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CN119581034A - A remote monitoring and nursing early warning system based on deep learning - Google Patents

A remote monitoring and nursing early warning system based on deep learning Download PDF

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CN119581034A
CN119581034A CN202510131316.2A CN202510131316A CN119581034A CN 119581034 A CN119581034 A CN 119581034A CN 202510131316 A CN202510131316 A CN 202510131316A CN 119581034 A CN119581034 A CN 119581034A
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周红岩
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First Hospital Jinlin University
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Abstract

本发明公开了一种基于深度学习的远程监测护理预警系统，本发明涉及远程监测预警技术领域，包括远程监测护理预警平台，所述远程监测护理预警平台通信连接有数据采集模块、特征提取模块、护理预警评价模块、预警分级响应模块以及用户交互模块，其中，各模块间电信号连接；所述数据采集模块，通过远程监测工具实时采集患者的生理数据集。该基于深度学习的远程监测护理预警系统，通过深度学习技术的应用分析和处理大量的患者生理数据，并通过学习正常的生理模式和识别异常模式，从大量的数据中挖掘出潜在的规律和模式，分析患者的健康状况变化，提高患者护理监测的准确性，更好地捕捉患者病情的细微变化，从而实现更早的干预和治疗。

The present invention discloses a remote monitoring nursing early warning system based on deep learning, which relates to the field of remote monitoring early warning technology, including a remote monitoring nursing early warning platform, wherein the remote monitoring nursing early warning platform is communicatively connected with a data acquisition module, a feature extraction module, a nursing early warning evaluation module, an early warning graded response module, and a user interaction module, wherein the modules are connected by electrical signals; the data acquisition module collects the patient's physiological data set in real time through a remote monitoring tool. The remote monitoring nursing early warning system based on deep learning analyzes and processes a large amount of patient physiological data through the application of deep learning technology, and mines potential laws and patterns from a large amount of data by learning normal physiological patterns and identifying abnormal patterns, analyzes changes in the patient's health status, improves the accuracy of patient nursing monitoring, and better captures subtle changes in the patient's condition, thereby achieving earlier intervention and treatment.

Description

Remote monitoring nursing early warning system based on deep learning
Technical Field
The invention relates to the technical field of remote monitoring and early warning, in particular to a remote monitoring, nursing and early warning system based on deep learning.
Background
Along with the continuous development of medical informatization and intelligence, the remote medical technology has become an important trend in the medical field, and deep learning is an important technology of artificial intelligence, can process and analyze a large amount of medical data, extract useful information, provide support for medical decision, and match deep learning to carry out early warning of remote monitoring nursing, can realize accurate analysis and prediction to physiological data of a patient, and once abnormality is found, early warning is immediately sent out to remind the patient and medical staff to take necessary measures so as to improve the accuracy and timeliness of early warning.
In the prior art, when physiological data of a patient are monitored remotely, due to different severity of abnormal conditions, countermeasures to be taken are different to a certain extent, so that how to evaluate the severity of the abnormal conditions of the patient and send out corresponding early warning signals so that medical staff can take appropriate countermeasures is a problem to be solved.
Based on the above-mentioned problems, it is needed to provide a remote monitoring nursing early warning system based on deep learning.
Disclosure of Invention
The remote monitoring nursing early warning system based on deep learning comprises a remote monitoring nursing early warning platform, wherein the remote monitoring nursing early warning platform is in communication connection with a data acquisition module, a feature extraction module, a nursing early warning evaluation module, an early warning grading response module and a user interaction module, and the modules are in electrical signal connection;
The data acquisition module acquires physiological data of a patient in real time through a remote monitoring tool (intelligent wearing equipment and a sensor) and pre-processes the acquired original data so as to improve the quality and usability of the data;
the feature extraction module is used for extracting feature information reflecting the physiological state and the health trend of the patient from the preprocessed data, constructing a nursing early-warning feature set and providing a key basis for subsequent early-warning evaluation;
the nursing early warning evaluation module is used for constructing a nursing early warning model by utilizing a deep learning algorithm based on characteristic information in nursing early warning characteristic sets, predicting and classifying the health state of a patient, evaluating the health state of the patient and evaluating the physiological health trend of the patient;
the early warning grading response module is used for setting different early warning grades according to the result of the nursing early warning evaluation module, matching corresponding early warning response mechanisms, and carrying out quantitative analysis on the predicted result so as to ensure the accuracy and timeliness of early warning;
The user interaction module provides a user interaction interface and displays monitoring results, early warning information and health suggestions, so that medical staff and patients can conveniently check monitoring data, early warning information and response measures.
