication dosage rule; sending the health risk prediction result and the medication adjustment information to an early warning terminal, so that the early warning terminal displays the risk prediction result and the medication adjustment information; according to the invention, based on the health state information, the medication information, the time biological characteristics and the drug molecular map structural characteristics of the user, the health risk prediction model is utilized to accurately predict the health risk, the health risk prediction efficiency is improved, and the health risk prediction accuracy is improved.

The present application relates to the technical field of intelligent health management, and particularly relates to a multi-modal intelligent health management method and system based on continuous memory, comprising: multi-modal preprocessing of voice, text, images and sensor data input by a user to obtain multi-modal health data; adopting a hierarchical continuous memory storage strategy to obtain a long-term memory item set through three-layer memory architecture step-by-step compression processing; constructing a health memory graph network based on a memory consolidation and association integration strategy; adopting a global health portrait incremental update and memory portrait driven parameter mapping strategy to map a health portrait into a role parameter vector; adopting a memory retrieval and knowledge graph enhanced reasoning strategy to combine historical memory recall, knowledge graph reasoning and drug information management results to generate and output a health management response. The present application realizes the organic unification of continuous memory accumulation, personalized accompaniment and intelligent health reasoning for a health management scene.