333 results about "Intensive care" patented technology

The invention discloses an accurate health risk early warning analysis system and method based on multi-modal medical data fusion. The system comprises a multi-source data acquisition module, a preprocessing module, a dynamic fusion module, a risk assessment module, an interpretability module and a dynamic early warning module. According to the method, multi-modal data are collected, feature vectors are generated through preprocessing and cross-modal fusion, a comprehensive health risk index is calculated through a double-flow model (time sequence LSTM + static GNN), abnormal association is analyzed in combination with causal reasoning, a threshold value is dynamically adjusted, grading early warning is triggered, and finally the model is optimized through reinforcement learning. According to the scheme, deep fusion and dynamic evaluation of multi-modal data are achieved, the accuracy, timeliness and interpretability of risk early warning are improved, the method is suitable for scenes such as chronic disease management and intensive care, and powerful support is provided for clinical decision making.

The invention provides an intensive care method and system based on intelligent analysis, and the method comprises the steps: collecting multiple physiological data, including electrocardiogram, blood pressure and the like, of a critical patient in real time through a sensor network; cleaning and standardizing the data; calculating the weight of each physiological data by using a dynamic weight evaluation formula; predicting an abnormal trend; calculating a risk score of the patient by using a comprehensive risk assessment model according to the weight and the abnormal trend; when the score exceeds a threshold value, the system automatically gives an early warning and sends information to the medical staff, and provides preliminary treatment suggestions at the same time. The method combines a traditional intensive care technology with a modern sensor network and a data processing technology, and aims to improve the treatment efficiency and level of critical patients.

The invention discloses an intensive care data acquisition and early warning system, and relates to the technical field of medical early warning, abnormal points in data can be effectively identified through a data processing module, an abnormal data set is generated, and the method is beneficial to screening out possible instantaneous abnormal conditions. And on the basis of the anomaly sets, corresponding monitoring time periods are identified, the relevance between the behavior state and the physiological parameters of the patient is further analyzed, influence factors are calculated, and the relevance-based analysis method effectively solves the problem of false positive anomaly which is difficult to distinguish in a traditional intensive care system. On the basis, the hierarchical response module constructs a screening model by using a convolutional neural network technology, fits and outputs a screening index, distinguishes abnormal types according to the numerical value of the index and sends out a corresponding early warning instruction, and meanwhile, ensures that dynamic adjustment and switching of the early warning instruction are more intelligent and accurate through a preset time window mechanism.

The invention relates to an intensive care cross-institution collaborative data processing method, which comprises the following steps of: defining a hierarchical data model based on the intensive care field, and modeling various types of information including vital signs, clinical diagnosis and laboratory detection results of a patient according to a time sequence; a self-adaptive standardized interface is designed, and data formats from different hospitals or devices are identified and converted; real-time dynamic tags are introduced for patient data through a tagging technology; various health data, including vital signs, medical history and treatment feedback, of a critical patient are converted into a multi-dimensional health track, and sequential analysis is used for conducting continuous modeling on the condition of the patient; a deep learning and reinforcement learning method is combined to establish a disease evolution deduction engine; the disease development is predicted according to historical data, and the influence of different intervention measures on the disease is simulated to intelligently recommend a treatment strategy; and according to the dynamic deduction result, generating a plurality of treatment schemes, and dynamically adjusting the intervention strategy according to the real-time response of the patient.

The invention discloses a pupil image recognition and disease change evaluation system based on image recognition, and relates to the technical field of monitoring analysis, and the system comprises the steps: obtaining the identity information and historical pupil data of a patient, and building a pupil dynamic database corresponding to the patient; alternately irradiating the eyes of the patient, and collecting a dynamic image sequence corresponding to a pupil light reflex area of the patient; performing spatial-temporal characteristic analysis on the dynamic image sequence, establishing a pupil diameter dynamic change curve, and generating a light reflection sensitivity index of the pupil of the patient according to the pupil diameter dynamic change curve; constructing a pupil dynamic evaluation model according to the pupil diameter dynamic change curve and the light reflex sensitivity index of the patient pupil, and outputting a pupil grade evaluation result based on the pupil dynamic evaluation model; comprehensive analysis is conducted on pupil grade evaluation results, analysis results are uploaded to an intensive care central system, and a grading early warning mechanism is triggered. The application has the effect of reducing the occurrence of illness state delay.

