135 results about "Patient Base" patented technology

This disclosure relates to a technique for identifying tumor-specific mutations through integrated analysis of next-generation sequencing data using machine learning models. In certain embodiments, a computer implementation method is provided, comprising: generating sequence reads from one or more samples collected from the same patient; generating variant calling files by analyzing the sequence reads corresponding to each of the one or more samples; generating a list of candidate somatic variants by comparing the variant calling files; generating a score for each of the candidate somatic variants in the list of candidate somatic variants using a classification machine learning model, wherein the score is generated based on a plurality of classifications generated by the classification machine learning model; determining the ctDNA status for the patient based on the score, wherein the ctDNA status is either positive or negative; and generating a report providing the ctDNA status for the patient.

In some examples, a system is configured to use a neural network algorithm of a cerebral autoregulation model to determine a cerebral autoregulation status of a patient based at least in part on blood pressure of the patient over a period of time and regional cerebral oxygen saturation of the patient over the period of time.

A method for patient identification comprises the steps of: determining (S10) initial identification data during an initial measurement of a patient (P) using a first measuring device (1) for determining ophthalmic and / or optometric measurement data; and determining (S30) a patient ID of the patient based on the determined initial identification data. The invention also relates to a system or computer program (product).

This invention discloses an infection early warning model and prediction system for critically ill patients based on multimodal monitoring data, belonging to the field of medical artificial intelligence and clinical decision support technology. The system collects multi-source heterogeneous physiological signals, laboratory test indicators, imaging features, and nursing records from patients. Through a self-designed cross-modal feature fusion algorithm and dynamic weight adjustment mechanism, it constructs a nonlinear infection risk prediction model. Furthermore, time-series segmented coding and adaptive threshold correction methods are introduced during model training to achieve early and accurate infection early warning. Compared with existing technologies, this invention significantly improves the sensitivity and specificity of prediction and maintains stable performance across different departments and diseases.

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.

This invention belongs to the field of medical wristband technology, specifically disclosing a translation method for bidirectional communication between medical staff and patients based on multimodal interaction, including the following steps: Step S1, the multimodal interaction unit receives multimodal input signals; Step S2, the multimodal interaction unit fuses the multimodal input signals to generate comprehensive input information; Step S3, the comprehensive input information is identified and translated based on a medical-specific translation engine; Step S4, the context-aware module analyzes the dialogue content, identifies the scenario, and dynamically adjusts the translation results; Step S5, the adjusted translation results are synchronously output bidirectionally and broadcast via voice. This invention achieves three core breakthroughs in clinical cross-language communication through a translation mechanism combining multimodal signal fusion and context awareness: enabling real-time, accurate, and private barrier-free communication between doctors and patients, reducing the risk of medical errors due to language barriers, improving consultation efficiency and patient satisfaction, and reducing the risk of misdiagnosis.

The present application relates to magnetic resonance examination device technical field, specifically to a kind of cardiac magnetic resonance examination device, including fixed plate and electrocardiogram control patch, fixed plate is equipped with chest coil;Fixed plate is detachably connected with detection plate, detection plate is equipped with breathing training component for exerting chest pressure, breathing training component is electrically connected with control panel;Control panel records and draws fluctuation curve based on the pressure data detected by breathing training component, then obtains the breathing interval of patient based on fluctuation curve, sends breath-holding reminding instruction to voice module according to breathing interval;Detection plate side away from fixed plate is equipped with several electrocardiogram monitoring electrode pieces for detecting the quality of heart beat signal strength, detection plate is equipped with driving mechanism for driving electrocardiogram monitoring electrode piece to move in range;The present application is used to assist patient breathing breath control, realize the accurate matching of patient breathing breath opportunity and imaging sequence, reduce the generation of breathing artifact, guarantee the progress of cardiac magnetic resonance.

The present application relates to the technical field of cardiac defibrillation treatment, and particularly relates to a data processing method and system of an automatic external defibrillator. The method comprises the following steps: obtaining a patient help signal through an SOS safety clock and transmitting the signal to a stereoscopic rescue management terminal; performing regional network positioning on the patient help signal through the stereoscopic rescue management terminal to obtain patient help positioning data; performing electrocardiogram monitoring on the patient based on the patient help positioning data using the automatic external defibrillator to obtain initial electrocardiogram data of the patient's heart; performing data preprocessing on the initial electrocardiogram data of the patient's heart to obtain high-quality electrocardiogram data of the patient's heart; performing cardiac fibrillation feature analysis on the high-quality electrocardiogram data of the patient's heart to obtain cardiac fibrillation feature data of the patient; and performing arrhythmia calculation on the cardiac fibrillation feature data of the patient to obtain a cardiac fibrillation arrhythmia index of the patient. The present application can realize accurate defibrillation of the patient's arrhythmia.