Preferably, in the data acquisition module, the process of acquiring and preprocessing physiological data of the patient includes:
The method comprises the steps that a data acquisition module is connected with a remote monitoring tool to monitor physiological data of a patient, wherein the remote monitoring tool is intelligent wearable equipment and a sensor to acquire the physiological data of the patient, and the physiological data comprise heart rate, blood pressure, body temperature, respiratory rate, blood oxygen saturation, blood sugar, blood fat and liver and kidney function indexes;
The method comprises the steps of performing cleaning, denoising and formatting pretreatment operation on collected physiological data of a patient, removing invalid, repeated or abnormal values in the data through data cleaning to ensure the accuracy and consistency of the data, denoising the data, improving the quality of the data, identifying and removing noise components in the data, performing formatting operation on the data, and converting the data into a uniform format and standard;
And constructing a data warehouse in the remote monitoring nursing early warning platform, and storing the preprocessed physiological data of the patient in the data warehouse for subsequent analysis and use.
Preferably, in the feature extraction module, the construction process of the nursing early-warning feature set includes:
Accessing a data warehouse, extracting patient physiological data from the data warehouse according to the patient ID, carrying out characteristic analysis on the patient physiological data, and extracting characteristic information reflecting the physiological state and the health trend of the patient from the patient physiological data, wherein the physiological characteristics comprise heart rate, blood pressure, body temperature, respiratory rate and blood oxygen saturation characteristics, and the biochemical characteristics comprise blood sugar, blood fat and liver and kidney function index characteristics;
determining standard threshold values of the characteristic information according to historical data and personalized nursing standards of patients, and distinguishing abnormal characteristics from normal characteristics based on the standard threshold values;
carrying out statistical analysis on the extracted physiological characteristic data by combining the standard threshold value of the determined physiological characteristic, calculating a physiological state trend index, analyzing the physiological state change trend of the patient, analyzing the extracted biochemical characteristic data by combining the standard threshold value of the determined biochemical characteristic, calculating a biochemical state evaluation index, and evaluating whether the biochemical index of the patient is in a normal range or not;
and integrating the extracted characteristic information with a set standard threshold value to construct a nursing early-warning characteristic set, wherein the nursing early-warning characteristic set comprises all the characteristics which have important influence on early-warning evaluation and the corresponding standard threshold value.
Preferably, the physiological characteristic standard threshold range is specifically:
the normal range of heart rate is that the resting heart rate of an adult is 60-100 times/min, the normal range of blood pressure is that the systolic pressure (high pressure) is 90-139mmHg, the diastolic pressure (low pressure) is 60-89mmHg, the normal temperature of body temperature is 36.3-37.2 ℃, the normal range of respiratory rate is that the normal adult is 12-20 times/min in a resting state, and the normal range of blood oxygen saturation is 95-100%;
the biochemical characteristic standard threshold range is specifically as follows:
The normal blood sugar range is 3.9-6.1mmol/L, the blood sugar after meal is 6.7-9.4mmol/L for 1 hour, the blood sugar after meal is less than 7.8mmol/L for 2 hours, the normal blood fat range is that the normal total cholesterol range is less than 5.2mmol/L, the normal triglyceride range is less than 1.7mmol/L, for people at risk of cardiovascular diseases, the low-density lipoprotein cholesterol is controlled below 2.6mmol/L, the proper range of normal people is less than 3.4mmol/L, the high-density lipoprotein cholesterol is greater than 1.0mmol/L for men, and the female is greater than 1.3mmol/L;
the liver and kidney function index is in the normal range of 0-40U/L for liver function, 0-40U/L for glutamic pyruvic transaminase (ALT), 3.4-17.1 mu mol/L for Total Bilirubin (TBIL), 53-106 mu mol/L for male, 44-97 mu mol/L for female, and 2.9-7.2 mmol/L for urea nitrogen (BUN).
