319 results about "Critically ill" patented technology

The invention discloses an intelligent nursing management system and method for a critical patient, and relates to the technical field of medical health monitoring, and the method comprises the steps: setting a multi-physiological signal collection channel, synchronously collecting multi-dimensional physiological signals of the patient, carrying out the preprocessing of the collected signals, and forming a standardized multi-modal physiological data flow; the preprocessed multi-modal physiological data are input into a deep learning model based on LSTM and a multi-head attention mechanism, intelligent fusion analysis of different physiological signals is achieved in combination with a dynamic weight adaptive algorithm, and a patient individualized physiological system coupling model is established; and based on an analysis result of the deep learning model, calculating an illness state risk assessment value of the patient in real time, and outputting a specific early warning level according to a set multi-level early warning threshold. According to the invention, the multi-stage and multi-dimensional early warning threshold system is constructed, so that the condition of the patient can be monitored and early warned in all directions. The monitoring quality of critically ill patients can be remarkably improved, and the missed judgment rate and the misjudgment rate are reduced.

The invention discloses an intelligent dynamic triage system and method for emergency patients. The triage system comprises a data acquisition module used for acquiring basic information, physiological parameters and historical records of patients; the artificial intelligence analysis module generates a preliminary triage result; the Internet of Things connection module collects hospital resource data in real time; the central control processing unit is connected with each module to receive data; the dynamic grading decision-making module divides the patients into critical symptoms, severe symptoms, subcritical symptoms and common symptoms; the resource allocation module dynamically generates a resource allocation scheme according to the grading result; the diagnosis and treatment process management module tracks the whole process of the patient and updates state data; when the state of the patient changes, the system transmits data to the artificial intelligence analysis module again for evaluation, triggers the grading decision and resource allocation module, dynamically updates the triage priority and the resource allocation scheme, and achieves closed-loop feedback and continuous optimization.

The invention discloses an outpatient queuing and calling scheduling system based on an AI dynamic decision engine. The outpatient queuing and calling scheduling system comprises a multi-source data acquisition and preprocessing module, a DeepSeek intelligent prediction and scheduling engine, a dynamic priority ranking and calling module and a cross-department resource collaboration module. According to the method, the whole outpatient service process is reconstructed through AI, a closed loop of data driving, intelligent prediction, dynamic scheduling and cross-section collaboration is achieved, a traditional static queue is converted into a dynamic system capable of sensing disease severity, special crowd requirements and resource loads, the medical resource allocation efficiency is improved, the average waiting time of patients is shortened, and the medical resource allocation efficiency is improved. The rescue response speed of critical patients is improved by multiple times, meanwhile, the diagnosis period of difficult cases is shortened through automatic triggering of multidisciplinary consultation (MDT), pregnant women / disabled persons / elderly patients are automatically recognized in cooperation with the multi-modal recognition technology, dynamic priority values are given, and the priority service missed recognition rate is obviously improved.

The invention relates to the technical field of remote monitoring, in particular to a critically ill patient remote monitoring system and method, and the system comprises a life signal collection module, a physiological state analysis module, a remote monitoring response module and a remote intervention operation module. According to the method, the data analysis and real-time feedback process is optimized, the efficiency and accuracy of medical response are improved, the key physiological indexes deviating from the normal range can be quickly and accurately recognized and analyzed by comprehensively analyzing the instant vital sign data and the historical medical records of the patient, the prediction accuracy is improved, and the prediction efficiency is improved. Medical personnel are allowed to receive clear health risk warnings at critical moments, direct communication with a medical network is enhanced through a rapid grading and alarm system, a faster medical intervention response is achieved, meanwhile, a treatment plan can be remotely and dynamically adjusted according to the current physiological state of a patient, the treatment process is more accurate, and the treatment efficiency is improved. And the survival rate and the treatment quality of patients are greatly improved.