This application discloses a bedridden patient exit warning and vital sign monitoring system based on millimeter-wave radar. The millimeter-wave radar front-end is responsible for emitting electromagnetic waves and collecting the original intermediate-frequency echo signal of the bedridden area; the signal processing module performs time-frequency transformation and clutter filtering on the original signal; the bedding feature identification module senses the bedding status of the clutter-filtered signal and outputs compensation parameters; the vital sign adaptive extraction module restores the signal based on the compensation parameters and extracts vital signs; the exit intention prediction module integrates point cloud data and vital signs to extract multi-dimensional features, predicts the exit intention through an artificial intelligence model, and triggers warnings in stages. Belonging to the field of millimeter-wave radar early warning and monitoring technology, this structure not only achieves high-precision vital sign monitoring in all seasons and under all working conditions, but also issues early warnings as early as when the patient sits up and leans forward.

A medical device system comprises a medical device comprising one or more sound sensors configured to generate a sound signal including noise experienced by a patient and processing circuitry. The processing circuitry is configured to determine, based on sound signal, one or more noise levels experienced by a patient, determine a heart disease risk for the patient based at least in part on the one or more noise levels, and generate an output corresponding to the heart disease risk to a computing device of the patient or another user.

This application relates to the field of medical and health monitoring technology, and discloses a system for assessing the body fluid status of hemodialysis patients based on multi-frequency bioelectrical impedance analysis. The system includes: a body fluid prediction module that uses a preprocessed impedance vector to solve for Cole model parameters through nonlinear fitting, combines real-time ultrafiltration rate data, and uses an unscented Kalman filter algorithm to dynamically optimize the Cole model parameters, obtaining optimized Cole model parameters. Based on these optimized parameters, time-series features are extracted and a fusion prediction model is used to obtain the predicted body fluid volume. A body fluid assessment and early warning module is used to determine the patient's current body fluid volume quantification index based on the optimized Cole model parameters and basic information, providing real-time early warning and short-term trend warning based on the predicted body fluid volume. This application helps improve the real-time performance and accuracy of body fluid status assessment.

This disclosure provides methods for identifying patients requiring treatment for pressure ulcers and for treating these patients using clinical interventions selected based on subepidermal water content. This disclosure also provides methods for grouping patients based on pressure ulcer risk and methods for reducing the incidence of pressure ulcers in nursing facilities.

This invention relates to a swarm intelligence-driven AI-based social companionship and matching method, comprising: collecting multimodal data of patients in social interactions; analyzing the data using a swarm intelligence algorithm that integrates multi-view manual annotations to extract key feature vectors; constructing and continuously updating a dynamic social function profile of the patient based on this profile; calculating the matching degree between the patient and other users or social activities using a neural network matching model based on this profile, and generating a personalized recommendation list; and finally performing the matching to facilitate the patient's participation in online social activities or the establishment of a one-on-one companionship relationship. This invention achieves accurate assessment and dynamic tracking of the social status of AD patients, and effectively improves the personalization and effectiveness of social intervention through intelligent matching, forming a continuously optimized closed-loop support system.

The present application belongs to the technical field of medical artificial intelligence auxiliary decision-making, and particularly relates to a method for predicting postoperative delirium risk of elderly abdominal surgery patients based on perioperative clinical characteristics. The present application extracts the demographic characteristics, preoperative laboratory indicators and intraoperative anesthesia-related characteristics of elderly abdominal surgery patients based on an electronic medical record system, and constructs a full-feature data set for postoperative delirium risk prediction. Further, a machine learning algorithm is used to establish a postoperative delirium risk prediction model, and the target model is screened in combination with the model stability evaluation result. On this basis, the model is optimized by using SHAP feature contribution analysis and recursive feature elimination strategy, and a simplified prediction model with fewer variables and better generalization performance is obtained. The prediction model can realize early identification of high-risk groups of postoperative delirium of elderly abdominal surgery patients during the perioperative period, has high discrimination ability, stability and clinical applicability, and can provide a reliable tool for precise prevention and auxiliary decision-making during the perioperative period.

