43 results about "Icu patients" patented technology

The utility model relates to the technical field related to medical care, in particular to a nursing waistband for an ICU (intensive care unit), which comprises a waist support and a positioning mechanism, the positioning mechanism is arranged on one side of the surface of the waist support, and a bunching component is mounted on one side of the surface of the waist support. According to the nursing waistband for the ICU, through the arrangement of the positioning mechanism, when the body position of an ICU patient needs to be restrained, a display screen can be clicked to control the controller, and due to the fact that the four sets of air bags are symmetrically distributed, the appropriate air bags can be selected through the controller to be inflated according to needs, so that the air bags at the corresponding positions are bulged, and the nursing waistband for the ICU is convenient to use. When the air bags on the two sides are inflated, a patient can be restrained in a flat lying position, and when the air bag on the single side is inflated, the patient can be restrained in a side lying position, so that the situation that the patient cannot keep the same position for a long time, and illness rehabilitation is affected is avoided; at the moment, the dustproof cover can be opened for charging through the charging interface.

The invention provides an ICU patient gastrointestinal complication risk prediction method and system based on time sequence data and a neural network. The method comprises the following steps: collecting time sequence physiological monitoring, static clinical and medical event data with timestamps of a patient, and constructing a dynamic time sequence knowledge graph in real time to represent clinical state evolution; map semantic embedding features are extracted from the map and fused with time sequence dynamic features extracted from the physiological data and static risk features extracted from the static data, and the fusion process is guided by map entity association; historical data and the corresponding atlas are used for training the risk prediction model, and atlas attenuation type label propagation based on atlas association is carried out on training labels according to the atlas structure and the time sequence path during training so as to optimize parameters; and inputting real-time data of a patient to be predicted into the trained model, and outputting a risk prediction result of gastrointestinal complications in a future time window. According to the method, the dynamic knowledge graph and deep learning are fused, and the accuracy and interpretability of risk prediction are improved.

The invention relates to an ICU patient delirium risk early screening system based on AI electrocardiogram analysis. The system comprises a neural signal extraction module, a component mode determination module, a delirium type identification module, a risk trajectory prediction module and a risk alarm generation module, wherein the neural signal extraction module extracts neuromodulation related signal fragments based on an electrocardiogram signal sequence and a preset neuromodulation specificity index; a component mode determination module extracts time sequence features from the fragments and determines an abnormal component distribution mode; the delirium type identification module is used for matching potential delirium types through the feature database; the risk trajectory prediction module predicts a risk trajectory in combination with a long-short-term memory network; a risk alert generation module evaluates a risk level and generates an alert. With the adoption of the system, early and dynamic screening of delirium risks can be realized, the screening accuracy and timeliness are improved through multi-module collaborative analysis, and accurate monitoring support is provided for ICU (Intensive Care Unit) patients.

This application provides a multi-agent, multimodal collaborative method, device, equipment, and storage medium for diagnosing the condition of patients in the intensive care unit (ICU), belonging to the field of medical artificial intelligence and intelligent analysis technology for intensive care. The method includes: receiving multimodal clinical monitoring data in an ICU setting, including bedside medical images, continuous life time series, and critical care clinical text; identifying and adaptively routing the input data through a modality detection agent, distributing it to corresponding domain expert agents for pathological and physiological feature extraction; constructing a patient-centric graph structure representing the dynamic clinical condition of ICU patients through a knowledge graph agent based on the features output by each domain expert agent, this graph structure integrating structured medical knowledge of entity extraction and relational reasoning; further, inputting the constructed graph structure and original multimodal features into a collaborative agent, generating the final ICU patient condition diagnosis result (such as mortality prediction, ICU long-stay prediction, etc.) and a traceable reasoning path through graph traversal and collaborative reasoning. This application effectively breaks down data silos between different monitoring devices in the intensive care unit environment, dynamically captures cross-modal associations specific to critically ill patients, and significantly improves the accuracy of critical care clinical diagnosis and medical trust.

The invention relates to the technical field of molecular biomedicine, in particular to a method and system for predicting the outcome of an ICU patient based on bronchoalveolar lavage fluid mNGS flora in combination with machine learning. The mNGS detection technology disclosed by the invention comprises the following steps: extracting nucleic acid in pulmonary alveolar lavage fluid, constructing a library, sequencing on a machine, analyzing flora and constructing a machine learning prediction model. According to the invention, a method for accurately predicting the outcome of the ICU patient can be provided clinically.