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

The invention relates to the technical field of medical care, in particular to an intensive care data management system based on a block chain, and the system comprises an access behavior analysis module, a data encryption module, a sensitivity monitoring module, a health abnormality monitoring module and a node monitoring module. According to the invention, intensive care data is finely managed by using the block chain technology, the medical process is optimized, the access behavior and response time difference is collected and analyzed, the operation efficiency of medical personnel and medical equipment is effectively monitored, the operation delay and the process bottleneck are identified in real time, the response speed is optimized, and the quality of medical services is improved. Through encryption processing and data classification, the confidentiality of information is guaranteed, the data security is also improved, the data leakage risk is reduced, and due to accurate monitoring and real-time abnormal monitoring of sensitivity, key medical data is particularly protected, and the service quality of patient data management is enhanced.

A medical monitoring system comprises a wearable garment incorporating multiple physiological sensors for real-time patient monitoring. The garment integrates an embedded processing unit that receives sensor data and transmits it wirelessly to a base station or remote server. A multi-modal physiological signal processing unit analyses the data to generate composite physiological indices, including metabolic rate, work of breathing, and fall risk assessments. The garment includes optional monitoring features such as transcutaneous carbon dioxide measurement, electrocardiography, pulse oximetry, blood pressure monitoring, and accelerometer - gyroscope-based motion tracking. The system provides early-warning alerts for clinical deterioration, enabling proactive therapeutic interventions. A wireless communication module ensures real-time data transmission, supporting remote monitoring and integration with hospital networks. The garment's modular design allows for non-invasive physiological assessment while maintaining patient mobility, making it suitable for critical care, ambulatory monitoring, and telemedicine applications.

The invention relates to the technical field of medical informatization, and provides an information digital management system and method based on an intensive care medicine department, and the system comprises a data collection module, a data processing module, and a visualization and alarm module. The data acquisition module obtains a patient monitoring data set, and each patient corresponds to a monitoring data sequence. The data processing module executes the following operations on each patient: determining a target monitoring data sequence and four associated monitoring periods including a first monitoring period, a second monitoring period, a third monitoring period and a fourth monitoring period group, and generating a corresponding monitoring subset; generating an early warning signal through a disease deterioration early warning model; and if the signal is abnormal, analyzing an abnormal reason and an influence range, and generating a structured disease condition comprehensive report. And the visualization and alarm module dynamically displays the state of the patient according to the report, and performs graded alarm on the critical area. The system realizes accurate early warning and monitoring through multi-cycle data fusion analysis and three-dimensional visualization.

The invention belongs to the technical field of early warning systems in Internet platforms, and discloses an Internet of Things-based early warning system and early warning method for an intensive care unit, and the system carries out the abnormal recognition of physiological parameters through a pre-trained early warning model, fuses response task features, medical worker resource states and historical scores based on a response scheduling module, and carries out the early warning of the medical worker. A responder assignment strategy is optimized, and the matching accuracy is improved; furthermore, periodic data acquisition is realized through a response closed-loop recording module, an early warning response responsibility chain containing node and causal link information is constructed, and on the basis, high-risk nodes and bottleneck regions are identified, task allocation weights and path optimization rules are dynamically adjusted, and adaptive updating of scheduling logic is realized; and early warning re-response and rescheduling based on new logic are supported, and a feedback closed loop driven by data is formed, so that the timeliness and accuracy of early warning response are effectively improved, and the risk of intervention failure caused by response lag is reduced.