The application discloses a diabetes patient biological age calculation method based on glycosylation markers and a multi-task deep learning framework, and belongs to the technical field of bioinformatics and medical data processing technology, and the method comprises the following steps: obtaining glycomics feature data and clinical examination data of diabetes patients; preprocessing the data; obtaining a feature set by adopting a multi-method screening and fusion strategy; constructing a deep learning model comprising a multi-modal feature fusion module and a multi-task learning module for training, wherein the multi-task learning module comprises a main task of aging clock regression and at least one auxiliary task; and outputting biological age prediction values and age acceleration evaluation results of individuals by using the trained model. The method realizes deep fusion of multi-modal biological data and exclusive modeling for the diabetes population, can accurately quantify the biological aging process and the deviation degree relative to the calendar age, and provides an objective quantitative tool for clinical evaluation.

The application relates to the technical field of orthopedic surgery navigation, and discloses a high-position tibial osteotomy navigation method, a system and a storage medium, and a communication method which comprises the following steps: generating a 3D bone model of a lower limb of a patient based on medical images and performing preoperative osteotomy planning; performing visual positioning system detection and spatial calibration of a surgical instrument; installing an optical marker array, collecting actual bone point cloud data, and realizing alignment of the model and the bone through a registration algorithm; in-surgery real-time calculation and display of deviation of an operation track and planning parameters; post-surgery reconstruction of a lower limb force line, and comparison and evaluation with preoperative planning and in-surgery navigation records. The system comprises a hardware execution unit and preoperative planning, calibration verification, in-surgery registration, real-time navigation and post-surgery evaluation modules. The application realizes full-process integration of preoperative accurate planning, in-surgery real-time closed-loop navigation and post-surgery quantitative evaluation, and significantly improves the precision, repeatability and predictability of clinical effects of surgery.

This invention belongs to the field of intelligent healthcare, specifically relating to a method, device, and medium for predicting the weaning risk of mechanically ventilated patients based on EIT (Extra-Intensive Lung Inclination) testing. The method includes: S101: acquiring pulmonary EIT monitoring data of mechanically ventilated patients over a period of time; S102: dividing the lungs into multiple regions and extracting EIT parameters based on the pulmonary EIT monitoring data, wherein the EIT parameters include a dynamic lung tilt index, which is the degree of variation in gas redistribution in each region during spontaneous breathing tests; S103: inputting the EIT parameters into a classifier for classification, and determining the patient's weaning outcome based on the classifier's output. The EIT parameters extracted in this application can effectively predict the weaning outcome of mechanically ventilated patients, helping clinicians choose a better weaning time.

The present disclosure provides a radiotherapy plan generation method, comprising: acquiring multi-modal data of a patient, the multi-modal data comprising medical image data and non-image data, the non-image data being text information with medical semantics; inputting the medical image data and the non-image data into a target agent, and determining a cognitive confidence of the target agent for a condition suffered by the patient; determining an execution action according to the cognitive confidence and the non-image data; in response to the execution action being a first execution action, performing segmentation on a tumor target region and / or an organ at risk of the patient based on the medical image data to obtain a segmentation result; and in response to the execution action being a second execution action, generating a radiotherapy plan based on the segmentation result.

A method, system, device, and medium for processing symptom cluster data of head and neck cancer patients based on sentinel symptoms are disclosed. The method includes: obtaining symptom data of head and neck cancer patients during radiotherapy and chemotherapy through longitudinal surveys; processing the symptom data using exploratory factor analysis to extract several symptom clusters; performing association rule analysis on the symptom cluster data based on the Apriori algorithm, and determining the sentinel symptoms within each symptom cluster by combining the time of first symptom appearance; and constructing an intervention plan for gastrointestinal symptoms using the sentinel symptoms as intervention targets. This invention achieves accurate determination of sentinel symptoms through longitudinal survey data, combined with the Apriori algorithm and the time of first symptom appearance, and constructs intervention plans based on the MRC complex intervention framework, realizing early symptom identification and providing precise and personalized symptom management support for head and neck cancer patients.