Preferably, the formula for calculating the physiological state trend index is:
;
Wherein, A physiological state trend index for evaluating the overall physiological state trend of the patient,For the i-th actual measurement of a physiological characteristic, representing the actual measurement of each physiological characteristic,Respectively corresponding to heart rate, blood pressure, body temperature, respiratory frequency and blood oxygen saturation,For the standard threshold of the ith physiological characteristic, representing the corresponding standard threshold of the physiological characteristic,Standard thresholds corresponding to heart rate, blood pressure, body temperature, respiratory rate and blood oxygen saturation respectively,Is a natural logarithmic base, is used in an exponential function, enhances the influence of a feature deviation threshold,The range of the values is as follows;
The calculation formula of the biochemical state evaluation index is as follows:
;
Wherein, For the biochemical state evaluation index for evaluating whether the biochemical index of the patient is in a normal range,The actual measurement value of the j-th biochemical feature represents the actual measurement value of each biochemical feature,Respectively corresponding to blood sugar, blood fat and liver and kidney function indexes,Is the standard threshold of the j-th biochemical characteristic, represents the corresponding standard threshold of the biochemical characteristic,Respectively corresponding to standard threshold values of blood sugar, blood fat and liver and kidney function indexes,The range of the values is as follows。
Preferably, in the care early warning evaluation module, the evaluation process of the physiological health trend of the patient includes:
the method comprises the steps of integrating feature information in a nursing early-warning feature set, dividing the nursing early-warning feature set into a training set and a testing set, wherein the training set is used for training and learning a model, the testing set is used for evaluating the performance of the model, and designing a convolutional neural network structure according to the characteristics of the nursing early-warning feature set;
Constructing a nursing early-warning model by combining training set data with a convolutional neural network, learning and identifying a patient health state mode, adjusting parameters of the model through forward propagation and backward propagation algorithms to minimize a loss function, evaluating performance of the model by using test set data, and adjusting parameters and structure of the model according to a verification result to improve generalization capability of the model, wherein in the nursing early-warning model, an input layer receives a physiological data sequence, and an output layer outputs a health state prediction result of the patient;
And (3) evaluating the health state of the patient by using a nursing early warning model in combination with the physiological data of the patient, calculating an early warning evaluation coefficient by combining the physiological state trend index and the biochemical state evaluation index, and comprehensively evaluating the physiological health state of the patient.
Preferably, the calculation formula of the early warning evaluation coefficient is as follows:
;
Wherein, For early warning the evaluation coefficient, is used for comprehensively evaluating the physiological health state of the patient,The weighting coefficients of the physiological state trend indices,The weighting coefficients of the index are evaluated for the biochemical state,AndFor balancingAndFor a pair ofIs determined according to the model training result and expert opinion,A physiological state trend index, quantifying the physiological state of the patient,For the biochemical state assessment index, quantifying the biochemical index state of the patient,Is a threshold parameter for adjustingIs set according to the actual clinical requirement,The value of (2) is between 0 and 1.
Preferably, in the early warning grading response module, the setting of the early warning grade and the matching process of the early warning response mechanism include:
receiving output results from a nursing early warning evaluation module, wherein the output results comprise the health state prediction and early warning evaluation coefficients of a patient and related physiological characteristic and biochemical characteristic data;
Setting different early warning grades according to the result of the nursing early warning evaluation module, and dividing the early warning grades into a low early warning grade, a medium early warning grade, a high early warning grade and an emergency early warning grade by combining the early warning evaluation coefficients, wherein each early warning grade corresponds to different risk degrees and emergency degrees;
The historical data is analyzed to match corresponding early warning thresholds for each early warning level, and corresponding response mechanisms are designed for each early warning level, including informing medical personnel, starting an emergency treatment process, and scheduling further examination or treatment.