The invention discloses an ICU patient closed-loop mode psychological intervention system and method based on physiological signal feedback, and belongs to the technical field of medical monitoring and intervention. The method comprises the following steps: acquiring a multi-modal physiological signal of an ICU patient, and generating a real-time physiological state vector; based on the physiological state vector, activating at least one intervention agent in an intervention agent cluster in parallel to generate an acting force vector; dynamically updating the state of a multi-dimensional intervention potential field according to the acting force vector; based on the state of the multi-dimensional intervention potential field, a multi-sensory intervention stimulus is synthesized and executed in real time. According to the method, the intervention decision-making system which is driven by a large number of agents following simple local rules and emerges from bottom to top is constructed, selection of an intervention scheme is converted into a physical process for seeking dynamic balance in an abstract potential field space, and therefore subtle changes of the physiological state of a patient can be accurately responded in real time; a highly personalized multi-sense organ healing microenvironment is generated, and the transformation from passive treatment to active and predictive intervention is realized.

The invention belongs to the technical field of artificial intelligence and medical health information processing, particularly relates to an ICU critical patient multi-mode risk early warning system based on deep learning, and aims to solve the problems that in intensive care, illness state prediction lags behind, multi-source data fusion is difficult, and single-index early warning precision is low. According to the system, high-frequency physiological signals, clinical observation values, inspection images and unstructured texts are collected through the data sensing layer, cross-modal embedding and comparative learning alignment are carried out through the heterogeneous fusion layer, and a shared semantic space is constructed; the time sequence reasoning layer utilizes an improved space-time diagram neural network to model a dynamic knowledge graph, and causal and time sequence dependence among clinical events is captured; and the risk decision-making layer outputs multi-dimensional risk probabilities of sepsis, respiratory failure and sudden cardiac arrest in parallel. The system supports online incremental learning and individualized time decay modeling, the timeliness, accuracy and interpretability of early warning are improved, and the method is obviously superior to a traditional method in clinical verification.

The invention discloses a shielding self-adaptive delirium monitoring system and method for an ICU (Intensive Care Unit) patient, and relates to the technical field of visual processing, and the method comprises the following steps: collecting continuous video streams and clinical metadata in a delirium monitoring process of the ICU patient; extracting a regional feature vector of the patient from the continuous video stream, wherein the region comprises a face region and a limb region; coding the clinical metadata into semantic vectors; performing interaction enhancement on the regional feature vector and the semantic vector to obtain a fusion feature vector; and outputting a delirium stage identification result according to the fusion feature vector. According to the invention, by constructing the occlusion perception adaptive fusion mechanism and dynamically adjusting the fusion weight of the multi-region features, stable monitoring in a complex ICU environment is realized, the delirium risk can be early warned, a key window is provided for clinical early intervention, and the problem that the face of a patient is seriously occluded by medical equipment, so that the face of the patient is affected is solved. The visual monitoring method is poor in robustness, and continuous and effective monitoring cannot be realized.

A wearable wireless temperature monitoring device includes a micromachined thermocouple probe fabricated on a Silicon-On-Insulator (SOI) substrate, including an electrically insulated housing and a microneedle probe. A doped silicon (Si)Zchrome (Cr)Zgold (Au) junction on the microneedle probe provides a Seebeck coefficient of approximately 447 μV / °C. The device further includes a wireless data communication transceiver positioned in the housing for continuous real-time temperature monitoring. The microneedle has a diameter of less than 100 microns and a submicron sharp tip for minimally invasive temperature measurements.

The invention relates to the technical field of medical information, and particularly discloses a method for identifying the death risk and safety index range of an ICU patient, and the method comprises the steps: obtaining vital sign data and blood gas analysis index data of the patient after entering the ICU; clustering results of the vital sign data are fused through consensus clustering fused with prognosis difference degree constraints, and patient subtypes are obtained; selecting a high-risk subtype; performing causal risk weighted two-factor interactive algorithm analysis on the high-risk subtype blood gas analysis index data to obtain a safety range of blood gas indexes; the blood gas index of the target patient after entering the ICU is obtained in real time, and the death risk of the target patient is judged. According to the invention, the typing stability is improved through ensemble learning, and the typing result is ensured to have clear clinical prognosis difference through prognosis constraint; and through a causal inference framework, hybrid bias is controlled, death risks are identified, and a key clinical index danger range is accurately defined for high-risk subtype patients.

