314 results about "Medical information" patented technology

This invention discloses a method, apparatus, device, and storage medium for medication risk assessment during pregnancy, relating to the interdisciplinary fields of medical information technology and clinical pharmacy. The method includes: receiving patient information and proposed medication regimen information, whereby the patient information includes gestational age and information on multiple comorbidities; calling the corresponding gestational age adaptive risk weight matrix based on gestational age, weighting the patient features to generate a weighted feature vector, and inputting this vector into a machine learning model to obtain an initial medication risk score; based on a constructed heterogeneous knowledge graph, using entities corresponding to multiple comorbidities and medication regimen information as query starting points, performing multi-hop reasoning to determine the existence and severity of conflicting paths; if a conflicting path is detected, revising the initial medication risk score according to the severity of the conflict to generate a final risk score. This application enables individualized, interpretable, and evidence-based intelligent risk assessment of proposed medication regimens for pregnant women with multiple comorbidities.

This invention relates to the field of medical information management technology, including an AI-based endometriosis management system. The system comprises a thickness dynamic monitoring module, an inflammation level assessment module, a lesion spread analysis module, a periodic lesion tracking module, and a disease progression analysis module. In this invention, AI is used to segment ultrasound image data, enabling dynamic monitoring of lesion areas. Lesion boundaries are extracted, and changes in local inflammatory marker concentrations are calculated, making the screening of abnormal inflammatory areas more accurate and enabling early identification of lesion development. The tracking of periodic lesions, combined with analysis of lesion contour changes and area increases / decreases, enhances the quantitative control of disease progression. The future development trend of lesions, combined with the calculation of periodic inflammatory pathways, makes the prediction of disease evolution more consistent with physiological changes. Multi-dimensional lesion analysis combined with AI intelligent calculation expands the diagnosis and treatment of endometriosis from static assessment to dynamic trend prediction, improving the adaptability of personalized treatment plans.

Methods and systems described herein may comprise a rigid bioelectric patch comprising one or more electrodes disposed on the outside of the rigid bioelectric patch and configured to allow conduction of an electroencephalogram (EEG) signal; and a signal processing unit configured to receive the EEG signal and determine user's medical information. Further comprising a hydrogel configured to be fixed to a user's forehead and conduct the EEG signal to the one or more electrodes, wherein the hydrogel is an adhesive of.01 N / cm − 1,000 N / cm for peel strength and a conductive material of.0001-9:1020 S / m.

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.

1. Name of the product in this design: Self-service machine (general medical care). 2. Purpose of this design: To provide users with self-service such as medical information inquiry. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The application relates to the technical field of medical information, and discloses a postoperative pain rehabilitation nursing data management system for a rehabilitation medical department, which comprises nine processing modules. A multidimensional data dynamic acquisition module is responsible for collecting postoperative pain related data of patients. A cross-platform data interaction module realizes data intercommunication and synchronization among multiple platforms in the system. A data encryption module performs encryption processing on collected sensitive data. A data analysis module calculates a pain comprehensive score and a rehabilitation progress index. A pain risk real-time early warning module performs real-time monitoring on the pain condition of a patient. An intelligent intervention scheme generation module generates a personalized pain intervention scheme. A pain rehabilitation evaluation module performs systematic evaluation on the pain rehabilitation process of a patient. A nursing feedback module collects feedback information of a patient on pain nursing service. A system emergency disaster recovery module provides a system-level disaster recovery backup and emergency response mechanism.

The present application relates to the technical field of medical information processing and intelligent disease prediction, in particular to a neurofibromatosis prediction method based on clinical medical information. The method comprises the following steps: acquiring multi-modal clinical medical information with time labels; performing anatomical system classification processing, extracting clinical feature nodes and calculating correlation, and establishing an initial multi-system prediction network graph; determining the asynchronous graph node feature aggregation rate according to the feature update rate difference of adjacent nodes under different time labels, performing local feature diffusion processing, and determining a global multi-system prediction network graph; further determining the cross-domain evolution incubation period weight, the phenotype cascade transfer probability matrix and the disease topology state information entropy, and determining the neurofibromatosis prediction result accordingly. The present application solves the contradiction of long-term and ineffective consumption of a large amount of computing resources to cope with low-frequency but high-impact clinical events, and optimizes the trade-off relationship between risk identification accuracy and system response timeliness.