Preferably, the early warning levels correspond to the early warning thresholds, and specifically satisfy the following relationship:
Low early warning level: The physiological and biochemical states of the patient are close to normal, the risk is low, and the routine monitoring and periodic evaluation are not needed to be interfered immediately;
medium pre-warning grade: The physiological or biochemical state of the patient is slightly abnormal, attention is required, the monitoring frequency is increased, and further diagnostic tests are considered to be carried out;
High early warning level: the physiological or biochemical state of the patient is moderately abnormal, possibly requiring medical intervention, immediately evaluating the condition of the patient, and preparing for medical intervention;
Emergency alert level: The method indicates that the physiological state of the patient is seriously abnormal, emergency intervention is needed, the physiological or biochemical state of the patient is seriously abnormal, emergency medical intervention is needed, an emergency treatment process is immediately started, and medical staff is informed to carry out emergency treatment;
Wherein, In order to pre-warn the evaluation coefficient,A lower threshold corresponding to the low early warning level and an upper threshold corresponding to the medium early warning level,For the lower threshold corresponding to the middle early warning level and the upper threshold corresponding to the high early warning level,A lower threshold corresponding to the high early warning level and an upper threshold corresponding to the emergency early warning level,,,。
Preferably, the user interaction module specifically includes:
according to the requirements of a remote monitoring nursing early warning system, designing a user interaction interface comprising a monitoring data display area, an early warning information prompt area and a health suggestion area;
The user interaction module receives real-time data from the remote monitoring nursing early warning platform, including physiological characteristics and biochemical characteristic monitoring results of the patient, and updates the physiological data of the patient to the user interaction interface in real time;
when the early warning condition is triggered, the early warning information is received by the user interaction module, the early warning information is displayed in an early warning information prompt area on the user interaction interface, and meanwhile, a flicker prompt is sent, and a user checks detailed early warning details including early warning level, triggering condition and suggested response measures by clicking the early warning information;
According to the monitoring data and the early warning level of the patient, personalized health advice and intervention measures are provided, the patient is helped to take appropriate actions to improve the health condition, and health advice is displayed in a health advice area on the interface.
The invention provides a remote monitoring nursing early warning system based on deep learning. The beneficial effects are as follows:
1. According to the deep learning-based remote monitoring nursing early warning system, a large amount of patient physiological data are analyzed and processed through application of a deep learning technology, potential rules and modes are mined from a large amount of data through learning of normal physiological modes and recognition of abnormal modes, health condition changes of patients are analyzed, accuracy of patient nursing monitoring is improved, fine changes of patient conditions are captured better, and accordingly early intervention and treatment are achieved.
2. According to the remote monitoring nursing early warning system based on deep learning, monitoring data are received and processed in real time, and once abnormality is found, an early warning mechanism is triggered immediately, so that medical staff can respond rapidly, risks caused by delayed disease finding are reduced greatly, and timely and effective nursing service is provided for patients.
Drawings
FIG. 1 is a block diagram of a deep learning-based remote monitoring care early warning system of the present invention;
FIG. 2 is a flow chart of the construction of the nursing early warning feature set of the present invention;
FIG. 3 is a flow chart of the evaluation of the physiological health trend of a patient according to the present invention;
FIG. 4 is a flow chart of the pre-warning level setting and pre-warning response mechanism matching of the present invention.
Detailed Description
The invention will be described in further detail with reference to the drawings and the detailed description. The embodiments of the invention have been presented for purposes of illustration and description, and are not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
The invention provides a deep learning-based remote monitoring nursing early warning system, which is provided with a remote monitoring nursing early warning platform, wherein the remote monitoring nursing early warning platform is in communication connection with a data acquisition module, a feature extraction module, a nursing early warning evaluation module, an early warning grading response module and a user interaction module, and the modules are in electrical signal connection;
The system comprises a data acquisition module, a data storage platform, a data warehouse and a data storage platform, wherein the data acquisition module acquires physiological data of a patient in real time through a remote monitoring tool (intelligent wearing equipment and a sensor), performs preprocessing on the acquired original data to improve the quality and usability of the data, establishes connection between the data acquisition module and the remote monitoring tool, monitors the physiological data of the patient, wherein the remote monitoring tool is the intelligent wearing equipment and the sensor to acquire physiological data of the patient, comprises heart rate, blood pressure, body temperature, respiratory rate, blood oxygen saturation, blood sugar, blood fat and liver and kidney function indexes, performs preprocessing operations of cleaning, denoising and formatting on the acquired physiological data of the patient, removes invalid, repeated or abnormal values in the data through data cleaning to ensure the accuracy and consistency of the data, performs denoising processing on the data to improve the quality of the data, recognizes and removes noise components in the data, performs formatting operation on the data, converts the data into a uniform format and standard, enables the data to be easier to process and analyze, comprises converting the data into a specific format (such as CSV and JSON) and performing standardization processing on the data to ensure that the data has different conditions and consistency in a data warehouse and performs subsequent pre-alarm processing on the data warehouse and is stored in the data warehouse after the data warehouse and is subjected to the data analysis and the physiological processing;
