11135 results about "Emergency medicine" patented technology

A system for remotely monitoring and managing health statuses of a plurality of users includes software instructions storable on a memory device usable by a computing device, the software instructions causing a hardware processor of the computing device to receive a plurality of sets of health-related information from a plurality of mobile computing devices of a plurality of users. The health-related information includes physiological information derived from wearable devices of the users indicative of an onset of symptoms associated with an infection, contact tracing data, and diagnosis data. The hardware processor determines exposure levels based on at least the contact tracing data, and determines user-specific risk states based on physiological information, diagnosis data, and exposure levels.

One or more data streams may be used for controlling multi-system interaction. For example, a data stream may be received, and a surgical option associated with a surgical instrument may be selected based on the data stream. A control signal associated with the surgical instrument may be generated based on the selected surgical option. A surgical device may receive an external data stream from a source external to the surgical system. The device may derive, based at least on the external data stream, decision contextual information. The device may select a surgical option associated with a surgical instrument based on the decision context information. The device may generate a visual indication of the decision context information associated with selecting the surgical option. The device may generate a control signal associated with the surgical instrument based on the selected surgical option.

A system for monitoring hemodynamic status of a patient can include a transducer, an adapter and one or more monitor devices. The adapter may be in communication with the transducer and the one or more monitor devices. The adapter can be configured to receive and process data from the transducer such as unprocessed physiological data. The adapter can be configured to transmit data to the monitor device(s) such as processed and / or unprocessed physiological data. The adapter can be configured to generate, and transmit to the monitor devices(s), user interface data for rendering interactive graphical user interfaces to display information such as physiological information relating to a hemodynamic status of the patient. The adapter can be configured to receive and process, from the monitor device(s) user commands or instructions to control an operation of the system or its components.

The invention provides a gynecological disease diagnosis and treatment method and system fused with an electronic medical record, and relates to the technical field of medical informationization, and the method comprises the steps: collecting the multi-modal diagnosis and treatment data of a patient, and constructing a personalized medical database through a self-encoding network; deploying a plurality of agents based on a dynamic causal network, and generating an initial treatment scheme in combination with deep reinforcement learning; through real-time data acquisition and a multi-objective optimization algorithm, the scheme is dynamically optimized by fusing expert decision features. According to the invention, personalized precise treatment of gynecological disease diagnosis and treatment can be realized, the treatment effect is improved, and the complication risk is reduced.

A system configured to facilitate monitoring a patient when the patient transitions between environments. The system can have an in-room display terminal configured to display indicia of a health of the patient for electronically monitoring the health of the patient within a healthcare environment. The system can receive, via the in-room display terminal, a request to initiate monitoring the patient with the in-room display terminal at the healthcare environment; access historical physiological data associated with the patient and generated by a home monitoring device before the patient enters the healthcare environment; access real-time physiological data associated with the patient and originating from a physiological monitoring device coupled to the patient within the healthcare environment; generate one or more physiological parameters from the real-time physiological data and the historical physiological data; and cause the in-room display terminal to display indicia of the one or more physiological parameters.

The invention discloses a patient medication risk early warning method, and relates to the technical field of medication risk evaluation. The method comprises the following steps: acquiring multi-dimensional data such as age, past medical history, allergy history, clinical examination data and prescription data of a patient, extracting chronic disease diagnosis information from the multi-dimensional data, and carrying out standardized coding to generate a common disease combination tag; calling a preset disease weight system, and calculating a common disease severity score in combination with an age factor; extracting metabolism-related indexes to construct a drug metabolism capability feature vector of the patient; mapping the drug combination, the co-disease tag and the metabolic feature vector to a clinical drug knowledge base to generate a comprehensive conflict detection result; and generating corresponding early warning information based on the conflict detection result and a preset early warning rule base, wherein the rule base comprises a mapping relationship between the conflict type and the early warning level. According to the method, drug use risk early warning can be realized according to the multi-disease coexistence state of the patient, and the drug use safety is improved.

The invention relates to an infection risk assessment method and system for nursing, and the method comprises the steps: collecting multi-source heterogeneous data of a patient, and fusing the multi-source heterogeneous data into a unified original data set; obtaining a standardized risk feature vector based on the original data set; inputting the risk feature vectors into a multi-modal risk assessment model, and outputting risk probabilities of individual infection and group infection; forming a personalized risk index based on the risk probability; according to the personalized risk indexes, the decision support system outputs hierarchical intervention measures matched with the risk levels, and records and feeds back execution conditions and intervention effect data of the hierarchical intervention measures for dynamically updating parameters of the risk assessment model; and integrating the intervention effect data and the early warning signal into feedback information to form a dynamic infection risk assessment system adapted to specific hospital characteristics. The accuracy of infection risk assessment of nursing can be improved.