The present application relates to the technical field of medical information management, in particular to a drug issuing whole-process dynamic tracking and visualized board system, comprising an information generation module, a process state acquisition terminal, a central processing server and a visualized board. The process state acquisition terminal deployed at each node realizes automatic and non-susceptible acquisition of the drug circulation state, reduces manual intervention, and the data is accurate and timely. Through a large visualized board, abstract process data is converted into an intuitive graphical interface, so that managers and staff can grasp the overall situation and quickly find bottlenecks. In addition, all circulation links are automatically recorded and stored in a database, so that the whole-process accurate traceability of any drug batch can be realized, providing a reliable basis for quality management and error analysis. The system perfectly realizes dynamic tracking and visualized management of the whole process of drug issuing, and has significant technical progress and practical value.

This invention relates to the field of medical information technology, and in particular to a method and system for generating clinical multi-agent task sequences. The method includes the following steps: acquiring and structuring core clinical objectives, which include patient basic information, clinical indicators, and disease management goals; decomposing the core clinical objectives into several structured sub-objectives with dependencies and execution order based on a clinical pathway template library and a medical knowledge graph; for each structured sub-objective, splitting it into multiple minimum executable clinical sub-tasks according to a clinical sub-task decomposition rule library, and defining the execution subject type, input / output, and dependencies for each clinical sub-task to construct a sub-task-level directed acyclic graph; integrating the sub-task-level directed acyclic graphs, identifying dependencies across structured sub-objectives, generating a global clinical task directed acyclic graph, and performing dependency verification and clinical logic consistency verification.

The application discloses a kind of narcotic drugs whole-process traceability management method and system, specifically related to medical information technology field, the method includes: obtaining the multimodal perception data generated in the circulation process of narcotic drugs, at least including operator identity data, drug object identity data, drug identification data, weight change data and operation image data;Multimodal perception data is input into the dynamic risk score model of edge computing node, the model extracts the behavior fingerprint characteristics of operator based on time series attention mechanism and outputs dynamic risk score;Determine whether dynamic risk score exceeds preset threshold, if exceeds, generate intercept instruction, otherwise generate release instruction and append storage traceability record.The application realizes the intelligent closed-loop traceability management of narcotic drugs whole process by multimodal fusion verification and adaptive risk assessment, effectively improves the real-time nature and data integrity of supervision.

This application discloses a method and system for intelligent generation and quality control of medical documents, relating to the field of medical information technology. The disclosed method and system for intelligent generation and quality control of medical documents acquires multi-source medical data, constructs a medical semantic graph network, performs clinical logical reasoning, generates and verifies medical documents, and updates them based on feedback. It can automatically construct a patient-specific medical knowledge network, identify deep semantic relationships between clinical entities, generate logically rigorous professional descriptions, and improve the quality of medical documents and medical safety.

This invention provides an information processing device, information processing method, information processing system, and program that enable centralized management of patient information by linking an electronic medical record database with databases from multiple customer acquisition media, and that utilize this information to propose medical services. [Solution] The information processing system includes a reception unit that receives patient needs, a patient information acquisition unit that acquires patient information including basic patient information and medical visit history information, a medical information acquisition unit that acquires medical information including medical treatment information and prescription details from medical institutions, an information centralized management unit that centrally manages patient information and medical information, a prompt generation unit that outputs generation commands based on the centrally managed patient information and medical information, and a proposal information generation unit that generates proposal information including medical services, medical institutions, treatment details, etc., suitable for the patient based on the generation commands.

To allow a user to further understand output results of a machine learning model.SOLUTION: A medical information processing device is configured to: extract, when assisting in interpreting output results of a model that outputs medical treatment assistance information as information for assisting in medical treatment of an examinee in response to the fact that a plurality of pieces of medical treatment information obtained when medically treating the examinee are inputted, first medical treatment information associated with medical treatment assistance information from among the plurality of pieces of medical treatment information, on the basis of correspondence in which the medical treatment assistance information is correlated to the plurality of pieces of medical treatment information and the degree of contribution of each of the plurality of pieces of medical treatment information to the medical treatment assistance information; extract second medical treatment information associated with the first medical treatment information from among the plurality of pieces of medical treatment information, on the basis of the mutual association degree of the medical treatment information and the contribution degree of each of the plurality of pieces of medical treatment information; and cause a display unit to display the medical treatment assistance information, the first medical treatment information and the second medical treatment information.SELECTED DRAWING: Figure 1