The system comprises a feature extraction module, a data warehouse, a data storage, a data analysis module and a data analysis module, wherein the feature information reflecting the physiological state and the health trend of a patient is extracted from the preprocessed data, a nursing early warning feature set is constructed, key basis is provided for subsequent early warning evaluation, the data warehouse is accessed, the physiological data of the patient is extracted from the data warehouse according to the patient ID, feature analysis is carried out on the physiological data of the patient, feature information reflecting the physiological state and the health trend of the patient is extracted from the physiological data, the feature information reflecting the physiological state and the health trend of the patient are respectively physiological features and biochemical features, the physiological features comprise heart rate, blood pressure, body temperature, respiratory rate and blood oxygen saturation level, the biochemical features comprise blood sugar, blood fat and liver and kidney function index features, standard threshold values of all feature information are determined according to historical data and personalized nursing standards of the patient, standard threshold values of all feature information are distinguished, statistical analysis is carried out on the extracted physiological feature data according to the standard threshold values of the standard threshold values, the physiological state trend of the patient is analyzed, whether the physiological state of the patient is in a normal range is evaluated, the biochemical feature information of the patient is integrated with the set of the standard threshold values, and the important early warning feature set is influenced by the corresponding to all the feature sets;
Further, the physiological characteristic standard threshold range is specifically:
the normal range of heart rate is that the resting heart rate of an adult is 60-100 times/min, the normal range of blood pressure is that the systolic pressure (high pressure) is 90-139mmHg, the diastolic pressure (low pressure) is 60-89mmHg, the normal temperature of body temperature is 36.3-37.2 ℃, the normal range of respiratory rate is that the normal adult is 12-20 times/min in a resting state, and the normal range of blood oxygen saturation is 95-100%;
the biochemical characteristic standard threshold range is specifically as follows:
The normal blood sugar range is 3.9-6.1mmol/L, the blood sugar after meal is 6.7-9.4mmol/L for 1 hour, the blood sugar after meal is less than 7.8mmol/L for 2 hours, the normal blood fat range is that the normal total cholesterol range is less than 5.2mmol/L, the normal triglyceride range is less than 1.7mmol/L, for people at risk of cardiovascular diseases, the low-density lipoprotein cholesterol is controlled below 2.6mmol/L, the proper range of normal people is less than 3.4mmol/L, the high-density lipoprotein cholesterol is greater than 1.0mmol/L for men, and the female is greater than 1.3mmol/L;
the normal range of liver and kidney function indexes is that for liver function, the normal range of glutamic pyruvic transaminase (ALT) is 0-40U/L, the normal range of glutamic pyruvic transaminase (AST) is 0-40U/L, the normal range of Total Bilirubin (TBIL) is 3.4-17.1 mu mol/L, for kidney function, the normal range of blood creatinine (Scr) is 53-106 mu mol/L for men, 44-97 mu mol/L for women and 2.9-7.2mmol/L for urea nitrogen (BUN);
further, the calculation formula of the physiological state trend index is as follows:
;
Wherein, A physiological state trend index for evaluating the overall physiological state trend of the patient,For the i-th actual measurement of a physiological characteristic, representing the actual measurement of each physiological characteristic,Respectively corresponding to heart rate, blood pressure, body temperature, respiratory frequency and blood oxygen saturation,For the standard threshold of the ith physiological characteristic, representing the corresponding standard threshold of the physiological characteristic,Standard thresholds corresponding to heart rate, blood pressure, body temperature, respiratory rate and blood oxygen saturation respectively,Is a natural logarithmic base, is used in an exponential function, enhances the influence of a feature deviation threshold,The range of the values is as followsWhen all the characteristic values are within the standard threshold value range,A value close to 0 indicates that the patient's physiological state is stable, and when any one of the characteristic values deviates from its standard threshold,An increase, representing an abnormality in the patient's physiological status, an exponential portionAmplifying the influence of the characteristic deviation threshold value so thatIs more sensitive to the comprehensive influence of a plurality of indexes;
the calculation formula of the biochemical state evaluation index is as follows:
;
Wherein, For the biochemical state evaluation index for evaluating whether the biochemical index of the patient is in a normal range,The actual measurement value of the j-th biochemical feature represents the actual measurement value of each biochemical feature,Respectively corresponding to blood sugar, blood fat and liver and kidney function indexes,Is the standard threshold of the j-th biochemical characteristic, represents the corresponding standard threshold of the biochemical characteristic,Respectively corresponding to standard threshold values of blood sugar, blood fat and liver and kidney function indexes,The range of the values is as followsWhen all the characteristic values are within the standard threshold value range,A value close to 0 indicates that the biochemical state of the patient is normal, and when any one of the characteristic values deviates from the standard threshold value,An increase, representing the trend abnormality of the biochemical state of the patient, the square term in the formulaEnhancing the influence of the characteristic deviation threshold value so thatIs more sensitive to the comprehensive influence of a plurality of indexes;
the nursing early warning evaluation module is used for constructing a nursing early warning model by utilizing a deep learning algorithm based on the characteristic information in the nursing early warning characteristic set, predicting and classifying the health state of the patient, evaluating the health state of the patient and evaluating the physiological health trend of the patient;
The early warning grading response module is used for setting different early warning grades according to the result of the nursing early warning evaluation module, matching corresponding early warning response mechanisms, and carrying out quantitative analysis on the predicted result so as to ensure the accuracy and timeliness of early warning;
And the user interaction module is used for providing a user interaction interface and displaying monitoring results, early warning information and health suggestions, so that medical staff and patients can conveniently check the monitoring data, the early warning information and response measures.