The vehicle first-aid medical method for guiding first-aid patient classification, treatment and hospital transfer according to the present application includes the following steps: a patient information registration unit receives necessary information about a patient in advance before getting on a vehicle or in the vehicle after getting on the vehicle; a patient symptom input unit receives the current symptoms of the patient in multiple ways; a first-aid treatment guideline providing unit provides a first-aid treatment method suitable for the patient based on the collected information; and a remote medical support unit supports real-time communication with a remote medical staff in the vehicle, so that the patient or a first-aid team member can obtain direct advice and guidance from a first-aid medical specialist.

This invention discloses an intelligent adaptive dose verification calculation method and system. The intelligent adaptive dose verification calculation method includes: obtaining preliminary dose distribution data for a patient based on a deep learning-based dose prediction model; dynamically assigning variance reduction parameters to particles in a Monte Carlo simulation based on the high and low dose distribution information in the preliminary dose distribution data, wherein the variance reduction parameters include simulation weights, with simulation weights assigned to particles predicted to be in high-dose regions being less than those assigned to particles predicted to be in low-dose regions; and using a Monte Carlo dose calculation engine, simulating the planned beam based on the dynamically assigned variance reduction parameters to obtain high-precision verification dose distribution data. This invention's intelligent adaptive dose verification calculation method ensures the physical authenticity of the final verification dose distribution and the credibility of the "gold standard."

The application discloses a kind of early warning screening systems of critical patient based on CI-TOFMS detection exhaled air VOCs, belong to early screening field.It includes sample processing system, CI-TOFMS, data processing module;The sample processing system is used to collect and pre-process exhaled air sample, the CI-TOFMS is used to detect exhaled air sample and obtain eVOCs spectrum, the data processing module is used to collect eVOCs spectrum data, and output early warning result;The data processing module includes data input module, eVOCs-based critical illness prediction model and result output module.The system of the application only needs to collect respiratory air to carry out the health status evaluation of test person, and it is very convenient.The eVOCs-based critical illness prediction model constructed further improves the accuracy and sensitivity of eVOCs detection technology in critical patient diagnosis, and provides more reliable diagnostic basis for clinicians.

The present application relates to the field of medical information technology, in particular to a comprehensive management system for intraoperative nursing of orthopedic patients based on artificial intelligence, comprising event analysis, rule extraction, state snapshot, strategy decision, sequence control and iterative update modules. The system analyzes the monitoring signals of heterogeneous terminals, dynamically calls key sign indicators and warning thresholds from the knowledge graph according to the operation stage, and generates a phased sign state snapshot. By comparing the sign data with the threshold, the reasoning engine is activated, the composite nursing instructions containing multiple collaborative measures are output, the virtual nursing task execution sequence is built and tracked in the system, and then the snapshot is iteratively updated according to the task completion state and new perception events, forming a closed-loop management. The scheme realizes the dynamic adaptation of intraoperative monitoring thresholds to the operation process, and integrates discrete nursing operations into a structured task process that can be tracked and evaluated.

This invention discloses a method for identifying mental illnesses combining electronic medical records and electroencephalogram (EEG) signals, comprising: acquiring electronic medical records and the corresponding patient's raw EEG signals; labeling patients based on disease type, and combining the labels, electronic medical records, and raw EEG signals into a dataset; constructing an initial model based on a large language model and a prior mental illness knowledge base, wherein the initial model includes an agent scheduling module, a data processing module, and a prediction module; and training the initial model using the dataset to obtain a multimodal predictive agent model for mental illnesses used to generate medical reports. The method provided by this invention enables multi-scale, multi-channel, and multi-feature exploratory analysis of EEG signals at any specified time slice, and jointly reasoning with medical record features and a mental illness knowledge base to generate structured diagnostic conclusions and interpretable reports.

This invention discloses a rehabilitation management method, device, and medium for patients with urinary incontinence, relating to the field of urinary incontinence rehabilitation technology. The method includes: classifying patients based on residual urine volume after urination; initially setting up the rehabilitation device based on the classification results; positioning the rehabilitation device; and using a pelvic floor mechanical tactile activation pad to perform a three-stage tactile awakening to activate the pelvic floor tactile senses. Based on this activation, a combined rehabilitation training program combining neuromuscular stimulation and the Crede maneuver is performed using a NMES therapy device and a lower abdominal pressure-limiting board, with the training results recorded. The combined rehabilitation training is then evaluated and adjusted based on these results. This invention transforms the Crede maneuver into a controlled, rhythmic, and positional pressure process using a pressure-limiting board, reducing human variation.