The application relates to the technical field of medical auxiliary decision and medical information processing, and discloses a necrotizing fasciitis operation opportunity auxiliary decision method and system based on lesion expansion trend modeling, wherein the method comprises the following steps: acquiring asynchronous multi-source observation data; performing a first-visit multi-modal observation reconstruction task; performing a deep expansion proxy quantity construction task; performing an expansion trend preprocessing task; performing an operation trigger judgment task; performing hierarchical trigger control; and outputting an operation auxiliary decision result. Compared with the method for judging the operation opportunity of necrotizing fasciitis by relying on single-time-point images in the prior art, especially under the condition that the surface swelling range of a patient is not significant in the emergency first-visit stage, the technical problem that the continuous depiction of the deep expansion trend of a lesion cannot be realized. Due to the establishment of an asynchronous multi-source observation unified reconstruction link, the establishment of time-weighted trend modeling and hierarchical trigger control, the auxiliary evaluation of the operation opportunity of a necrotizing fasciitis lesion is realized.

This invention relates to the field of medical information processing technology and discloses a method and system for predicting brain diseases in medical assessments of the elderly. By introducing methods such as discontinuous response boundary recognition, stage division, interruption semantic description, and interactive correlation calculation, the method distinguishes between long pauses caused by external interference and pathological delays in responses caused by abnormalities related to brain diseases within the same processing framework. This not only improves the accuracy and stability of brain disease risk prediction results in discontinuous response scenarios but also generates stage risk results and abnormal contribution results from interactive units. This provides a clearer basis for interpreting results in medical assessments of the elderly and addresses the problem that existing technologies cannot effectively distinguish between exogenous interruptions and pathological cognitive delays in discontinuous response scenarios in the elderly, leading to poor accuracy in brain disease prediction.

To provide an index capable of evaluating resistance in a capillary vessel.SOLUTION: According to an embodiment, a medical information processing device includes an acquisition part and a calculation part. The acquisition part acquires blood vessel blood flow in a coronary artery, and myocardial blood flow in a cardiac muscle region to which blood is supplied by the coronary artery. The calculation part combines the blood vessel blood flow and the myocardial blood flow to calculate an index showing a capillary vessel resistance amount in the capillary vessel that supplies blood to the cardiac muscle region.SELECTED DRAWING: Figure 1

This invention relates to the field of medical information processing technology, specifically to an ICU ward round device and system. The ward round device includes a trolley body, which comprises a tray and a base. The tray has a storage slot, and the base has a movable component at its bottom. A folding assembly for folding the trolley body is located between the tray and the base. A back panel is hinged to the top of the tray, and a tablet computer is fixedly connected to the side wall of the back panel. A first spring is fixedly connected between the tray and the base. This invention designs the ward round device to be foldable into a suitcase-like shape. It can be quickly unfolded into a trolley form during use and can be quickly folded and retracted to reduce its size when not in use or during inter-floor transport. This facilitates manual handling by medical staff or flexible placement in elevator corners, avoiding the problems of existing ward round devices being too large to fit in crowded elevators and obstructing the transport of personnel and goods.

This application discloses a method, device, equipment, and medium for monitoring the execution of nursing plans, relating to the field of medical information technology. It preprocesses multivariate nursing information of the current patient to obtain a current nursing plan; inputs the current nursing plan into a target BERT model to obtain structured task tuples including various nursing interventions; uses an incremental FUP algorithm to mine strong association rules between the types of nursing interventions in the structured task tuples and the nursing record types corresponding to the current nursing execution event, and constructs an association graph based on these strong association rules; matches the current nursing execution event with the nursing interventions in the structured task tuples based on the association graph to obtain association evidence information; evaluates the execution status of the current nursing plan based on the association evidence information, and generates a quality control report based on the evaluation results. This eliminates data fragmentation in nursing plan execution monitoring, thereby improving the accuracy of monitoring results.