1. Name of the designed product: patient gown (ICU). 2. Use of the designed product: the designed product is used for patient gown for ICU patients. 3. Design points of the designed product: in shape. 4. Picture or photo that best indicates the design points: perspective view. 5. The designed product is a thin product, omitting the left view; the designed product is a thin product, omitting the right view; the designed product is a thin product, omitting the top view; the designed product is a thin product, omitting the bottom view.

A method of improving glycemic control is provided. The method includes stimulating a cutaneous distribution of a subject's vagus nerve within the subject's ear with a nerve stimulating signal. The method also includes monitoring the subject's blood glucose. The method further includes administering insulin infusions to the subject.

The invention provides an ICU patient multi-dimensional vital sign real-time monitoring and data early warning system. The system is applied to a monitoring system integrating vital signs, an environment sensor and a clinical operation recorder. The system firstly calculates a real-time standard deviation and a mean value of vital sign data, and generates a dynamic weight through an exponential decay function; standardizing the environmental parameters and performing weighted summation to obtain environmental influence factors; meanwhile, the clinical operation data is subjected to weighted processing, and a logarithmic function is applied to generate contribution terms. And inputting the vital sign data after dynamic weight adjustment, the environmental factors and the operation contribution items into a mode recognition engine, and dynamically optimizing harmonic coefficients by the engine according to a mode recognition result, calculating a comprehensive risk index and generating an early warning. According to the method, through multi-source data fusion and dynamic weight adjustment, accurate risk assessment is realized, the false alarm rate is reduced, and the early warning real-time performance is improved.

The utility model provides a kind of anti-pressure sore bedpan device for ICU patient, including support, the accommodating groove recessed in the support, and be equipped in the accommodating groove two-way drive assembly and cushioning component, the cushioning component includes the support platform connected with the two-way drive assembly, the bedpan placement slot recessed in the support platform, and detachably equipped in the bedpan placement slot bedpan, the bedpan includes rigid body part, and be equipped on the rigid body part elastic part, the bionic curved surface for adhering human body hip is formed on the elastic part.The anti-pressure sore bedpan device for ICU patient in the utility model, the pressure of sacrococcygeal part and ischial tuberosity is dispersed by the elastic part on bedpan, and the unilateral pressure concentration of hip when long-term bedridden patient is in toilet is reduced by two-way drive assembly, to avoid pressure sore.

The invention discloses a critical patient brain function assessment method and system based on electroencephalogram analysis. The method comprises the steps that 1, EEG signals are collected; 2, a data buffering and windowing step; 3, a sub-window data preprocessing step; 4, a brain function prediction step; and 5, a data visualization and alarm step. The evaluation system comprises an EEG signal acquisition device, an EEG signal acquisition module, a data processing module, a brain function prediction module and a visualization module. The critical patient brain function assessment method and system based on electroencephalogram analysis have the advantages that subtle changes of the consciousness state can be captured in time clinically, and objective, continuous and real-time prediction of the brain function state of an ICU patient is achieved.

The invention discloses a real-time disease deterioration prediction system based on multi-modal data, and belongs to the technical field of auxiliary medical treatment based on deep learning. Based on a bidirectional encoder characterization model, a Transform model and a text generation model, the ICU model can be accessed to a monitor, an electronic medical record system and the like to obtain multi-modal data, including real-time high-frequency physiological signals, clinical text records, image reports and the like, and the condition deterioration probability of ICU patients in multiple different time periods in the future is predicted; and an interpretable report is generated to prompt a doctor of a basis for model evaluation. According to the invention, complex multi-modal medical data can be processed, disease change of the ICU patient can be detected in real time, early discovery, early intervention and early treatment can be realized, and the method has great potential in optimizing medical resource distribution, improving working efficiency of medical staff and reducing death rate of the ICU patient. In addition, the method is high in generalization ability, and a special system can be developed for specific departments and specific diseases, so that the prediction accuracy and the result credibility are improved.