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 present invention deals with a heat and moisture exchange filter with biocidal activity for medical ventilation equipment that comprises polymeric cellulose fibers impregnated with silver nanoparticles (AgNPs). The heat and moisture exchanger filter can be used in intubated patients under artificial ventilation in anesthesiology and intensive care and allows robust replacement, a fundamental utility for protection against nosocomial cross-contamination infections caused by microorganisms, such as bacteria, fungi, yeasts and viruses.The invention also deals with the method of preparing the heat and moisture exchange filter with biocidal activity and the use of silver nanoparticles.

The utility model provides a severe nursing sputum suction tube, and belongs to the technical field of sputum suction tubes. Comprising a sputum suction tube body, one end of the sputum suction tube body is an insertion end inserted into the respiratory tract of a patient, the other end of the sputum suction tube body is a connecting end connected with a negative pressure generation assembly, the insertion end of the sputum suction tube body is connected with an end head with a smooth surface, and a sputum suction hole is formed in the side wall of the insertion end of the sputum suction tube body. And a connecting head is arranged at the connecting end of the sputum suction tube body. The end head with the smooth surface is arranged at the insertion end of the sputum suction tube body, so that the phenomenon that the end face corner angle of the insertion end of the sputum suction tube body scratches the respiratory tract of a patient when the sputum suction tube body is inserted into the respiratory tract of the patient can be effectively avoided, and the sputum suction tube is safer and more reliable.

The invention provides a multi-agent medical examination recommendation system and method, and relates to the technical field of medical intellectualization. Comprising a medical examination recommendation data set building module used for extracting key medical entities and personal information of patients from electronic medical records of an intensive care medical information database, and generating enhanced patient sequence data by using the key medical entities and a preprocessing mode; the medical knowledge graph building module is used for building a medical knowledge graph according to the disease and the triple generation module based on the large language model; and the medical examination recommendation module is used for processing the enhanced patient sequence data and personal information by utilizing a plurality of intelligent agents and a medical knowledge graph to obtain a target medical examination item. In this way, structured medical examination data can be provided, and the quality of a data set is high; multiple agents are introduced, so that the flexibility and adaptability of the recommended target medical examination items are improved; and the reasonability of the recommendation result is improved by utilizing the established medical knowledge graph.

The present application relates to a kind of respiratory synchronous diaphragm nerve electric stimulator in vitro, belong to medical equipment technical field.The technical scheme of a kind of diaphragm nerve electric stimulation based on real-time breathing signal synchronization trigger is proposed for the problems of high weaning failure rate and quality of life decline caused by VIDD.The technical scheme points include: detecting inspiration negative pressure signal through nasal cavity air pressure sampling tube, control unit dynamically adjusts trigger threshold according to negative pressure signal, when negative pressure is lower than preset value and waveform slope meets the standard, trigger electric stimulation module to output biphasic pulse, pulse parameters include positive and negative alternating waveform, width 0.1ms to 1ms, frequency 10Hz to 50Hz and current intensity 5mA to 30mA, and strictly limit electric stimulation to be only output in inspiration cycle, and expiration cycle is completely blocked stimulation by hardware switch circuit.It is mainly suitable for intensive care and rehabilitation therapy scene, effectively prevent and treat VIDD, improve patient weaning success rate and quality of life.

The invention discloses a respiratory intensive care method and system, and the method comprises the steps: deploying an acoustic sensor on a respirator loop, and synchronously obtaining the ventilation parameters and optional temperature and humidity data of a respirator; preprocessing the acquired multi-modal data, and converting an acoustic signal into a time-frequency spectrogram; the preprocessed data are input into a multi-modal fusion fault recognition model, the model comprises a convolutional neural network branch used for extracting acoustic features and a recurrent neural network branch used for capturing time sequence dynamics, and the probability of a specific fault precursor is output by combining the features of all the branches through a fusion layer; and finally, performing early warning decision according to the probability. The invention also comprises a system for implementing the method. According to the invention, through deep fusion of acoustic, pneumatic and thermodynamic data, accurate prediction of fault precursor is realized, and an earlier time window is provided for clinical intervention, so that the monitoring safety is improved.