The application discloses a neural system disease intraoperative monitoring system based on a large model, relates to the technical field of medical information processing, and comprises a synchronous acquisition module, a mapping construction module, a consistency evaluation module, a semantic normalization module and an attribution decision module. The system synchronously acquires double-channel vocal cord electromyography signals and stimulation events, intercepts a response segment and calculates a dominant side symbol; the consistency score of normal and reverse syntax is counted in a recording window, and channel side reversal caused by tracheal intubation rotation is actively identified; accordingly, semantic normalization processing of channel exchange or retention is performed, and the underlying anatomical mapping is corrected; finally, the large model reasons the normalized signal, accurately distinguishes real physiological damage from device measurement artifacts, and maps and outputs action instructions. The application eliminates semantic pollution caused by side error to the large model from the root, and greatly improves the accuracy of intraoperative intelligent monitoring.

This invention relates to the field of artificial intelligence technology, specifically to an AI-based medical information consultation method and apparatus. The method includes: acquiring multimodal medical consultation input from a user; preprocessing and extracting features from the multimodal input, and fusing them to generate a unified multimodal feature representation; based on the feature representation, performing topic indexing in a pre-constructed medical knowledge graph, and extracting a set of highly relevant medical knowledge using unsupervised clustering analysis and a dynamic threshold filtering mechanism; inputting the filtered medical knowledge set and multimodal features into a medical large-scale language model for logical reasoning, and running a multi-turn dialogue intent tracking algorithm during the reasoning process to complete missing entities; intercepting high-risk behaviors and correcting compliance issues in the preliminary response generated by the large-scale language model, and outputting a safe and professional medical information consultation response. This invention significantly improves the cross-modal perception capability, logical reasoning accuracy, and clinical application legitimacy of medical AI.

The application relates to the technical field of medical informatics and intelligent treatment control, and discloses an electroencephalogram synchronous electroacupuncture stimulation calibration method and system based on embedded AI. The embedded AI module is used for collecting and intelligently analyzing brain electrical signals of a patient in real time, generating brain state characteristics, dynamically adjusting electroacupuncture stimulation parameters in combination with subjective feedback of the patient, and forming a closed-loop feedback system. The system carries out individualized baseline calibration before treatment, improves the accuracy of brain state recognition, continuously monitors the brain electrical signals during treatment, dynamically updates the brain state characteristics, adjusts the electroacupuncture parameters according to the brain state characteristics, and ensures the individualization and dynamic optimization of the treatment. The system also has long-term learning ability, continuously accumulates treatment experience, optimizes the association rules, and improves the accuracy and efficiency of the treatment. The application significantly improves the pertinence, response speed and stimulation calibration accuracy of electroacupuncture treatment, and provides a new solution for individualized medical treatment.

The application relates to the technical field of medical information processing, and discloses a method and system for dynamically evaluating post-renal biopsy occult bleeding risk based on multi-source data fusion, wherein the method comprises the following steps: constructing a standardized uniform observation vector sequence; constructing a short-time instant risk model; constructing a mechanism to establish a long-time lag risk transfer model; generating a hierarchical treatment decision and a review scheduling plan; gain correcting the risk probability; and outputting a final occult bleeding risk grade. Compared with the scheme for postoperative bleeding risk in the prior art, which only performs static risk evaluation based on a single time section index, especially in the case that postoperative vital signs are temporarily stable but there is delayed hemoglobin decline, the technical problem that the continuous prediction of the risk transfer process of'short-time stability-long-time lag deterioration' cannot be realized is solved. Since the time-varying state transfer probability matrix is introduced in the application, and the risk transfer acceleration is determined, the sensitivity of postoperative risk identification is improved.

The application discloses a method and a generating device for generating an electronic medical record based on a doctor-patient conversation, relates to the technical field of medical information, and is used for improving the accuracy of speech recognition when generating an electronic medical record based on a doctor-patient conversation.In the method, the generating device automatically distinguishes subjective self-reports of patients and empirical symptoms supported by objective evidence based on the doctor-patient conversation, and fills them into different columns of the medical record respectively, and meanwhile, the empirical symptoms are sorted according to the strength of the objective evidence, so that the accuracy of speech recognition is improved when generating an electronic medical record based on a doctor-patient conversation.

We provide the system. [Solution] Means for acquiring user care data, medical information, and preference information, A means of analyzing acquired data to generate the optimal care plan, A means for transmitting the generated care plan to the caregiver's information device, A method for analyzing work-related memos and instructions entered by caregivers and updating the database, A method for caregivers to perform their duties while checking care plans in real time and receiving visual instructions using smart glasses, A system that includes this.