The present application discloses a multifunctional health monitoring method and system based on a millimeter wave radar, and a computer readable storage medium. The method comprises: receiving an echo signal in a space, and on the basis of the echo signal, calculating point cloud data of a user and phase change data caused by the respiration and heartbeat of the user; calculating behaviors of the user on the basis of the point cloud data; calculating vital signs of the user on the basis of the phase change data, wherein the vital signs comprise respiration rate and heart rate; and on the basis of the behaviors and / or the vital signs, determining that the user triggers a set alarm event, and on the basis of the type of the triggered alarm event, performing a corresponding alarm operation.

A medical device with an implantable blood pump and a control and sensing unit configured to determine the flow rate generated by the blood pump when driven by an electric motor, wherein the flow rate is determined using peak-to-peak current data generated by the electric motor and, in some cases, associated heart rate data. In some embodiments, the validity of pulsatility of the resulting blood flow is determined and, if out of predetermined limits, an alarm may be actuated.

The invention discloses a neurosurgery patient postoperative care risk early warning system, and relates to the technical field of postoperative care risk early warning. According to the postoperative care risk early warning system for the neurosurgical patient, multi-source postoperative data such as brain waves, physiological indexes, behaviors and inflammation are integrated through the data acquisition module, single-index anomaly recognition is achieved through the sub-item risk assessment module, and then overall risk judgment is conducted by further fusing data when single indexes are normal through the comprehensive risk assessment module; finally, the early warning module performs graded early warning, so that multi-dimensional data fusion analysis is realized, abnormity of a single index can be found in time, potential overall risks can be mined through a comprehensive model, the comprehensiveness, accuracy and timeliness of early warning are improved, scientific and accurate risk judgment basis is provided for postoperative nursing, early intervention of medical staff is assisted, and the medical staff is prevented from suffering from early warning. Problems of single postoperative care risk assessment and lack of comprehensive judgment of existing neurosurgery patients are solved.

The invention relates to the technical field of health monitoring, and discloses a patient health monitoring method and system based on dynamic electrocardiogram analysis, and the method comprises the steps: synchronously obtaining multi-lead dynamic electrocardiogram data continuously collected by a patient within a preset duration, and related physiological parameters of exercise intensity, respiratory rate and body position change; performing dynamic self-adaptive preprocessing on the dynamic electrocardiogram data based on the associated physiological parameters to obtain standardized electrocardiosignals; extracting a multi-dimensional characteristic parameter set from the standardized electrocardiosignal, and sampling according to a preset time window to generate a characteristic parameter sequence with a timestamp; and inputting the characteristic parameter sequence into a dynamic optimization analysis model capable of iteratively updating parameters through real-time physiological parameter feedback, and performing graded evaluation on the health state of the patient to generate an evaluation result. The system corresponds to the method. By adopting the method and the system, the accuracy of dynamic electrocardiogram monitoring and the evaluation refinement level meet the dynamic monitoring requirement of cardiovascular health.

A computer-implemented system for personalized health optimization constructs a confidence-weighted personal health knowledge graph (PHKG) from heterogeneous data, including wearable sensors, medical devices, lab results, medication logs, and conversational inputs. A multi-stage causal-inference stack identifies modifiable drivers of outcomes using layered methods (e.g., MI, GAM, Neural Granger, DAG-GNN), and simulates candidate interventions. A recommendation engine ranks lifestyle or pharmacologic actions using a benefit-to-friction score, selecting a personalized intervention aligned with user readiness and clinical safety constraints. Interventions may include a minimum effective dose (MED), optimal level, adaptive low-dose, or behavioral challenge. Optional modules include reinforcement learning for timing adaptation and privacy-preserving on-device inference. The system operates across domains including metabolic, cardiovascular, renal, sleep, stress, and medication response, enabling cross-condition synergy evaluation. The architecture is modular, supports runtime plug-in targets, and adapts in real time with or without continuous clinical oversight, depending on deployment.

