76 results about "Physiological model" patented technology

The invention discloses an intelligent anesthesia monitoring and regulation system, and relates to the technical field of medical monitoring and automatic control. The system comprises a data acquisition and preprocessing module, a dynamic physiological model construction module, an anesthesia state evaluation and prediction module, a real-time uncertainty quantification module and a layered hybrid closed-loop regulation and control module. The layered mixed closed-loop regulation and control module is used for predicting the change track of the anesthesia depth under different administration strategies in a few minutes in the future through a dynamic physiological model, and calculating an optimal administration rate sequence which can maintain the anesthesia depth in a target range and can minimize the drug dosage and blood pressure fluctuation through online optimization, so as to obtain the optimal administration rate sequence. The confidence score is referenced when controlling the administration rate. The system can realize highly personalized anesthesia monitoring and regulation and control, and has the advantages of quantifiability, high reliability, stable operation, accurate regulation and control and the like.

The invention relates to the technical field of intelligent education, in particular to an AI-based nursing teaching scene construction and evaluation system. According to the system, a dynamic case script is generated through historical case data, and a virtual patient physiological model is constructed; creating three teaching scenes of community follow-up visit, emergency rescue and traditional Chinese medicine nursing fusion by using a mixed reality technology; a personalized learning portrait of the student is constructed through a pre-test, and accurate matching between the ability of the student and the teaching content is formed; a double-track system skill training module is built, and AI tutor guidance and teacher guidance are integrated; and an ability evaluation system based on operation data is established, and intelligent pushing of adaptive training content is realized. The problems that in traditional nursing teaching, scene authenticity is insufficient, personalized training is insufficient, and clinical thinking is difficult to cultivate are solved, and the clinical operation skill and decision judgment ability of students are improved.

The invention provides an echocardiogram video abstraction method based on a hierarchical space-time attention mechanism, and the method comprises the steps: obtaining an echocardiogram, inputting the echocardiogram into an anatomy and physiological basic model of a key frame recognition task, and obtaining key frame information; inputting the key frame information into a multi-dimensional spatial-temporal feature extraction model to obtain feature information and frequency domain information; fusing the feature information and the frequency domain information; performing key frame enhancement processing on the fusion information by adopting an SE channel attention mechanism to obtain key frame anatomical feature expression; processing the key frame anatomical feature expression by adopting a key frame backtracking strategy and a classification network to obtain a key frame identification classification result; performing left ventricle segmentation on the classified image by adopting an improved U-Net left ventricle region segmentation network; estimating the area and volume of the left ventricle based on a segmentation result; fusing the estimated left ventricular area and volume by adopting a hierarchical attention mechanism and a cross-layer information fusion strategy to obtain a ventricular volume result; according to the method, through abstract extraction of the key information of the ultrasonic cardiac video, the clinical practicability of intelligent screening and auxiliary diagnosis of cardiovascular diseases based on ultrasonic is improved.

The invention discloses a farmland environment intelligent monitoring system and method based on multi-modal sensor fusion, and relates to the technical field of farmland environment monitoring, and the method comprises the steps: obtaining first integrated data in a farmland, and carrying out the preprocessing of the first integrated data; constructing a sound wave attenuation model based on the preprocessed first comprehensive data, and inverting the air humidity and the vegetation density through the sound wave attenuation model; conflict detection and resolution processing are carried out based on an inversion result; and constructing a three-dimensional microclimate field based on a conflict resolution result, and carrying out verification and iterative optimization by fusing a crop physiological model. According to the method, farmland space-time heterogeneity is adapted by means of a dynamic space-time weight coefficient, so that a three-dimensional microclimate field covering horizontal and vertical dimensions is constructed, and a'monitoring-verification-optimization 'closed loop is formed through crop physiological model association and iterative optimization; the problems of limitation of a single sensor, low inversion precision, poor data continuity, extensive monitoring and lack of decision association in the prior art are effectively broken through.