The invention discloses an ICU (intensive care unit) medicine intelligent blending and information management system, which relates to the technical field of intelligent medical treatment and comprises a data acquisition module, a data integration module, a dose calibration module, a consumption analysis module and an inventory management module. Multi-modal physiological sign data of an ICU patient are collected in real time through the data collection module and preprocessed into standardized physiological sign data, the data integration module is connected with the electronic medical record system to extract clinical basic data and basic sign baseline data, and a unified diagnosis and treatment data set is formed through correlation and integration. The dosage calibration module calculates the adjusted individualized medication dosage and generates an individualized medication instruction on the basis of the unified diagnosis and treatment data set in combination with clinical medication specifications and physical sign fluctuation-dosage adjustment rules, so that the medication dosage is accurately matched with the individual characteristics of the patient and real-time illness state changes, medicine allocation better meets the diagnosis and treatment requirements of the ICU patient, and the diagnosis and treatment efficiency of the ICU patient is improved. And the limitation of a fixed dose scheme is avoided.

The invention discloses a sepsis patient prognosis method and system based on a multi-task Transform model, a storage medium and equipment, and belongs to the technical field of intelligent medicines.The method comprises the following steps that S1, multiple time sequence physiological data of ICU patients are extracted from an electronic health record database; s2, preprocessing the time sequence physiological data; s3, a multi-task Transform prognosis model is constructed on the basis of the preprocessed time sequence physiological data, and different backtracking windows are used for generating input sequences with different lengths according to prediction task types; the prediction task type comprises an acute task and a long-term task; s4, carrying out training on the multi-task Transform prognosis model, and carrying out training on the multi-task Transform prognosis model; and S5, predicting the shock and death risks of the current sepsis patient by using the trained multi-task Transform prognosis model. According to the method, the backtracking windows with different lengths are set for the acute task and the long-term task, so that the method better fits the time sequence characteristics of different clinical events, the prediction accuracy is improved, the method is adaptive to different clinical scene requirements, and the interpretability is high.

The invention relates to the technical field of ICU return risk prediction, in particular to an ICU return risk prediction method based on patient state perception. According to the method, a patient state time sequence matrix is constructed through multi-source heterogeneous data collection, and risk scores and a key factor set are generated based on feature extraction and integrated learning; constructing a dynamic risk association graph by adopting a two-stage association analysis method of Pearson correlation analysis and Granger causality test, and revealing a causality and a time sequence relationship among risk factors; performing topological optimization and path weight calculation on the association graph to identify a high-risk evolution path; and finally, integrating the risk score, the association map and the evolution path to generate a visual decision support report. According to the method, the limitation that only isolated risk factors can be provided in a traditional method is broken through, the dynamic association and conduction path of the risk factors can be clearly displayed, a complete basis is provided for a clinician to formulate a targeted intervention scheme, and the accuracy and safety of ICU patient transfer decision making are remarkably improved.

The invention relates to the field of intelligent interaction systems, and discloses an ICU patient communication assistance system and method based on AI eye movement tracking driving, and the system comprises the steps: collecting the eye movement trajectory data of a patient through an ICU monitoring terminal, and constructing a patient eye movement behavior feature data set; carrying out adaptive feature extraction on the eye movement behavior feature data set of the patient, and constructing a patient intention prediction model; on the basis of the constructed patient intention prediction model, online dynamic training is carried out, and the model weight and the input feature priority are adjusted; according to the patient intention prediction model updated after training, combining the ICU environment variables to carry out environment adaptability correction on the inference result, and generating corrected communication intention output; and based on the corrected communication intention output, a patient instant feedback report is generated, and auxiliary prompt suggestions are automatically proposed in combination with an established clinical communication standard library. The method has the advantage of higher recognition precision in a complex clinical environment.

This invention discloses a dynamic hemodynamic monitoring and support system for critically ill ICU patients, relating to the field of medical monitoring technology. Through a multi-parameter synchronous acquisition module, it achieves simultaneous and linked acquisition of hemodynamic parameters and coagulation function indicators. A data preprocessing module performs precise filtering, outlier removal, and missing value completion on the acquired raw data. A dynamic coupling analysis module constructs a quantitative blood flow-coagulation correlation model, which can dynamically adjust parameter warning thresholds based on the patient's individual characteristics and condition, accurately calculate coupling risk values, and determine risk types and levels. An intelligent intervention and control module formulates differentiated treatment plans based on coupling risks and achieves closed-loop intervention through a feedback correction mechanism. A display, alarm, and data storage module realizes the visualization of multi-dimensional information and full-process data retention, achieving dynamic coupling monitoring and intelligent support for hemodynamics and coagulation function.