This application discloses an intelligent queuing method based on patient type, comprising the following steps: obtaining the attribute information of each patient in the current queue and calculating the static basic priority of each patient; calculating the dynamic hunger value of each patient based on the waiting time of each patient in the current queue; weighting the dynamic hunger value and the static basic priority of each patient to obtain the real-time priority of each patient; when a new patient needs to be enqueued, calculating the real-time priority of the new patient and determining the insertion position of the new patient in the current queue through an incremental local rearrangement algorithm based on the influence range. The beneficial effects of this application are: through the hunger value function, any patient with an excessively long waiting time can receive priority compensation, ensuring from the algorithm's principle that low-priority patients will not wait indefinitely, which aligns with human psychological expectations and reduces dissatisfaction caused by "being constantly cut in line"; the algorithm's parameters are adjustable and can adapt to various scenarios.

According to one embodiment of the present invention, a system for predicting the functional state of a lung cancer patient may be provided, comprising: a sample data collection unit for collecting sample data including data on a plurality of explanatory variables and outcome variables for predicting the functional state of a lung cancer patient; a variable selection unit for selecting explanatory variables to be used in a learning model for predicting the functional state of a lung cancer patient among a plurality of explanatory variables of the sample data; a learning model construction unit for performing learning on a plurality of learning models based on the selected explanatory variables and outcome variables of the collected sample data and constructing a plurality of learning models for predicting the functional state of a lung cancer patient; a learning model selection unit for selecting a learning model for predicting the functional state of a lung cancer patient based on performance statistics from the constructed plurality of learning models; an explanatory model generation unit for generating a local surrogate model for explaining the correlation between the functional state of a lung cancer patient and explanatory variables based on the selected learning model; and a prediction result providing unit for providing a functional state prediction result that predicts the functional state of a lung cancer patient, who is the subject of analysis, from the explanatory variables of the subject of analysis based on the selected learning model and the generated local surrogate model.

This application relates to a method, device, equipment, and medium for dynamic prediction of Alzheimer's disease course. The method includes: acquiring multidimensional detection data of a patient; determining the patient's disease subtype and disease stage based on the multidimensional detection data and clinical diagnostic criteria; performing time-series analysis on the multidimensional detection data according to the disease subtype and the disease stage to obtain the patient's disease progression pattern; predicting the patient's future disease trajectory using a preset disease trajectory prediction model based on the disease progression pattern to obtain the patient's Alzheimer's disease course prediction result; and generating a recommended intervention plan for the patient based on the Alzheimer's disease course prediction result and preset clinical intervention rules. This method enables dynamic prediction of the Alzheimer's disease course and provides personalized intervention suggestions for patients, improving clinical treatment outcomes.

This application relates to a method, system, device, and medium for detecting visual function depth in patients with no light perception. It is applied in the field of visual function testing technology. The method includes: acquiring basic information, historical testing data, and parameter fixing rules for the target patient; generating an initial testing plan based on the basic information, historical testing data, and parameter fixing rules; performing visual function testing on the target patient based on the initial testing plan, generating real-time testing data and real-time operational data; adjusting the initial testing plan in real-time based on the real-time testing data and real-time operational data to generate a visual function testing plan; acquiring full-process testing data and full-process operational data; and generating a visual function depth testing report based on the visual function testing plan, full-process testing data, and full-process operational data. This application improves the accuracy of visual function testing and evaluation in patients with no light perception.

The application discloses a lumbar degenerative disease patient treatment expectation evaluation system and relates to the technical field of medical health information processing; the system comprises a data acquisition unit, a expectation evaluation unit, an influence factor analysis unit and a report generation unit; the data acquisition unit is used for collecting multidimensional information of a lumbar degenerative disease patient; the expectation evaluation unit is connected with the data acquisition unit and is used for quantitatively evaluating the treatment expectation of the patient based on the collected information; the influence factor analysis unit is connected with the expectation evaluation unit and is used for analyzing key factors influencing the treatment expectation of the patient; the report generation unit is connected with the expectation evaluation unit and the influence factor analysis unit and is used for integrating evaluation and analysis results and generating a visual evaluation report. The application integrates various evaluation tools that have been tested for reliability and validity, converts subjective inquiries that are originally scattered and depend on the personal experience of medical staff into a unified, standardized and multidimensional quantitative evaluation process, and ensures the scientificity and comparability of evaluation results.