This invention discloses a smart medical information push method and device, belonging to the field of smart medical technology. The specific steps of the push method are as follows: Ⅰ: Collect and preprocess medical resource data and various types of data from each user, and then extract the features corresponding to the processed data; Ⅱ: Based on the processed medical resource data, generate a dynamic medical resource heat map, and delineate the optimal time and space for push based on the various types of data from each user; This invention can achieve precise matching between medical resources and user needs, improve the overall efficiency of medical service connection, ensure that the timing and space of push are in line with user needs and the actual situation of medical resources, and provide adaptive content for different users; At the same time, this invention can achieve early intervention for physiological abnormalities, assist users in health management, ensure the targeting of push, and flexibly respond to changes in the busyness of various departments, thus improving the practicality of push.

The application discloses a RAU risk generation and prediction method and system based on a causal guided diffusion model, belongs to the cross technical field of medical information technology and artificial intelligence, and does not directly predict a risk label, but learns and simulates complete evolution distribution from a current health state to multiple possible health states in the future. Through Monte Carlo sampling, the application can generate a large number of 'future selves' conforming to individual characteristics, and statistically obtain risk probabilities from the 'future selves'. The method can better capture the uncertainty of disease development, provide a probability distribution instead of a single point estimate, and the prediction result is more in line with the randomness nature of a biological process.

This invention relates to the field of medical information technology and scheduling, and discloses a method for scheduling appointments based on medical ultrasound department data. The method addresses the hidden conflict of existing systems neglecting the reprocessing of intracavitary probes, leading to idle examination rooms but no available probes, and the time drift caused by continuous high-load scanning by operators. This solution collects patient requests and extracts probe status, estimating the expected time to offset individual differences; reads historical probe reprocessing events to obtain the processing time including the cleaning to storage and release cycle; combines the above time parameters to extrapolate the actual available time for each candidate appointment; further, it calculates the comprehensive cost of the candidate appointments based on historical delay results, selects the scheme with the minimum cost for dynamic scheduling, and corrects the historical average based on measured data in a closed-loop manner. This invention effectively alleviates queuing deadlock and improves the accuracy of resource scheduling in ultrasound departments.

The present application relates to the field of medical informatics, and provides a paliperidone efficacy prediction method and system based on plasma metabolomics. The method comprises the following steps: collecting basic data of a target patient; performing feature selection on metabolites through the basic data to obtain relevant metabolites corresponding to paliperidone efficacy; training a neural network based on the relevant metabolites and baseline data in the basic data to obtain a prediction model; and performing paliperidone efficacy prediction on a patient to be predicted through the prediction model to obtain a prediction result. The present application can predict the probability of patient response to paliperidone, thereby more accurately evaluating the efficacy of paliperidone before medication, and providing an auxiliary decision for individualized medication.

A method for remote optimization of a medical technology is described. The method comprises acquiring data associated with one or more attributes of operation of the medical technology. Further, medical information associated with the attributes of operation of the respective medical technology is obtained. The method further comprises analyzing the obtained medical information and the acquired data to optimize the operation of the medical technology. Further, method comprises determining data associated with one or more predefined parameters related to the optimized operation of the respective medical technology. The method further comprises obtaining the data associated with predefined parameters related to the optimized operation of the respective medical technology. Further the obtained data operation of the respective medical technology is analyzed and an optimized data associated with the one or more predefined parameters for optimal operation of the medical technology based on the analyzed data is determined.

The application belongs to the technical field of medical information, and discloses a tumor chemotherapy scheme individualized adjustment and curative effect evaluation method and system. Static basic data, dynamic treatment data, single cell sequencing data and structured data of doctor-patient conversation are comprehensively collected in the data fusion quality control stage. Format adaptation and semantic alignment of cross-modal data are realized through a unified standardized interface. Data quality control is carried out in combination with an isolated forest algorithm, and only high-quality data with a score of 80 or more are included to provide a reliable basis for scheme development. In the multi-module collaborative prediction stage, a medical knowledge graph and a hybrid modeling framework are fused, multi-center data is jointly trained, and drug sensitivity scores are accurately output. When the scheme is generated, the clinical guidelines and patient needs are linked, multi-constraint rule reasoning and multi-objective optimization are used to develop individualized schemes that adapt to the patient's genetic characteristics, pathological types and quality of life demands, and reduce the risk of poor efficacy and serious adverse reactions caused by individual differences.