The utility model relates to an ICU patient dress which comprises a dress body, the back of the dress body is open, an observation opening is formed in the length direction of the dress body and used for a pipeline and a lead to penetrate through, and sleeve body pieces are located on the dress body, are unfolded and can be located on arms of a patient to surround and be closed to form closed sleeve bodies. The pipeline arrangement piece is installed on the sleeve body piece, the pipeline and the lead are fixed to the pipeline arrangement piece, the back is designed to be open, a patient can put on and take off the sleeve without sitting up, limb turning during nursing operation is reduced, and the pipeline traction risk is reduced; the observation opening extends to the waist in the longitudinal direction and can be compatible with patients of different heights, it is ensured that lines such as an electrocardio lead wire and an infusion tube naturally fall when penetrating out of the opening, line winding is avoided, and medical workers are allowed to observe key signs such as the pasting state of an electrode slice and the errhysis condition of a puncture part in real time while the privacy of the patient is kept through the observation opening.

Provided is an artificial intelligence (AI) model device of estimating the survival rates of a critically ill patient, adapted to estimate short-, medium- and long-term survival rates of an intensive care unit (ICU) patient, including a monitoring module, a data processing module, an AI evaluation module, and a display module. Therefore, AI algorithmic computation is performed on the ICU patient (but not targeted at any specific disease) with an XGBoost-based AI algorithmic computation model according to the ICU patient's daily personal features, test report data, physiology data, and evaluation data to evaluate the ICU patient's 30-day, 60-day, and 90-day survival rates. The AI algorithmic computation model is effective in estimating the longer-term prognosis of a critically ill patient precisely and enabling an ICU team to allocate medical resources appropriately and communicate with the ICU patient's family members better.

An ICU patient abnormal behavior recognition method and system based on computer vision, relating to the technical field of computer vision, comprising: acquiring a continuous image sequence of a monitored area by a camera device; extracting motion parameters of a patient in the monitored area according to the continuous image sequence; according to the motion parameters, identifying corresponding behavior nodes according to a preset rule; according to a preset risk behavior logic rule, logically associating the identified behavior nodes in time sequence, matching a risk behavior causal chain representing a preset risk behavior; when the risk behavior causal chain is successfully matched, outputting an alarm information. By constructing the causal chain of the risk behavior, even if a key action cannot be directly observed due to obstruction, the complete risk event can still be inferred through other observable behavior nodes before and after it, such as sitting up and moving the limbs to the dangerous area, effectively reducing the false negative rate.

The invention belongs to the field of medical treatment, and particularly relates to a multi-mode pain assessment method and a pain monitoring system for an ICU patient. The method comprises the following steps: constructing a pain recognition model for generating a pain state of a patient according to multi-modal data; the pain recognition model comprises a feature extraction module, a feature fusion module, a personalized module and a pain evaluation module; basic information, face videos and physiological monitoring data of ICU patients meeting requirements are obtained to form multi-modal data, and label information is added according to CPOT pain assessment results; setting a loss function according to task types to obtain a large number of sample data sets; the training set, the test set and the verification set are divided into a training set, a test set and a verification set for training, testing and verifying the pain recognition module; the validated model is retained and used for pain assessment of the ICU patient. According to the invention, the defects of hysteresis and insufficient reliability of a patient pain assessment scheme adopted in the existing ICU are overcome.

The utility model relates to the technical field of medical instruments, in particular to a body position adjusting device for ICU nursing, which comprises a bed frame, supporting legs, a bed plate and a gap, a mounting plate is welded and fixed on the bed frame, a main shaft is rotatably mounted in the mounting plate, a plurality of turning plates are fixed on the main shaft, the turning plates are positioned in the gap, and the supporting legs are fixed on the bed plate. A connecting plate is fixedly welded to the turning plate and fixedly connected with the pedal through a fixing plate. When an ICU patient needs to be turned over, a nurse only needs to step on the pedal, the turning plate at the other end can be lifted under the action of the lever, the body of the patient on the bed can be turned over by a certain angle laterally, at the moment, the nurse can complete the turning over operation by pulling the body of the patient, and the mode of stepping on the pedal and pulling by hands is labor-saving and efficient; a single person can turn over a patient without action ability, the patient turns over gently in the whole process, and various detection sensing devices on the body of the patient are protected.