The invention relates to a clinical surgery experiment virtual simulation teaching method and device, equipment and a medium. The method comprises the following steps: responding to an obtained teaching task configuration file; the teaching task configuration file comprises a clinical scene and an evaluation standard; calling a corresponding three-dimensional digital physiological model according to the teaching task configuration file, and performing special effect rendering according to the clinical scene to obtain a visual virtual simulation experiment scene; responding to the obtained user action data, performing real-time deformation on the three-dimensional digital physiological model, and generating feedback parameters; the feedback parameter is used for indicating the interaction control to work according to the feedback parameter; and comparing the user action data with preset standard expert action data, and generating a teaching evaluation result based on an evaluation standard. By adopting the method, high fidelity can be carried out on the whole process of clinical surgery experiment teaching, effective technical support is provided for medical skill teaching, and the method is suitable for multi-scene and multi-level medical teaching practice requirements.

The invention provides an electrocardiosignal anomaly detection method fused with electrocardiosignal propagation simulation. The method comprises the steps that a space diagram structure is established based on ECG data; performing spatial position mapping on each electrode and establishing a heart simulation physiological model; feature extraction is carried out on each electrode node, and a dynamic space diagram structure is constructed; based on a dynamic graph structure, through multi-layer graph convolution updating and time aggregation, global space-time feature vectors are output, electrocardiosignal detection categories are output through a classifier, a space dynamic neural network model is constructed, a total loss function is established for model optimization, the electrocardiosignal detection categories are predicted, and quantitative recognition of abnormal propagation paths is achieved. And iteratively optimizing the dynamic graph structure. According to the method, unified modeling is carried out through time dynamics and spatial relevance of physiological signals, the defect that a static graph cannot capture abnormal changes of a propagation path in a pathological state is overcome, and the accuracy of cardiovascular disease diagnosis and the effectiveness of clinical decision support are remarkably improved.

A method of monitoring a subject with an opto-physiological sensor system is described herein. The method comprises obtaining a model of the optical properties of at least one body tissue type to be monitored, wherein the model of the optical properties comprises a definition of static (DC) and dynamic (AC) components of transmitted optical power and a definition of a source-detector separation related to a normalised path length for an illumination source of the opto-physiological sensor: obtaining an indication of at least one physiological property of the subject from a wearable device worn by the subject: obtaining an indication of at least one physical variable from the wearable device worn by the subject; determining, using the opto-physiological model, how the at least one physical variable affects the at least one physiological property; and determining a corrected value for the physiological property based on the determination of how the at least one physical variable affects the at least one physiological property.

The invention discloses a physiologically-driven standardized intelligent simulation patient system, which comprises an adult simulation patient, a computer, a computer, a computer, a computer, a computer, a computer, a computer and a computer, wherein the adult simulation patient is made of a simulation man-made high polymer material, is environment-friendly and pollution-free, has obvious anatomical marks, touches two nipples, ribs, sternum and xiphoid processes, is flexible in joints, and realizes various operation body positions; wherein the physiological data comprises the heart rate, the blood pressure and the respiratory rate, the data is combined with a built-in physiological model to simulate the physiological state of a patient in real time, a monitor is simulated, real-time physiological data monitoring and feedback are provided, and the vital signs, the heart rate, the blood pressure and the oxyhemoglobin saturation of the simulated patient are displayed; the system has strong physiologically-driven standardized intelligent comprehensive simulation patient, can perform situational teaching according to a real clinical environment, effectively assesses the clinical thinking and personal operation skills of students, can greatly improve the team cooperation spirit of the students, and simulates the real physiological response and clinical scene.

A processor-implemented method comprises obtaining measured glucose values of a person, fitting a physiological model to a portion of the measured glucose values within a time window after a start of a meal to determine meal-specific values of parameters of the physiological model that characterizes the person's glycemic response to the meal, estimating an amount of residual glucose or residual carbohydrate of the meal that would not be counteracted by insulin already delivered to the person using the physiological model and the meal-specific values of the parameters of the physiological model, and determining a dose of a correction bolus to be delivered to the person based on the amount of residual glucose or residual carbohydrate of the meal.