The invention discloses a critical patient acute lung injury assessment system and method based on machine learning, and relates to the field of acute lung injury assessment, and the method comprises the steps: obtaining chest multi-modal image data of a critical patient, and carrying out the standardization processing to generate a standard data set; dividing the lung area into structural blocks and extracting structural indexes, and selecting compensation missing data through cross-modal space mapping or candidate blocks; identifying a modal conflict area and a structure mutation area to construct a scoring key area; after disturbance screening effective areas are applied, a mean value or weighted fusion strategy is selected according to a consistency index; calculating a lung injury score in combination with the dynamic weight and performing iterative optimization; according to the method, the recognition accuracy of the acute lung injury key area can be improved, and the accurate quantitative evaluation of the lung injury degree is realized.

The invention discloses a risk early warning system based on enteral nutrition management of a patient, and belongs to the technical field of medical technology and biomedicine. A risk early warning system based on enteral nutrition management of a patient comprises a patient monitoring module, a score calculation module, a nutrition control module, an early warning notification module, a nutrition scheme adjustment module and a continuous observation and feedback module. The problems that in the prior art, symptoms of critical patients with the acute respiratory distress syndrome are not monitored in time, the regulation efficiency of enteral nutrition supply is low, errors are prone to occurring, and an early warning mechanism is incomplete are solved. By introducing a real-time monitoring and intelligent quantitative evaluation tool, an automatic regulation and control mechanism and multi-level early warning notification and detailed guidance, main problems existing in enteral nutrition management of the severe patient with the acute respiratory distress syndrome at present are effectively solved, and the rehabilitation process and the treatment effect of the patient are remarkably improved.

The invention relates to the technical field of gastrointestinal tracts, in particular to a gastrointestinal tract ultrasonic virtual simulation nursing teaching platform which comprises a function module and a hardware configuration module. The function module comprises an anatomy learning module, an ultrasonic simulation module, a case training module and an examination and evaluation module; the hardware configuration comprises a VR head-mounted display, an ultrasonic simulator and an operation panel. The system has the advantages of safe and risk-free training, standardized process learning, immersive scene experience and instant feedback evaluation, a high-fidelity gastrointestinal tract ultrasonic virtual simulation teaching platform for critically ill patients is constructed, a 3D virtual model is constructed by using a virtual simulation technology, knowledge is presented dynamically and visually, real situations such as ICU wards and the like are simulated through VR, and real-time feedback evaluation of the gastrointestinal tracts of the critically ill patients is realized. The learning initiative and the sense of participation are stimulated; an ICU environment is virtualized, an unlimited'trial-and-error 'practice space is provided, the equipment cost is reduced, potential safety hazards are avoided, and space-time and safety barriers are broken.

The invention discloses a critical patient physiological parameter monitoring and intelligent early warning system, which relates to the technical field of intelligent medical treatment, and is provided with a multi-modal data collection module for collecting multi-modal physiological parameter data of a critical patient through a sensor array of flexible electronic skin, and a signal alignment module for aligning the multi-modal physiological parameter data of the critical patient. Aligning signal sequences with different sampling rates in the multi-modal physiological parameter data, setting a physiological theoretical range updating module, constructing a transfer learning model according to historical medical records of a patient, updating a physiological theoretical range of a heart rate variation coefficient in real time, setting a grading early warning module, and triggering a Bayesian network composite event detection model. Generating a graded early warning signal, setting a decision support module, dynamically loading a clinical knowledge graph through a micro-service architecture, and pushing decision support information containing rescue priority labels to a monitoring terminal; and the precision and efficiency of intensive care are obviously improved.