The invention discloses a method for predicting the risk of dysphagia after extubation of a neurosurgery ICU tracheal intubation patient, and belongs to the technical field of dysphagia risk prediction. The invention discloses a method for predicting the risk of dysphagia after extubation of a neurosurgery ICU tracheal intubation patient. The method comprises the following steps: forming an original data set; obtaining a standardized data set; outputting a risk prediction value of dysphagia of the patient after extubation; an airway swallowing collaborative function evaluation result is obtained; and outputting a final accurate risk prediction result and an intervention suggestion. According to the invention, the problems of low neurosurgery patient prediction accuracy and high missed judgment rate in the prior art are solved. According to the method, the neurosurgery specialized risk factor set is constructed, the pathogenic root of the dysphagia is accurately anchored, the prediction pertinence is improved, the dynamic time sequence fusion prediction model is constructed, multi-modal data weighted fusion is carried out, the prediction efficiency is improved, hierarchical intervention closed-loop management is realized, and medical resource configuration is optimized.

This application discloses a method for anomaly identification in ICU patients based on video surveillance, relating to the field of video surveillance technology. The method includes: collecting historical data; establishing a standard set and a habitual feature set based on the collected historical data; generating a prediction set based on the standard set and habitual feature set; collecting real-time data; comparing the real-time data with the standard set, habitual feature set, and prediction set to obtain anomaly coefficients; calculating a comprehensive risk coefficient based on the anomaly coefficients; and triggering an early warning based on the comprehensive risk coefficient. The method also includes real-time collection of several patient condition types; selecting one condition type through a two-level identification method; quantifying the patient's risk level through the calculation of the comprehensive risk coefficient; providing intuitive decision support for medical staff; and improving the accuracy of judgment through comparative analysis of the standard set, habitual feature set, and prediction set.

The utility model relates to the technical field of specimen storage tools, in particular to a bedside various specimen collection and storage box suitable for ICU (intensive care unit) patients, which comprises a base, the top of the base is of a step structure and is sequentially provided with four steps from right to left, and four storage grooves are sequentially arranged on the four steps. The containing grooves are a blood culture bottle containing groove, a blood sample test tube containing groove, a sputum culture bottle containing groove and a drainage liquid sample containing groove, supporting assemblies are installed at the corners of the side edges of the periphery of the base, and an isolation assembly is installed between every two adjacent containing grooves. According to the collection and storage box for the various specimens, the four storage grooves are used for containing the blood culture bottles, the blood specimen test tubes, the sputum culture bottles and the drainage liquid specimens respectively, standard placement and centralized storage of the various specimens are achieved, and confusion and loss of the specimens are avoided; the isolation assemblies are installed between every two adjacent storage grooves, and the sealing plates are arranged at the tops of the storage grooves, so that contact between different specimens is effectively isolated, and cross contamination is prevented.

The utility model relates to the technical field of medical equipment, and discloses an ICU patient breathing assisting device which comprises an oxygen inhalation mask, a liquid drainage assembly is arranged in the oxygen inhalation mask and comprises a blocking strip fixed to the position, close to the rear end, of the inner wall of the oxygen inhalation mask, the whole blocking strip inclines towards the bottom of the oxygen inhalation mask, and a guide groove is formed in the front side of the blocking strip. A liquid outlet hole is formed in the bottom face of the inner wall of the oxygen inhalation mask and located at the barrier strip. The barrier strip is arranged at the position, close to the rear end, of the inner wall of the oxygen inhalation mask, after a patient wears the oxygen inhalation mask, the rear end of the oxygen inhalation mask faces downwards, water drops condensed on the inner wall of the oxygen inhalation mask slide to the barrier strip along the inner wall, the water drops enter the guide groove, and due to the fact that the whole barrier strip inclines towards the bottom of the oxygen inhalation mask, the oxygen inhalation mask is prevented from falling off. The water drops on the inner wall of the oxygen inhalation mask are effectively prevented from sliding to the face of a patient, and discomfort of the patient is avoided.