In a second embodiment, referring to fig. 3 and 4, in the care early warning evaluation module, the evaluation process of the physiological health trend of the patient includes:
The method comprises the steps of integrating characteristic information in a nursing early-warning characteristic set, dividing the nursing early-warning characteristic set into a training set and a testing set, wherein the training set is used for training and learning of a model, the testing set is used for evaluating the performance of the model, a convolutional neural network structure is designed according to the characteristics of the nursing early-warning characteristic set, a nursing early-warning model is built by combining training set data with the convolutional neural network, a patient health state mode is learned and identified, parameters of the model are adjusted through forward propagation and backward propagation algorithms, a loss function is minimized, the performance of the model is evaluated by using testing set data, the parameters and the structure of the model are adjusted according to a verification result, so that the generalization capability of the model is improved, wherein in the nursing early-warning model, an input layer receives physiological data sequences, an output layer outputs a health state prediction result of the patient, the health state of the patient is evaluated by using the nursing early-warning model and combining physiological state trend indexes and biochemical state evaluation indexes, and an evaluation coefficient is calculated, and the physiological health state of the patient is comprehensively evaluated;
Further, the calculation formula of the early warning evaluation coefficient is as follows:
;
Wherein, For early warning the evaluation coefficient, is used for comprehensively evaluating the physiological health state of the patient,The weighting coefficients of the physiological state trend indices,The weighting coefficients of the index are evaluated for the biochemical state,AndFor balancingAndFor a pair ofIs determined according to the model training result and expert opinion,A physiological state trend index, quantifying the physiological state of the patient,For the biochemical state assessment index, quantifying the biochemical index state of the patient,Is a threshold parameter for adjustingIs set according to the actual clinical requirement,The value of (2) ranges from 0 to 1 whenAndWhen the two are in the normal range,Close to 1 indicates that the physiological state of the patient is good whenOr (b)When the deviation from the normal range is made,A decrease, indicative of a trend in the patient's physiological state toward abnormality,The closer to 0, the worse the physiological health status of the patient, requiring more attention and intervention,The closer the value is to 1, the more stable the physiological state of the patient,The lower the value, the more unstable the physiological state of the patient and the higher the risk;
In the early warning grading response module, the setting of the early warning grade and the matching process of the early warning response mechanism comprise:
Receiving output results from a nursing early warning evaluation module, wherein the output results comprise health state prediction and early warning evaluation coefficients of a patient and related physiological characteristic and biochemical characteristic data, setting different early warning grades according to the results of the nursing early warning evaluation module, dividing the early warning grades into a low early warning grade, a medium early warning grade, a high early warning grade and an emergency early warning grade by combining the early warning evaluation coefficients, wherein each early warning grade corresponds to different risk degrees and emergency degrees, analyzing historical data to match corresponding early warning thresholds for each early warning grade, and designing corresponding response mechanisms for each early warning grade, wherein the response mechanisms comprise informing medical staff, starting an emergency treatment process, and arranging further examination or treatment;
further, the plurality of early warning levels correspond to a plurality of early warning thresholds, and specifically satisfy the following relationship:
Low early warning level: The physiological and biochemical states of the patient are close to normal, the risk is low, and the routine monitoring and periodic evaluation are not needed to be interfered immediately;