The invention relates to the technical field of medical information, in particular to an intelligent prediction model and method for postoperative complications of anesthetized patients, and the method comprises the steps: collecting preoperative to postoperative complete-cycle clinical data of a patient through a medical data interface; analyzing operation codes to generate risk features, extracting vital sign dynamic features, and establishing a complication probability mapping relation through a multi-modal fusion network; combining the complication probability and pharmacokinetic parameters to construct an optimization model, and solving an individualized anesthetic dosage interval by using a gradient descent algorithm; vital signs are dynamically monitored in the operation, a dose re-optimization mechanism is triggered, the infusion rate is adjusted, and a closed-loop control link is formed; and generating a visual decision report. According to the method, through deep integration of complete-cycle clinical data and multi-modal feature modeling, preoperative physiological parameters, operation coding semantic information and intraoperative vital sign dynamic modes are subjected to fusion analysis, a nonlinear mapping relation between dosage and complication probability is constructed, and the risk prediction precision and individualized adaptability are remarkably improved.

The invention relates to the technical field of medical equipment and intelligent learning, in particular to a critical patient vital sign dynamic monitoring and intelligent early warning system which comprises a multi-modal data acquisition module, a medical equipment linkage control module and an intelligent early warning decision module. A dual-channel Kalman filtering state updating model, a biological impedance monitoring model and an anti-interference and signal optimization model are established in the multi-modal data acquisition module, and multi-dimensional physiological monitoring information of the critical patient is obtained through the models; a physiological information standardization model and a safety control mechanism are arranged in the medical equipment linkage control module, and then multi-dimensional physiological monitoring information is cooperatively controlled to output multi-modal vital sign data; and the intelligent early warning decision module receives and analyzes the multi-modal vital sign data to obtain power spectral density, and predicts the disease development condition of the critical patient in combination with the depth prediction model and the multi-modal vital sign data. All the modules work cooperatively to achieve dynamic monitoring and intelligent early warning of vital signs of critical patients.

Systems and methods for designing and implementing patient-specific surgical procedures and / or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

The invention discloses a chronic disease patient personalized diet and exercise guidance system and method, and relates to the technical field of medical health information, and the system comprises a data sensing module which continuously collects the dynamic physiological data, behavior data and environment variable data of a patient through an intelligent sensing device, the dynamic physiological data comprises a heart rate time sequence, a step number time sequence and a blood glucose concentration time sequence monitored by the wearable device, and the behavior data comprises a medication operation record with a timestamp and a patient's daily symptom self-grading number. According to the personalized diet and exercise guidance system and method for the chronic disease patient, the time synchronization precision of multi-source data is effectively improved, the accuracy of medication compliance monitoring and physiological index correlation analysis is ensured, and by establishing the dynamic correlation model of the environment temperature and the human body metabolic rate, the accuracy of medication compliance monitoring and physiological index correlation analysis is improved. The timeliness and safety of clinical intervention are improved, and powerful support is provided for health management of chronic disease patients.

The invention discloses a battery pack health state prediction method and device, and the method comprises the steps: building a battery pack three-dimensional matrix based on collected historical data of a plurality of cells in a battery pack, carrying out the sliding window processing of the battery pack three-dimensional matrix, and generating model input data; determining a battery pack health state prediction model, wherein the battery pack health state prediction model comprises an SE-TCN network, a Performer encoder and a space-time fusion layer; and inputting the model input data into the battery pack health state prediction model to obtain a health state prediction value of the battery pack. According to the method, the time dependency characteristics of the time sequence data of the plurality of battery cells in the battery pack and the global dependency relationship among the battery cells are respectively captured, and the time sequence characteristics and the space correlation characteristics are fused, so that the model can capture the complex interaction relationship among the plurality of battery cells; and the accuracy and robustness of battery pack health state prediction are greatly improved.

The invention provides a contrast image analysis method and device for digital diagnosis and treatment equipment. The method comprises the following steps: predicting a disease progress condition based on a focus volume characteristic parameter, a density statistical parameter and a quantitative index of a focus surrounding tissue density difference; judging whether the prediction result of the disease progress condition reaches a preset risk threshold range or not, if the prediction result is within the preset risk threshold range, judging that the disease progress condition is in a risk state, and generating high-risk early warning information; and judging the severity of the illness state according to the judgment result of the illness state progress condition prediction and in combination with the fitting data of the focus volume and density changing along with time, performing stage classification on the focus progress process, and generating a quantitative illness state diagnosis report.