The invention relates to the technical field of monitoring restraint, and particularly discloses an intensive care restraint device and a using method thereof.The intensive care restraint device comprises an abdomen restraint strap, the abdomen restraint strap surrounds the waist of a patient for binding, the abdomen restraint strap is sleeved with a binding strap, and the two sides of the binding strap are connected with connecting straps through adjusting buckles; shoulder restraint straps are arranged outside the binding strap in a crossed manner; the device further comprises a pressure sensor, the pressure sensor monitors the extrusion pressure change in real time and is used for judging the dysphoria degree of the patient, the pressure sensor is arranged at the intersection of the shoulder restraint strap to monitor the extrusion pressure change when the patient struggles in real time, and a motor in the control assembly is combined to drive a screw transmission mechanism to drive the patient to move. When the patient moves restlessly, the restraint strap is automatically tightened to prevent struggling off or accidental extubation; after the patient is calm, the patient is gradually relaxed and constrained, complications such as blood circulation disorder and pressure sores caused by long-term excessive constraint are effectively avoided, and medical safety and patient comfort are both considered.

The invention discloses a multi-modal data-based auxiliary decision-making system suitable for a perioperative period and an intensive care period, which can provide multi-dimensional reference contents such as real-time monitoring, attribution analysis and decision-making recommendation of physiological parameters for a user, can provide auxiliary information for decision making of medical personnel, and can improve decision-making efficiency. Meanwhile, the system greatly reduces the requirements for medical knowledge and personal professional literacy of medical personnel, not only reduces the requirements for personnel ability, but also weakens the dependence on human experience, and can realize dynamic adjustment of physiological parameter calibration data, so that compared with the traditional technology, the use reliability is improved; therefore, the method is very suitable for large-scale application and popularization.

The invention relates to the technical field of intensive care, and particularly discloses intensive care patient transfer equipment which comprises a height adjusting mechanism arranged at the top of a transfer trolley and used for adjusting the transfer height according to the height of a sickbed. The transfer trolley at least has the following beneficial effects that by arranging the height adjusting mechanism, after the transfer trolley is moved to the position beside a sickbed of a patient, the structure on the transfer trolley can be adjusted to the needed height position according to the height of the sickbed, and then the patient can be conveniently transferred to the transfer trolley from the sickbed; by arranging the shifting mechanism and the pushing mechanism, when a patient is transferred from a sickbed, the shifting mechanism can be moved to the position below the body of the patient through the pushing mechanism, then the patient can be moved through the shifting mechanism, then the working intensity of medical staff can be relieved, and harm to the patient is reduced.

Provided are a method, device, and recording medium of early prediction of sepsis or septic shock through bio-data analysis on a computing device. In an embodiment, the method comprises training one or more time series deep learning models by: encoding sepsis or septic shock patient vital signs as time series-based bio-data to generate embedded time series patches; encoding associated patient medical histories utilizing a pre-trained language encoder to generate encoded patient medical history patches; combining the embedded time series patches and the patient medical history patches into an array; and randomly masking the embedded time series patches and asking the model to reconstructing the masked patches based at least in part on the associated patient medical histories; and utilizing the one or more trained time series deep learning models to predict sepsis or septic shock for a new patient.

The present invention belongs to the technical field of medical devices, and specifically is a nursing suction device for an intensive care unit, including a left bottle, a right bottle, a gas transmission mechanism, a mounting frame, and a reversing mechanism. In order to improve the suction efficiency and reduce the discomfort of patients, a single-tube delivery method is adopted, and the patient can be treated with suction therapy and expectorant therapy respectively, reducing the number of pipes inserted into the affected part, and at the same time, the steps of pulling out and plugging pipes and expectorant therapy when treating thick sputum can be optimized, shortening the treatment time while improving the suction efficiency, solving the problem that traditional suction devices cannot perform expectorant therapy and too many pipes are inserted into the affected part for suction therapy. At the same time, the adjustment method of suction and expectorant is simple, and only the switching coil magnet and the trigger judgment of the left trigger and the right trigger need to be exchanged, which helps medical staff to master the use method more quickly, improve work efficiency, and save precious time.