The invention relates to the technical field of breathing assisting devices, in particular to an intelligent temperature-control humidifying breathing critical patient assisting breathing nursing device which comprises a global perception fusion central system used for constructing a high-frequency biological characteristic thermodynamic map analysis layer and generating a particulate matter deposition energy level distribution model; forming a composite feature matrix; the self-adaptive airflow modulation matrix system is used for implanting a multi-order recursive calibration engine on the basis of a composite characteristic matrix, a spanning compensation strategy is generated through vectorization processing of a clinical characteristic curve, and a viscous resistance spectrum variation value of an expiratory phase triggers a self-consistent conversion mechanism every time; a dynamic balance point of an aerosol dispersion speed and a water molecule phase change threshold value is autonomously balanced, and a nanoscale lubricating interface of a gas transmission channel is maintained in real time; and the hierarchical response execution network system is used for designing a three-phase interlocking decision tree structure based on a thermodynamic energy level envelope line output by the compensation strategy. According to the invention, a brand new clinical response form exceeding linear control is constructed.

The invention belongs to the technical field of intelligent medical treatment, and discloses an auxiliary assessment system for deep venous thrombosis occurrence risks of critical patients in plateau regions. Comprising a data acquisition module used for acquiring basic data, numerical feature data and image feature data of a patient; the index evaluation module is used for performing group division on the numerical feature data and evaluating real-time indexes; the index detection module is used for acquiring historical indexes and identifying abnormal indexes in the real-time indexes based on the historical indexes and the real-time indexes; the image analysis module is used for analyzing the image feature data, extracting blood vessel feature data and quantifying a form anomaly index; the risk assessment module is used for fusing the abnormal index and the form abnormal index, assessing a thrombus risk coefficient and obtaining a risk degree; the method can effectively improve the assessment accuracy of the occurrence risk of deep venous thrombosis, timely identify high-risk patients, and reduce the occurrence rate of deep venous thrombosis and related complications.

The invention relates to the technical field of medical treatment, in particular to an intra-abdominal pressure adjusting device and method.The device comprises an adjustable support used for supporting a skin contact assembly and allowing the position and angle of an electromagnet to be adjusted; the electromagnetic adsorption assembly is fixed on the adjustable bracket and is used for lifting the abdominal skin of the patient according to an electromagnetic adsorption effect so as to reduce the abdominal wall pressure; the skin contact assembly connected with the electromagnetic adsorption assembly is used for being attached to the skin of a patient and forming a sealed space; the control assembly is electrically connected with the electromagnetic adsorption assembly and used for collecting current pressure data and dynamically adjusting current output of the electromagnetic adsorption assembly according to a preset pressure threshold value and the current pressure data. Therefore, the problem that in the prior art, although the body position therapy can relieve the gravity compression of the abdominal wall, in a critical patient needing to be kept in a semi-reclining or upright position, the reverse effect of chest pressure conduction and diaphragm downward movement possibly aggravates the pressure load of the abdominal cavity is solved.

The invention discloses a severe patient organ failure assessment and risk stratification method based on the occurrence time point of blood flow infection, and relates to the field of severe medicine prognosis risk assessment modeling. Comprising the following steps: firstly, collecting daily sequential organ failure evaluation scores of a blood flow infection patient during an infection occurrence period, and dividing data into a training set and a verification set; importing the training set into a group trajectory model for fitting, determining the optimal grouping number of trajectory groups, and verifying the model through a verification set; then, according to a verification result, the patients are allocated to the track group with the highest membership probability for research; finally, a Cox proportional risk regression model is adopted, the correlation between different track groups and the death rate is evaluated, and decisive factors influencing prognosis are determined. According to the method, a robust framework is provided for hierarchical management of ICU blood flow infection patients, the death rate of the patients can be independently predicted, reference is provided for early intervention and formulation of personalized management strategies, and the clinical outcome of the patients is finally improved.