The invention relates to the technical field of health status assessment, in particular to a cardiovascular disease layered early warning system, which is characterized in that blood vessel compliance and pulse wave velocity are extracted, arterial dilation capacity is measured, blood vessel elasticity is calculated, blood perfusion distribution is detected, and abnormal vasoconstriction is classified on the basis of a hemodynamic state and a heart electrical activity state; in combination with the thickness of the blood vessel wall and the blood vessel stenosis rate, the plaque deposition condition is analyzed, the arteriosclerosis score is calculated, the thrombosis tendency is detected, the local blood flow blocking degree is quantified, the isolation forest algorithm is adopted, the blood flow dynamic change is calculated, the heart rhythm abnormality is analyzed, the acute vascular occlusion probability is measured, and the sudden heart rhythm abnormality risk is evaluated. And based on a gradient boosting tree algorithm, screening high-risk cases, matching medical intervention measures, setting a risk buffer mechanism, adjusting an individual health management strategy, and optimizing a cardiovascular risk hierarchical early warning system.

The invention relates to the technical field of medical instrument control, in particular to a lung biopsy sampling needle stabilizing method and system based on dynamic respiration compensation. The stabilizing method comprises the following steps: acquiring a respiratory movement signal of a patient in real time through a multi-source biosensor, wherein the multi-source biosensor comprises an optical fiber strain sensor array, a diaphragm myoelectricity sensor and a respiratory airflow sensor which are deployed at the chest and abdomen. According to the method, millimeter-microsecond-level accurate and stable control over the lung biopsy sampling process is achieved by establishing a cooperation mechanism of a breathing phase-displacement nonlinear mapping model and a macro-micro composite driving framework. The biological rationality of displacement prediction is improved through deep coupling of multi-mode breathing signals, and the chest wall non-uniform deformation gradient is analyzed in space based on cross topology deployment of an optical fiber strain sensor in an intercostal space area in combination with cross-scale feature fusion of myoelectricity and airflow sensing.

The invention provides an hemorrhagic brain injury intracranial pressure prediction and early warning system based on a CT image, and the system comprises a data collection module which is used for collecting the original data of a target data source of a target patient; the hybrid model comprises a 3DCNN model used for extracting hematoma spatial features of the head CT image sequence; the LSTM model is used for extracting hematoma time sequence characteristics of the physiological signals; the multi-modal fusion module is used for fusing the hematoma space features and the hematoma time sequence features and outputting hematoma fusion features; the prediction module is used for predicting an ICP sequence of the target patient in a future preset time period based on the hematoma fusion features; the self-adaptive early warning module is used for outputting a visual result based on the predicted ICP sequence in the future preset time period; and through a dynamic threshold adjustment mechanism, adjusting an early warning threshold of the target patient in combination with the case information, and triggering graded early warning in real time. Through personalized data processing and dynamic early warning, accurate, efficient and real-time patient risk management is realized.

The invention relates to a blood oxygen and heart rate monitoring system and method based on PPG signals and motion data, and relates to the technical field of medical health and intelligent wearable equipment. The system comprises a PPG signal acquisition module, a motion sensor module, a signal preprocessing module, a dynamic signal segmentation and quality evaluation module, an oxyhemoglobin saturation calculation module, a heart rate calculation module, a motion state recognition module, a self-adaptive Kalman filtering module and an output display module. The method and the device have the effects of realizing a function of identifying the motion state by the wearable equipment, improving the signal interference suppression capability and widening the application scene.

The embodiment of the invention provides a multifunctional vital sign monitoring method and system. The method comprises the following steps: acquiring real-time vital sign signals of a user in different physiological states, and forming a multi-dimensional monitoring data set in combination with environmental parameters; dynamically adjusting the monitoring frequency and precision of real-time vital sign signals by applying an intelligent regulation and control algorithm according to the multi-dimensional monitoring data set, generating a data collection strategy, and collecting vital sign data based on the data collection strategy; based on the vital sign data, an instant evaluation engine is used for analyzing and processing monitored vital sign abnormal conditions, and a health condition evaluation result and personalized health suggestions are generated; and safely synchronizing the health condition evaluation result and the personalized suggestion to a medical monitoring system of the user to realize remote medical service and continuous health management. According to the technical scheme provided by the embodiment of the invention, the accuracy and efficiency of health monitoring are improved.

The invention relates to the technical field of disease risk assessment, in particular to a prediction support system for a high risk level of a sepsis patient. According to the abnormal degree of the physiological data in the abnormal time period of each category and the time interval between the abnormal time period and the adjacent abnormal time period, the importance degree of each category is obtained; selecting a feature category and a target time period based on the importance, and obtaining the severity of the target time period according to the importance difference between all categories in the target time period and the feature category and the number of categories with abnormal physiological data in the target time period; and acquiring a high risk value according to the change trend and the time interval of the severity of the target time period and the adjacent target time period, and adjusting and analyzing all types of physiological data in the time period by using the high risk value so as to predict the high risk of sepsis of the to-be-detected patient. According to the method, the difference of the physiological data in the time period in which the abnormal expression has the difference is dynamically expanded through the high-risk value, so that the accuracy of predicting the high-risk condition of sepsis is improved.