medium pre-warning grade: The physiological or biochemical state of the patient is slightly abnormal, attention is required, the monitoring frequency is increased, and further diagnostic tests are considered to be carried out;
High early warning level: the physiological or biochemical state of the patient is moderately abnormal, possibly requiring medical intervention, immediately evaluating the condition of the patient, and preparing for medical intervention;
Emergency alert level: The method indicates that the physiological state of the patient is seriously abnormal, emergency intervention is needed, the physiological or biochemical state of the patient is seriously abnormal, emergency medical intervention is needed, an emergency treatment process is immediately started, and medical staff is informed to carry out emergency treatment;
Wherein, In order to pre-warn the evaluation coefficient,A lower threshold corresponding to the low early warning level and an upper threshold corresponding to the medium early warning level,For the lower threshold corresponding to the middle early warning level and the upper threshold corresponding to the high early warning level,A lower threshold corresponding to the high early warning level and an upper threshold corresponding to the emergency early warning level,,,;
The user interaction module specifically comprises:
According to the requirements of a remote monitoring nursing early warning system, a user interaction interface comprising a monitoring data display area, an early warning information prompt area and a health suggestion area is designed, a user interaction module receives real-time data from a remote monitoring nursing early warning platform, the real-time data comprise physiological characteristics and biochemical characteristic monitoring results of a patient, the physiological data of the patient are updated to the user interaction interface in real time, when an early warning condition is triggered, the early warning information is received by the user interaction module, the early warning information prompt area on the user interaction interface is displayed, and meanwhile, a flicker prompt is sent out, a user checks detailed early warning details, the early warning details comprise early warning grades, triggering conditions and suggested response measures, personalized health suggestions and intervention measures are provided according to the monitoring data and the early warning grades of the patient, the patient is helped to take proper actions to improve the health condition, and the health suggestions are displayed in the health suggestion area on the interface.
It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art and which are included in the embodiments of the present invention without the inventive step, are intended to be within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.

Claims (10)

1.一种基于深度学习的远程监测护理预警系统，包括远程监测护理预警平台，其特征在于，所述远程监测护理预警平台通信连接有数据采集模块、特征提取模块、护理预警评价模块、预警分级响应模块以及用户交互模块，其中，各模块间电信号连接；1. A remote monitoring and nursing early warning system based on deep learning, comprising a remote monitoring and nursing early warning platform, characterized in that the remote monitoring and nursing early warning platform is communicatively connected with a data acquisition module, a feature extraction module, a nursing early warning evaluation module, an early warning graded response module and a user interaction module, wherein the modules are connected by electrical signals; 所述数据采集模块，通过远程监测工具实时采集患者的生理数据，并对采集到的原始数据进行预处理；The data acquisition module collects the patient's physiological data in real time through a remote monitoring tool and pre-processes the collected raw data; 所述特征提取模块，从预处理后的数据中提取反映患者生理状态和健康趋势的特征信息，构建护理预警特征集；The feature extraction module extracts feature information reflecting the patient's physiological state and health trend from the preprocessed data to construct a nursing warning feature set; 所述护理预警评价模块，基于护理预警特征集中的特征信息，利用深度学习算法构建护理预警模型，预测和分类患者健康状态，并对患者的健康状态进行评价，评估患者的生理健康趋势；The nursing warning evaluation module uses a deep learning algorithm to construct a nursing warning model based on the feature information in the nursing warning feature set, predicts and classifies the patient's health status, evaluates the patient's health status, and assesses the patient's physiological health trend; 所述预警分级响应模块，根据护理预警评价模块的结果，设定不同的预警等级，并匹配相应的预警响应机制；The early warning graded response module sets different early warning levels according to the results of the nursing early warning evaluation module, and matches the corresponding early warning response mechanism; 所述用户交互模块，提供用户交互界面，展示监测结果、预警信息和健康建议。