The invention relates to the technical field of program control, in particular to a program control system and method based on a pressure distribution test. Comprising a multi-dimensional pressure data acquisition unit, a control logic decision unit and a cooperative control unit. Through the combination of the high-resolution flexible sensor array and the deep learning inversion model, the spine curvature and the shoulder-hip proportion of the user can be automatically identified, the shoulder, the waist, the hip and other skeleton protruding parts can be accurately tracked, a unique and accurate personalized physiological model basis is provided for subsequent analysis, and the user experience is improved. A'ideal pressure-bearing database 'constructed based on clinical medical knowledge is embedded, a'digital human' pressure state sensed in real time is compared with a matched'health template ', a dynamic weight mechanism with clinical significance is introduced, and a'pressure-bearing difference value' aiming at preventing pressure sores and maintaining spine health is calculated; the purpose of converting an adjustment target from fuzzy'more comfortable 'into quantitative physiological index optimization with definite health orientation is achieved.

The invention provides an intelligent binding skill operation training robot and a control method. The operation training robot comprises a simulation human body model, a dynamic driving system, a wound simulation module, a sensing matrix system and a control system. The simulation human body model, the dynamic driving system, the wound simulation module, the sensing matrix system, the control system and the artificial intelligence evaluation system are combined to construct an intelligent simulation training platform which is highly vivid, accurate in feedback and capable of meeting various training requirements; according to the operation training robot, a passive static model is changed into an active interactive intelligent robot, the operation training robot is no longer only an operation object, but a simulated wounded person capable of sensing operation and dynamically responding according to an internal physiological model, and therefore a training environment with the sense of reality and challenging is provided for trainees, and the training efficiency is improved. And the operation skill, clinical decision and strain capability can be comprehensively exercised.

This invention relates to a method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model. First, a refined, segmented, multi-layered human physiological thermal model is constructed. Based on this model, a set of coupled biological heat transfer equations is established, embedding an active thermoregulation mechanism mediated by the human central nervous system. This set of equations is solved by deeply and dynamically coupling skin temperature, core temperature, and physiological parameters such as blood perfusion rate, metabolic rate, and sweat evaporation. Subsequently, by setting the initial and boundary conditions of a fire scene operation, the coupled equations are solved in one go using numerical methods. During the same solution process, the system synchronously and in parallel performs segmented skin burn risk assessment and overall human heat stress risk assessment. This invention overcomes the limitations of traditional models in segmented prediction and coupled active thermoregulation, achieving effective synchronous early warning of skin burn risk and overall heat stress risk in various parts of the human body under extreme thermal environments.

Devices, systems, and methods for culturing cells in a 3-dimensional (3-D) arrangement are disclosed. A microfluidic device includes a plurality of layers including a first layer defining a first chamber for holding a first cell culture, a second layer defining a second chamber for holding a second cell culture, and channel layers defining channels. The first and second chambers are fluidically coupled and enable the first cell culture and the second cell culture to grow in the 3-D environment. A porous membrane is positioned between the first and second layers to enable interfacing between the first cell culture and the second cell culture. The channels are fluidically coupled with the first chamber or the second chamber to enable passage of fluids with respect to the cells cultured. The microfluidic device enables development of more physiologically accurate models of complex tissues or organs for basic science research or drug development.

A testing device and testing method of a hydrocephalus shunt valve. It relates to the technical field of medical device testing. The long-term reliability and adaptability of the shunt valve can be simulated in a complex physiological environment. The testing device of the hydrocephalus shunt valve comprises a controller, a body position simulation device, an ICP pressure chamber, an IPP pressure chamber, a backflow pump, a flow sensor and a circulating pipeline. The body position simulation device comprises a support, a pair of long arms simulating the human torso and a pair of short arms simulating the human head and neck, and a motor one driving the pair of long arms to swing and a motor two driving the pair of short arms to swing. The present application combines the in-vitro hardware testing platform with different patient physiological models, can reproduce physiological characteristics such as basic body position posture change, body temperature circadian rhythm fluctuation and long-term applicability, and realizes the in-vitro testing of the shunt valve quickly, at low cost and with high authenticity. The present application can quantify the performance of the shunt valve in a real physiological environment.