The invention relates to the field of hospital patient safety management, and particularly discloses a hospital patient safety management system and method based on data analysis, and the method comprises the steps: firstly obtaining the hospitalization record of a patient, the heart rate values, collected by an intelligent device, of the patient at a plurality of preset time points, and the blood pressure values, collected by the intelligent device, of the patient at a plurality of preset time points; and then feature extraction and correlation analysis are carried out on the three by using a deep learning technology, and finally whether a patient safety early warning is sent out is judged through a classifier, so that the patient nursing quality is improved, and the response speed to the health condition of the patient is improved.

The invention relates to the technical field of motor testing, in particular to an online monitoring method and system for the health state of a carbon brush of a motor, and the method comprises the steps: determining a target operation load environment cluster of a target carbon brush at the current moment according to the pre-obtained load condition and operation environment condition of the target carbon brush in the current operation time period; screening out a reference operation time period; determining an abnormal loss factor and a potential abnormal wear feature vector of the target carbon brush at the current moment; according to the Lyapunov index of the vibration signal of the target carbon brush in the current operation time period and the temperature change and the current change of the target carbon brush in the current operation time period, determining a wear fault display possible feature vector; therefore, the health state of the target carbon brush at the current moment is determined. According to the invention, the potential abnormal wear condition of the target carbon brush at the current moment is considered, the health state of the carbon brush is monitored, and the timeliness of abnormal health state monitoring of the carbon brush is improved.

Introduced here are approaches for capturing and recording medical event details during cardiopulmonary resuscitation (CPR) events and other emergency medical events. A recording begins when an operator inputs a request to initiate recording. The operator may use a recording system with a combination of input devices to record medical actions. The recording system acknowledges the operator's actions by various means which may include icons displayed for the operator. The operator's interactions with the icons indicate corresponding actions observed by the operator, which are recorded as a log of the actions in temporal order that serves as a non-transient record of the medical event. An analysis of the recorded details of the medical event, by comparing the actions to prescribed actions in one or more professional standards, can provide an assessment of the actions taken.

The present disclosure provides a medication dispensing system comprised of a medication dispensing device and a keyed and associated medication cartridge. The device includes a housing containing a cartridge carrier, designed with a specific connector to align with the medication cartridge. The device also includes an authentication mechanism that works in conjunction with an activator mechanism. When operated, the activator mechanism causes the cartridge carrier to move the medication cartridge between a stored position and a dispensing position, where it can be compressed to safely and efficiently dispense medication to a user.

A patient support apparatus includes a frame, a support surface, a caregiver control panel, a patient control panel, and a controller. The caregiver control panel includes a lock out control that, when manually activated by a caregiver, locks out one or more particular controls on the patient control panel. The controller is also adapted to automatically lock out the one or more particular controls, irrespective of the state of the lock out control, in response to a triggering event. The triggering event may include one or more of the following: the arming of an exit detection system, the arming of a monitoring system, the passage of a predetermined time period, the use of a scale, the zeroing of the scale, the receipt of a fall risk score for the patient, etc. The caregiver control panel may allow the caregiver to select what patient controls are automatically locked out.

The invention relates to the technical field of medical early warning, and discloses a graded early warning system based on multi-parameter vital sign detection. According to the system, real-time physiological parameters such as the heart rate, the blood pressure, the oxyhemoglobin saturation and the body temperature of a patient are collected through vital sign monitoring equipment; and inputting the parameters into a feature extraction network, generating a multi-dimensional physiological feature vector, and constructing a dynamic risk assessment matrix containing physiological state change trends of different time windows according to the multi-dimensional physiological feature vector. Dividing risk grade intervals according to a preset grading early warning threshold value, adjusting the intervals by adopting a self-adaptive weight distribution strategy, and generating a comprehensive risk score; and when the score exceeds the preset early warning trigger line, activating a corresponding early warning response mechanism. The system can realize comprehensive dynamic assessment of the physiological status of the patient, is suitable for emergency treatment, intensive care and chronic disease nursing scenes, and meets the clinical health risk monitoring and early warning requirements.