The invention relates to the technical field of medical data mining, and particularly discloses an elderly sepsis index model, a prediction model construction method and a prediction system.The method comprises the steps that a development queue data set and an independent verification queue data set of elderly sepsis patients at the age larger than or equal to 60 years old are determined according to an intensive care database; performing sepsis death and prognosis importance sorting on candidate inflammation-immune composite indexes of the development queue data set according to a total correlation feature selection algorithm; superposing and combining preset basic indexes, each index in the importance inflammation marker indexes and a preset importance scoring standard; comparing the scoring results of the multiple groups of joint optimization index models, and evaluating the scoring results; and determining the joint optimization index model with the optimal evaluation result as the senile sepsis index model. According to the construction method of the senile sepsis index model provided by the invention, the index model suitable for senile sepsis can be obtained based on the existing index database.

The utility model relates to the technical field of medical instruments, and discloses a respiratory training device for intensive care, which comprises a main plate, a fitting plate is clamped at the bottom of the main plate, a collecting pipe for storage is clamped at the top of the main plate, and a circulating pipe for butt joint is clamped at the top of the collecting pipe. An auxiliary piece used for sliding is clamped in the runner pipe, a sleeving ring used for preventing leakage is clamped on one side of the runner pipe, and a flexible rubber hose used for conveying is arranged on one side of the sleeving ring. Through cooperative arrangement of a main plate, an attaching plate and a collecting pipe, internal components can be better disassembled and adjusted when the device is used, a patient can conduct cleaning and disinfection treatment in time, it is avoided that saliva doped in gas emits peculiar smells in the device, the service life of the device is shortened, and up-down irregular movement of an internal ball is prevented; the accuracy in the detection process is improved, and the requirements of the public market are met.

The invention discloses a PatchTST model-based respiratory signal restoration method, which comprises the following steps of: S1, reading low-resolution sensor input data and high-resolution respiratory physiological waveform target data, and carrying out data cleaning, formatting and time alignment to form a unified data set; and S2, dividing the aligned time sequence data into window samples with fixed lengths, dividing a training set and a verification set, carrying out standardization processing on the data, and then constructing a PyTorch data loader. According to the method, the adaptation problem of the low-resolution input data and the high-resolution target waveform can be effectively solved, the target data quality can be further optimized, the breathing physiological state of a patient can be reflected more truly, and reliable data support is provided for clinical intensive care and respiratory disease diagnosis.

The utility model relates to the technical field of medical protection instruments, in particular to an adjustable anti-falling guardrail for a sickbed, which comprises a sickbed main body, bed edges on the front side and the rear side of the sickbed main body are respectively and fixedly provided with a support cross frame, and the tops of the two groups of support cross frames are respectively provided with an adjustable fence mechanism. The child patient can be protected and prevented from falling from the two sides of the sickbed, operation is convenient, meanwhile, the height of the protective net can be adjusted according to the height of a child, the child patient is more humanized, the child patient is effectively prevented from falling from the sickbed, safety is high, and other accidents are avoided.

Systems and methods for Endovascular Perfusion Augmentation for Critical Care (EPACC) are provided. The system may include a catheter having an expandable aortic blood flow regulation device disposed on the distal end of the catheter for placement within an aorta of a patient. The system may also include a catheter controller unit that causes the expandable aortic blood flow regulation device to expand and contract to restrict blood flow through the aorta. The system may also include one or more sensors for measuring physiological information indicative of blood flow through the aorta, and a non-transitory computer readable media having instructions stored thereon, wherein the instructions, when executed by a processor coupled to the one or more sensors, cause the processor to compare the measured physiological information with a target physiological range associated with blood flow through the aorta such that the catheter controller unit automatically adjusts expansion and contraction of the expandable aortic blood flow regulation device to adjust an amount of blood flow through the aorta if the measured physiological information falls outside the target physiological range.