The application provides a three-dimensional face animation generation method and system based on physiological prior art, and belongs to the fields of computer vision and computer simulation, and comprises the following steps: S1: obtaining an initial three-dimensional face model based on CT and MRI scanning section images; correcting, mesh processing and filling the initial three-dimensional face model to obtain a three-dimensional face physiological model; S2: using an unstructured Delaunay algorithm to realize unstructured tetrahedral partitioning on the three-dimensional face physiological model to obtain a three-dimensional face physiological model mesh partitioning result; S3: performing biomechanical modeling on the three-dimensional face physiological model mesh partitioning result; S4: adding contact constraints and boundary conditions between different tissues of the three-dimensional face physiological model to construct a finite element analysis model to simulate facial deformation; and S5: solving and analyzing the finite element model to obtain a three-dimensional face animation. The method can generate high-quality three-dimensional face animations based on a small amount of samples.

Systems and methods for training and applying a foundation model for processing measurement data of at least one physiological parameter associated with an anatomical target region. A method comprises obtaining text data describing one or more medical conditions associated with the anatomical target region; generating, using one or more physiological models associated with the anatomical target region, synthesized measurement data of the at least one physiological parameter based on the text data; and training the foundation model based on the text data and the synthesized measurement data.

The invention relates to the technical field of medical equipment and signal processing, in particular to a cerebral infarction thrombectomy postoperative intracranial pressure management method and system based on fNIRS-TCD combined monitoring, and the method comprises the steps: firstly, synchronously collecting fNIRS cerebral blood oxygen concentration change data and TCD cerebral blood flow velocity data through a head-mounted device, and calculating a pulsation index; then, on the basis of a Bayesian data assimilation framework, constructing the data into a real observation vector, combining a state vector predicted by an intracranial physiological model and an observation vector predicted by an observation model, calculating a Kalman gain through an iterative integrated Kalman algorithm, and further correcting the state vector in real time; and obtaining state estimation containing precise intracranial pressure. And finally, the system carries out three-level early warning according to the real-time intracranial pressure data in combination with brain blood oxygen and pulsation index changes, and carries out stepped individual management from observation, drug intervention to emergency rescue according to the three-level early warning. According to the invention, the intracranial pressure management accuracy is improved.

This invention provides a subway station fire evacuation simulation method and system, belonging to the field of rail transit technology. Based on the lattice-Boltzmann method, the lattice-Boltzmann sublattice model obtained by large eddy simulation is integrated with an obstacle flow simulation model and a smoke diffusion model to construct a lattice-Boltzmann fire model. Sensory, psychological, and physiological models are constructed based on the lattice-Boltzmann fire model, and combined with a social force model to construct an evacuation model. The lattice-Boltzmann fire model is combined with the evacuation model to simulate evacuees' real-time avoidance of fire smoke. This invention achieves a substantial integration of the fire smoke evolution and evacuation processes, simulating their interaction and better recreating actual fire scenarios, providing certain benefits for engineering practice.

The invention belongs to the technical field of edge calculation and artificial intelligence, and particularly relates to a physiological feature modeling pitaya edge decision-making system which comprises a physiological data acquisition module, an edge calculation module, a local storage module, a physiological modeling module, a real-time decision-making module, an edge control and feedback module and a safety communication module and is deployed on a pitaya device or a connection terminal. Through the above scheme, decision delay can be significantly reduced, data privacy security can be improved, bandwidth energy consumption overhead can be reduced, high-precision personalized edge intelligence can be realized, and system robustness can be enhanced.

The invention relates to a diabetes early subtype recognition and risk prediction system based on continuous blood glucose monitoring. The system comprises a data preprocessing module, a subtype recognition module, a physiological modeling module, a residual prediction module and a fusion prediction module. The data preprocessing module is used for interpolating and standardizing the continuous blood glucose monitoring data; the subtype recognition module is used for extracting a typical daily curve, performing clustering analysis and outputting a metabolic subtype tag; the physiological modeling module is used for constructing and fitting a glucose-insulin kinetic model and outputting personalized physiological parameters; the residual prediction module is used for training the LSTM network and predicting the residual of the dynamic model; and the fusion prediction module is used for dynamically fusing output of the two types of models and generating a final blood glucose prediction result. According to the method, subtypes of the early stage of diabetes can be recognized, a prediction analysis method with high precision and high interpretability can be provided for different subtypes, and individualized dynamic mode recognition and hybrid prediction based on continuous blood glucose monitoring (CGM) data are achieved.