The invention discloses an intelligent processing method and system for physiological data of a respiratory critically ill patient, and the method comprises the steps: carrying out the STL time sequence decomposition of a volume-time curve, obtaining the residual terms of the volume-time curve under abnormal fluctuation and no abnormal fluctuation, and judging whether the current volume data is abnormal data or not; obtaining the possibility of abnormal tidal volume caused by airway leakage based on the residual value representation of the respiratory cycle corresponding to the obtained abnormal data on the airway pressure-time curve; and based on the possibility of the abnormal data and the possibility of the abnormal tidal volume caused by the airway leakage, obtaining the abnormal degree of the abnormal tidal volume data caused by the airway leakage, and correcting the abnormal data according to the abnormal degree of the abnormal tidal volume data caused by the airway leakage. According to the invention, the possibility of abnormal data can be accurately judged, and intelligent correction can be carried out according to the abnormal degree, so that tidal volume data closer to reality can be obtained.

The invention discloses an excrement character recognition and drug delivery closed-loop control method and system, and the system comprises an excrement collection and preprocessing module which is used for carrying out the automatic collection, preliminary physical and chemical character detection and sample preprocessing of the excrement of a patient; the multi-modal data analysis module is used for carrying out deep fusion analysis on the original multi-modal data output by the excrement collection and preprocessing module and carrying out diarrhea type diagnosis and severity evaluation; the drug delivery closed-loop control module is used for accurately and dynamically infusing drugs according to the diagnosis result of the multi-modal data analysis module; and the man-machine interaction and early warning module is used for monitoring and intervening the state of the system by medical staff and receiving important alarms. According to the invention, objective and continuous evaluation of excrement properties is realized, the drug dosage and scheme can be dynamically adjusted according to the properties, and the accuracy, individualization level and safety of diarrhea treatment of critical patients are significantly improved.

The invention discloses a respiratory training game device for assisting in clearance of the airway of a critically ill patient, and relates to the related field of respiratory training, and the respiratory training game device comprises an embedded sensing module which is used for collecting respiratory data of a user and establishing a respiratory data set; and the joint sensing module is used for acquiring basic data of the user under the authorization of the user. And the plan construction module is used for performing feature extraction and fusion on the respiration data set and the basic data and then constructing a respiration training plan. And the dynamic feedback module is used for acquiring breathing data of the user through the sensing device after the user performs training based on the breathing training plan, generating dynamic feedback based on the breathing data and a mapping threshold value of the breathing training plan and performing user training management according to the dynamic feedback, and the dynamic feedback comprises excitation feedback and adjustment feedback. The technical problems that in the prior art, breathing training equipment cannot achieve comprehensive and personalized data collection and analysis on users, so that the training effect difference is large, and the user compliance is insufficient are solved.

The invention discloses a method and a system for identifying muscular weakness of a critical patient, and relates to the field of muscular weakness identification. Subjective clinical muscular strength grading of the patient and instrument detection data are integrated, a muscular weakness grade mapping model of multiple parts is established, and the diagnosis accuracy is greatly improved; through a host / guest part mapping model, namely in combination with subjective feeling and objective detection data of a patient, the medullary muscular weakness symptom prone to missed diagnosis in traditional examination can be identified; on the basis of a muscle weakness index data prediction mechanism, a muscle weakness crisis risk can be warned in advance; through a treatment-evaluation-adjustment closed-loop system, dynamic optimization of a medicine scheme is realized, the crisis occurrence rate can be greatly reduced, the dosage of an immunosuppressive agent is reduced, meanwhile, the treatment effect is maintained, and the system is suitable for patient groups managed for a long time; by establishing a mapping system of a part type set and an index grade set, quantitative unified evaluation of different types such as eye muscle type / whole body type and the like is realized, and subjective judgment errors are reduced.