The present invention discloses a liquid food feeding device for critical care, and relates to the technical field of medical devices. The present invention comprises an outer cylinder and an upper cover plate and a lower lining plate respectively arranged at the upper and lower positions of the outer cylinder, and also comprises a U-shaped tube and an inner cylinder arranged inside the outer cylinder, and a connecting sleeve is also arranged at the upper end position of the inner wall of the outer cylinder, and a through groove is arranged in the connecting sleeve along the vertical direction, and a connecting groove is connected and arranged on the outer cylinder on one side of the through groove, and a discharge pipe is connected and arranged on the outer wall of the outer cylinder at the connecting groove position; one end of the U-shaped tube slides from the bottom of the inner cylinder and penetrates and extends to the inside of the inner cylinder, and the other end of the U-shaped tube slides and inserts into the through groove from the lower end of the connecting sleeve, and the U-shaped tube overlaps the connecting groove by 2 / 3-3 / 4 in height. The present invention can timely control whether the liquid food flows, can detect the consistency of the liquid food, can effectively heat the liquid food, and has good overall practicality. At the same time, the device is easy to disassemble and clean, and can effectively avoid the breeding of internal bacteria.

The invention discloses a patient sign deterioration assessment processing method based on intensive care clinical data, and the method can generate a more accurate grading result based on comprehensive data through the continuous monitoring of a plurality of pieces of physiological index data and the deep analysis of historical patient data. In an analysis process of a grading result, by introducing concepts of a dynamic normal value and a dynamic standard deviation and utilizing an acute change coefficient and a long-term adaptation coefficient, a sign deterioration score can be calculated more accurately, so that changes of physiological states of patients can be captured in time, and meanwhile, by combining individual difference factors such as ages and body mass indexes of the patients, the physical sign deterioration score can be calculated more accurately. The dynamic normal value of each physiological index is calculated, so that personalized evaluation and grading are realized; the structure causal model is utilized, and the intervention strategy suitable for the current patient can be intelligently output based on the grading result by training the multiple pieces of sample intervention strategy information and the corresponding grading result, so that the scientificity and effectiveness of medical decision making are improved.

The present invention relates to a method and system for monitoring cardiac functions and quantifying objective, operator-independent indices of cardiac functions. The invention quantifies both ventricle cavity and wall dynamics. It provides a picture with a comprehensive view of the six phases of cardiac contraction, relaxation and filling (isovolumic contraction, ejection, isovolumic relaxation, rapid filling, diastasis and atrial kick). It enables to quantify the effect of respiration and mechanical ventilation on cardiac chamber loadings and thereby to optimize mechanical ventilation in the general and cardiac intensive care units. It enables to assess the phase differences between the electrical activations and mechanical contractions of the various chambers and thus to adjust and optimize the parameters of cardiac synchronization therapies of ventricle chambers and chamber walls (as cardiac resynchronization therapy).

The invention discloses an intensive care state real-time prediction method and system based on mask dynamic composition. The method directly faces the challenge that clinical time sequence data is extremely sparse, and a monitoring missing mode is creatively converted into valuable prediction features. A monitoring mode dynamic relation graph is constructed to encode the clinical cooperative monitoring intensity between the indexes; comprehensively modeling time sequence dependence in combination with a time sequence attention mechanism and multi-level local feature extraction; and finally, through adaptive multi-channel fusion, accurate and end-to-end prediction of the future multi-index clinical trajectory of the patient is realized. Compared with a traditional filling or special architecture method, the method has the advantages that knowledge hidden in clinical monitoring behaviors can be more effectively mined, more excellent prediction performance can be achieved on an intensive care state data set with extremely sparse data, and a new technical path is provided for development of an intelligent clinical early warning system.