The application discloses a kind of cardiac transmembrane potential imaging methods based on 12 lead electrocardiogram joint physiological model and attention mechanism, to solve the serious ill-conditioned problem caused by information asymmetry between 12 lead signal and TMP, reconstruct cardiac transmembrane potential from 12 lead ECG signal.In order to obtain the dynamic physiological information of BSP and TMP, the application introduces self-attention mechanism to capture the long-term, periodic dynamic activation process characteristics of cardiac electrophysiology, extract the similarity within the sequence, calculate the attention score, then multiply the score with the input sequence, extract the key segment in the sequence, i.e.the segment that is expected to be "noticed".Meanwhile, the application regards the inverse problem as the potential feature mapping of TMP on BSP, captures the dynamic characteristics of inverse relationship by introducing cross-attention module, learns the inverse feature mapping relationship from a large number of data pairs using neural network, and solves the ill-posed problem of inverse problem.

Methods and systems are disclosed for assisting with the management of anemia in a patient. In some examples, the method includes accessing patient parameters associated with the patient, wherein the patient parameters include an aligned hemoglobin time series for the patient generated from multiple measurement sources. The method then includes accessing a physiology-based model and adapting the physiology-based model into a patient specific model that predicts future hemoglobin levels for the patient based on one or more erythropoiesis-stimulating agent (ESA) dosing regimens, wherein adapting the physiology-based model to the patient specific model utilizes the patient parameters, and generates estimates of patient-specific physiological characteristics. The method then includes running simulations with the patient specific model and determining a recommended ESA dose. The recommended ESA dose that the model predicts will cause either or both of the patient's hematocrit or hemoglobin concentration to reach a desired range within a specified time frame.

A method for monitoring a subject using an optical physiological sensor system includes obtaining an optical property model of at least one biological tissue type to be monitored, the optical property model including definitions of static and dynamic components of transmitted optical power and a source-to-detector distance related to a normalized optical path length of an illumination source of the optical physiological sensor. The method also includes obtaining an indication of at least one physiological property of the subject from a wearable device worn by the subject, obtaining an indication of at least one physical variable from the wearable device worn by the subject, determining using the optical physiological model how the at least one physical variable affects the at least one physiological property, and determining a correction value for the physiological property based on the determination of how the at least one physical variable affects the at least one physiological property.

The invention discloses an artificial intelligence-based anesthesia efficacy dynamic evaluation system and method, and relates to the technical field of anesthesia efficacy evaluation, and the system comprises a simulation prediction unit, a comparison correction unit, an abnormity backtracking unit, and an output early warning unit. The simulation and prediction unit is used for collecting real-time physiological parameters of a patient through medical monitoring equipment, constructing a virtual anesthesia mirror image according to the real-time physiological parameters, generating a virtual physiological model through a pharmacokinetic algorithm, outputting anesthesia efficacy evaluation data and sending the anesthesia efficacy evaluation data to the comparison and correction unit; the situation that anesthesia is too deep or too shallow due to inaccurate evaluation can be reduced, the operation risk is reduced, the life safety of a patient is guaranteed, an anesthetist can know possible anesthesia state changes of the patient in advance and adjust an anesthesia scheme in time, for example, the infusion rate, dosage and the like of anesthetics are adjusted, the stability of the anesthesia state in the operation process is guaranteed, and the operation safety is improved. The smooth proceeding degree of an operation is improved, and a more comprehensive and detailed decision basis is provided for anesthetists.

The invention relates to a method (1900) performed by an anesthetizing monitoring unit (110), the method comprising: controlling (1910) transmission of a first plurality of stimuli signals (210) using a first set of stimuli signal characteristics, receiving (1920) input data indicative of response signals (230) received in response to the transmitted stimuli signals, generating (1930) parameters (fa, fb) indicative of a physiological response of a subject to the first plurality of stimuli signals (210) using a first physiological model (FUNC1), mapping (1940) the generated parameters (fa, fb) to a Level-Of-Block value using a second model (FUNC2).