The invention relates to the technical field of medical monitoring, and discloses an intestinal nutrition monitoring system and method based on a critically ill patient, and the method comprises the steps: collecting a dynamic image of a gastric antrum region of the critically ill patient, and recognizing the contraction frequency and amplitude of the gastric antrum of the critically ill patient to calculate a gastric emptying index; calculating the intestinal transport efficacy of the critically ill patient by using the intestinal peristalsis data and the gastric emptying index; constructing an intestinal nutrition transmission scheme of the critically ill patient, and collecting transmission conversion data about the critically ill patient under the nutrition transmission scheme; the intestinal nutrition tolerance of the critically ill patient is analyzed, the metabolic data and the immune state of the critically ill patient after the nutrition transmission scheme are collected, and a target nutrition transmission scheme is constructed; and monitoring the intestinal nutrition status of the critically ill patient under the target nutrition transmission scheme in real time to obtain an intestinal nutrition monitoring report. According to the invention, the comprehensiveness of intestinal nutrition monitoring of critically ill patients can be improved, so that an optimal nutrition collocation scheme is constructed.

The invention relates to the technical field of biological medicine, in particular to construction and application of a candida patient death risk prediction model. The model is based on traditional SOFA scoring, core variables are screened through single-factor and multi-factor logistic analysis and LASSO regression analysis, respiratory system, blood coagulation function and circulatory system SOFA scoring and key indexes such as the maximum lactic acid value, the minimum albumin value and the maximum blood urea nitrogen (BUN) are integrated, and a prediction model is constructed through logistic regression. Through multi-center data verification, the AUC of an internal verification queue reaches 0.826, the AUC of an external verification queue is 0.813, and a calibration curve shows that the predicted death rate is highly consistent with the actual death rate. Patients can be divided into a high risk group and a low risk group through a 13-score threshold value, and the 28-day survival rate difference is significant (plt; 0.05) of the method. An efficient and convenient early risk assessment tool is provided for critical candida patients, and clinical precise intervention and prognosis improvement are assisted.

The invention belongs to the technical field of lung tissue lesion positioning, and particularly relates to a lung tissue lesion position positioning system and method based on three-dimensional reconstruction. According to the method, overall and local two-stage registration is combined, whole-lung expansion errors are corrected, local deformation is compensated, the lesion position and image alignment is achieved, the three-dimensional lesion position is displayed through the augmented reality technology, doctors are helped to visually master spatial distribution of the focus in the body of a patient, the safety and the success rate of operation or needle biopsy are improved, and the operation efficiency is improved. Based on a breathing model and an elastic registration method, the method can adapt to lung compliance differences and breathing movement conditions of different patients, is particularly suitable for severe or old patients difficult to keep stable breathing, reduces the risk of blind penetration or excessive resection through accurate positioning, and reduces tissue damage and the occurrence rate of postoperative complications.

Methods, apparatus, systems, and computer program products for providing patient predictions are provided in various embodiments. Responsive to receiving an indication of initiation of a patient interaction, a model for the patient is initiated by an assessment computing entity. The model has been trained using machine learning and the model is configured to generate a prediction for the patient. The prediction comprises at least one of an acuity score or a mortality prediction. Responsive to identifying a prediction trigger, the assessment computing entity updates the model for the patient based at least in part on medical data corresponding to the patient. The assessment computing entity generates the prediction using the updated deep learning model. The assessment computing entity provides at least a portion of the prediction such that the at least a portion of the prediction may be used to update an electronic health record corresponding to the patient and / or provided to a clinician for review.

The invention relates to the technical field of medical information, in particular to an intensive care medical patient monitoring system which comprises a dynamic threshold value calculation module used for setting an initial threshold value including a daytime mode blood pressure fluctuation tolerance value based on physical sign data and forcibly triggering an early warning signal when the blood pressure does not reach the initial threshold value; the night sensing and anti-interference module is used for generating a sleep depth parameter through a weighting algorithm according to the physical sign data and the environment data, synchronously transmitting the sleep depth parameter and the night specific physical sign data to the dynamic threshold value calculation module, and if the sleep depth parameter received by the dynamic threshold value calculation module continuously exceeds a preset threshold value, sending the sleep depth parameter to the night sensing and anti-interference module; if yes, the blood pressure fluctuation tolerance is improved to 1.2 times of the daytime mode, and organ perfusion critical parameters are obtained. By integrating the functions of multi-source data real-time processing, dynamic threshold self-adaptive adjustment, night physiological rhythm perception and interference resistance, false alarm intelligent correction and the like, the system is used for realizing complete-cycle accurate monitoring and timely early warning of critical patients.

The present invention relates to a data fusion processing method and system for an MEMS-based intelligent sensor, including the following steps: decomposing the multi-modal physiological parameter time series data stream to obtain a key vital sign feature sequence; calculating the phase coupling degree of the key vital sign feature sequence to obtain an organ-interfunction association strength matrix; performing high-dimensional feature space projection on the critically ill patient based on the organ-interfunction association strength matrix to obtain a patient pathological state feature map; performing non-linear dynamics analysis on the patient pathological state feature map to obtain a disease progression dynamic trajectory; and performing clinical decision support reasoning based on the disease progression dynamic trajectory to obtain personalized treatment recommendations, solving the technical problem that existing analysis methods are often limited to the observation of a single physiological parameter or simple statistical analysis and lack the ability to understand the disease mechanism from an overall perspective.

The invention provides a method and system for determining the volume of the end-expiratory lung and a breathing machine, and relates to the technical field of breathing detection.The method comprises the steps that the volume-impedance relation of the lung of a target object is obtained; and determining the end-expiratory lung volume of the target object based on the absolute impedance of the lung of the target object at the end-expiratory moment and the volume-impedance relationship. According to the method and system for determining the end-expiratory lung volume and the breathing machine, the end-expiratory lung volume can be determined, lung function evaluation, mechanical ventilation strategy guidance, prognosis improvement and the like are facilitated, and the practicability is high. And meanwhile, the device also has the advantages of no radiation, no invasion, low cost, convenience in operation, less time consumption, no requirement on special working environment, low matching requirement on patients, bedside operation, suitability for critical patients and the like.

The invention relates to the technical field of clinical decision support and medical data intelligent analysis, in particular to a critical patient individualized liquid resuscitation decision support system, which is characterized in that a data acquisition unit acquires pulse pressure variation waveform and multi-dimensional physiological data of each stroke volume; a volume benefit analysis channel of the dynamic decision-making unit segments a respiratory cycle, extracts pulse pressure fluctuation characteristics, outputs a per-stroke volume increase probability value through a machine learning classifier, an organ injury early warning channel calculates a blood lactic acid and creatinine clearance rate change rate, and generates an acute kidney injury risk sign in combination with inferior vena cava image verification; the execution control unit sends an infusion control or limit instruction according to the combined state; and the feedback optimization unit adjusts the weight of the model according to the actual value of blood lactic acid removal, and recalculates the probability value, so that individualized precise resuscitation, balance capacity benefit and organ protection are realized, and the treatment safety and effectiveness are improved.

The invention provides acupuncture and moxibustion equipment for critically ill patients and a using method thereof, and belongs to the technical field of medical equipment. The acupuncture and moxibustion equipment comprises a control mechanism and an acupuncture and moxibustion mechanism. The control mechanism comprises a control body, and a controller, a timer and a display screen are arranged in the control body. A needle applying mechanism and a needle feeding mechanism are arranged on an acupuncture main body of the acupuncture mechanism, a needle feeding position is arranged in the acupuncture main body, the needle applying mechanism is arranged above the needle feeding position, and the needle feeding mechanism is arranged on one side of the needle feeding position; the needle feeding mechanism is used for feeding a needle into the needle feeding position, and the needle applying mechanism is used for driving the needle in the needle feeding position to move to realize needle applying; and the needle applying mechanism is electrically connected with the controller. The equipment can be matched with a physician to automatically apply a needle, needle insertion at different angles and lifting, inserting and twisting manipulation operation can be achieved during use, needle inserting time can be automatically monitored, needle pulling can be automatically carried out, the workload of the physician can be greatly relieved, the risk that a doctor and a patient are punctured by a needle is reduced, and the medical experience of the patient is improved.