The invention relates to the technical field of animal husbandry management, in particular to a shift milk yield prediction and mastitis early warning method and system based on a dairy cow physiological model, and the method comprises the steps: 1, obtaining historical shift milk yield data of an individual dairy cow, and carrying out the preprocessing of the data; step 2, constructing physiological lactation models, and constructing and training a physiological lactation model for each dairy cow to analyze the milk yield of the dairy cow; 3, constructing a robust linear trend analysis model, and analyzing the change trend of the actual milk yield of the dairy cow in the recent period; and step 4, designing mastitis early warning logic, and performing milk yield analysis and mastitis early warning according to results output in the step 2 and the step 3. According to the method, the milk yield is accurately predicted in combination with the physiological characteristics of the dairy cow, and early and reliable mastitis early warning is carried out based on the dynamic change of the milk yield.

A processor-implemented method includes obtaining a measured glucose value of a person; fitting a physiological model to a portion of the measured glucose values within a window of time following the start of the meal to determine meal-specific values of parameters of the physiological model, the physiological model characterizing a glycemic response of the person to the meal; using the physiological model and meal-specific values of parameters of the physiological model to estimate the amount of residual glucose or carbohydrate of the meal that will not be counteracted by insulin that has been delivered to the person; and determining a dose of a corrected bolus to be delivered to the person based on the amount of residual glucose or residual carbohydrate of the meal.

Methods and systems are disclosed for assisting with the management of anemia in a patient. In some examples, the method includes accessing patient parameters associated with the patient, wherein the patient parameters include an aligned hemoglobin time series for the patient generated from multiple measurement sources. The method then includes accessing a physiology-based model and adapting the physiology-based model into a patient specific model that predicts future hemoglobin levels for the patient based on one or more erythropoiesis-stimulating agent (ESA) dosing regimens, wherein adapting the physiology-based model to the patient specific model utilizes the patient parameters, and generates estimates of patient-specific physiological characteristics. The method then includes running simulations with the patient specific model and determining a recommended ESA dose. The recommended ESA dose that the model predicts will cause either or both of the patient's hematocrit or hemoglobin concentration to reach a desired range within a specified time frame.

The invention provides an intermittent heart rate gating ankle rehabilitation robot system and a training method, and belongs to the technical field of rehabilitation engineering, man-machine interaction and motion control. The training method comprises the steps that real-time heart rate data of a patient is collected and processed, and the maximum heart rate is estimated; parameters such as a double-threshold ratio and a state duration time threshold for switching the high power mode and the low power mode are set; automatically switching a power assisting mode according to the heart rate and the duration time, and triggering a protection mechanism when the heart rate exceeds a safety boundary; multi-type patient physiological models are established in a virtual simulation environment, and optimal PID control parameters are matched; issuing the verified prescription to a rehabilitation execution mechanism for execution; heart rate records are monitored in real time during training, and data of the whole process are traced. According to the intermittent heart rate gating ankle rehabilitation robot system and the training method, people with ankle joints injured or needing rehabilitation are helped to conduct more effective and safer training.

The application provides a CAR-T cell therapy toxicity management optimization method and system, relates to the technical field of artificial intelligence, and comprises the following steps: acquiring clinical data of a blood tumor patient receiving a CAR-T cell therapy; performing missing value interpolation and standardization processing on baseline characteristic information and process monitoring data to obtain a standardized data set; obtaining a calibrated virtual patient physiological model according to a preset tumor cell kinetics model, an immune cell kinetics model and a toxicity reaction model; taking the calibrated virtual patient physiological model as an interactive environment to obtain a correlation database; constructing a deep Q network intelligent agent with a maximum reward function as a target to obtain an optimized deep Q network intelligent agent; and inputting the process monitoring data into the optimized deep Q network intelligent agent to obtain a target strategy, so that the individualized CAR-T treatment strategy balancing the therapeutic effect and toxicity can be automatically output by inputting the real-time monitoring data of the patient.