1. The name of this design product: Transfer vehicle (for critically ill patients). 2. Purpose of this design product: used for transporting critically ill patients. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.

The utility model discloses a support device of life support equipment for critical patient transfer, which comprises a support seat and support plates, the two sides of the bottom end of the support seat are fixedly connected with the support plates, one side of the top end of the support seat is fixedly connected with a fixed column, the fixed column is sleeved with a fixed sleeve, and the fixed sleeve is fixedly connected with the support seat. A hook is fixedly connected to one side of the fixing sleeve, connecting bases are fixedly connected to the top end of the supporting base, and a storage structure for assisting in storing the pipe body is arranged between the connecting bases. According to the support device of the life support equipment for transferring the critical patient, parts such as the fixing base are arranged, equipment needing to be treated is placed on the supporting base, various connecting pipes can be placed in a containing groove formed in the front end of a sliding block, the sliding block can be moved at any time due to the fact that the connecting block is embedded in the sliding groove in a sliding mode, and the life support equipment is convenient to use. The pipe connector is more flexible and convenient to use, and the problems that the number of connected pipe bodies is large, the connected pipe bodies are disordered, and storage and arrangement are not easy are solved.

The invention discloses a severe patient blood pressure trend monitoring and intervention decision-making system based on deep learning, and relates to the technical field of medical information processing, which comprises the following steps: acquiring a continuous invasive arterial pressure waveform and carrying out pulse-by-pulse segmentation, and extracting pulse-by-pulse average pressure and a multi-dimensional high-frequency waveform characterization quantity; and calculating a voltage-sharing deviation amount, a high-frequency retention amount and a steady-state collapse eigen index in combination with preorder analysis window data, inputting the data into a time sequence deep learning network to obtain a pseudo trend probability, further obtaining a corrected voltage-sharing slope and a real trend slope, respectively calculating a link checking priority and a real low-voltage intervention priority, and outputting an auxiliary intervention decision. According to the invention, link distortion and real blood pressure deterioration can be distinguished, and the accuracy of trend monitoring and intervention decision is improved.

The invention relates to a split transfer device for moving a critical patient to an examination couch, and relates to the technical field of medical equipment.The split transfer device comprises a transfer couch body and a movable bottom frame arranged at the bottom of the transfer couch body, a flexible bed sheet for bearing the patient is laid on the transfer couch body, and a top plate is arranged above the transfer couch body; the four corners of the bottom of the top plate are each provided with a suspension assembly used for hoisting the whole flexible bed sheet. A first driving mechanism used for driving the top plate to horizontally move is arranged above the top plate, and a second driving mechanism used for driving the first driving mechanism to vertically ascend and descend is arranged on one side of the first driving mechanism. According to the transfer device, a more stable critical patient transfer system is constructed by adopting the flexible bed sheet and the suspension assembly, local compression caused by a traditional hard carrier in the transfer process is avoided by adopting the flexible bed sheet, and the flexible bed sheet is linked with the telescopic bottom frame, the positioning assembly and the limiting assembly; and the whole process from hoisting, moving to placing on the examination bed is stable and orderly.

The invention relates to the technical field of medical instruments, and discloses a child critical patient nursing restraint device which comprises a supporting mechanism, the supporting mechanism comprises a supporting frame, supporting legs are fixedly connected to the outer wall of the bottom of the supporting frame, and a first hydraulic rod is fixedly connected to the outer wall of the bottom of the supporting frame; the inner wall of the end, close to the first hydraulic rod, of the supporting frame is fixedly connected with a first connecting shaft, and the adjusting mechanism comprises a second sliding frame fixedly connected with the output end of the first hydraulic rod. The deflection angle of the turnover frame is adjusted through the second motor, so that the contact area between the air bag in the turnover frame and the critical child patient is increased, and peripheral circulation dysfunction caused by long-term too tight compression or improper binding angle of local tissue is avoided.