The user interaction module provides a user interaction interface to display monitoring results, warning information and health suggestions. 2.根据权利要求1所述的一种基于深度学习的远程监测护理预警系统，其特征在于：所述数据采集模块中，患者生理数据的采集及预处理过程包括：2. A remote monitoring and nursing early warning system based on deep learning according to claim 1, characterized in that: in the data acquisition module, the acquisition and preprocessing process of the patient's physiological data includes: 将数据采集模块与远程监测工具建立连接，监测患者的生理数据，其中，远程监测工具为智能穿戴设备和传感器，以获取患者的生理数据，包括心率、血压、体温、呼吸频率、血氧饱和度、血糖、血脂以及肝肾功能指标；Establish a connection between the data acquisition module and the remote monitoring tool to monitor the patient's physiological data, wherein the remote monitoring tool is a smart wearable device and a sensor to obtain the patient's physiological data, including heart rate, blood pressure, body temperature, respiratory rate, blood oxygen saturation, blood sugar, blood lipids, and liver and kidney function indicators; 对采集的患者生理数据进行清洗、去噪、格式化的预处理操作，通过数据清洗去除数据中的无效、重复或异常值，并对数据进行去噪处理，对数据进行格式化操作，将数据转换为统一的格式和标准；Perform pre-processing operations such as cleaning, denoising, and formatting on the collected patient physiological data. Remove invalid, duplicate, or abnormal values in the data through data cleaning, denoise the data, format the data, and convert the data into a unified format and standard; 在远程监测护理预警平台中构建数据仓库，并将经过预处理后的患者生理数据存储在数据仓库中。A data warehouse is constructed in the remote monitoring, nursing and early warning platform, and the pre-processed patient physiological data is stored in the data warehouse. 3.根据权利要求2所述的一种基于深度学习的远程监测护理预警系统，其特征在于：所述特征提取模块中，护理预警特征集的构建过程包括：3. A remote monitoring nursing warning system based on deep learning according to claim 2, characterized in that: in the feature extraction module, the construction process of the nursing warning feature set includes: 访问数据仓库，根据患者ID从数据仓库提取患者生理数据，并对患者生理数据进行特征分析，从中提取反映患者生理状态和健康趋势的特征信息，分别为生理特征和生化特征，其中，生理特征包括心率、血压、体温、呼吸频率以及血氧饱和度特征，生化特征包括血糖、血脂以及肝肾功能指标特征；Access the data warehouse, extract the patient's physiological data from the data warehouse according to the patient ID, and perform feature analysis on the patient's physiological data to extract feature information reflecting the patient's physiological state and health trend, which are physiological features and biochemical features. The physiological features include heart rate, blood pressure, body temperature, respiratory rate, and blood oxygen saturation features, and the biochemical features include blood sugar, blood lipids, and liver and kidney function index features; 根据历史数据和患者的个性化护理标准，确定各特征信息的标准阈值，基于标准阈值区分异常特征和正常特征；Determine the standard threshold of each feature information based on historical data and the patient's personalized care standards, and distinguish abnormal features from normal features based on the standard threshold; 结合确定的生理特征的标准阈值，对提取的生理特征数据进行统计分析，计算生理状态趋向指数，分析患者的生理状态变化趋势，并结合确定的生化特征的标准阈值，分析提取的生化特征数据，计算生化状态评估指数，评估患者的生化指标是否处于正常范围；Combined with the determined standard threshold of physiological characteristics, statistical analysis is performed on the extracted physiological characteristic data, the physiological state trend index is calculated, and the trend of changes in the patient's physiological state is analyzed; combined with the determined standard threshold of biochemical characteristics, the extracted biochemical characteristic data is analyzed, the biochemical state evaluation index is calculated, and whether the patient's biochemical indicators are within the normal range is evaluated; 将提取出的特征信息和设定的标准阈值进行整合，构建护理预警特征集。The extracted feature information and the set standard threshold are integrated to construct a nursing warning feature set. 4.根据权利要求3所述的一种基于深度学习的远程监测护理预警系统，其特征在于：所述生理特征标准阈值范围，具体为：4. A remote monitoring and nursing early warning system based on deep learning according to claim 3, characterized in that: the standard threshold range of the physiological characteristics is specifically: 心率正常范围：成年人静息心率为60-100次/分钟；血压正常范围：收缩压90-139mmHg，舒张压60-89mmHg；体温正常温度：36.3℃-37.2℃；呼吸频率正常范围：正常成人安静状态下为12-20次/分钟；血氧饱和度正常范围：95%-100%；Normal range of heart rate: resting heart rate for adults is 60-100 beats/minute; normal range of blood pressure: systolic pressure 90-139 mmHg, diastolic pressure 60-89 mmHg; normal body temperature: 36.3℃-37.2℃; normal range of respiratory rate: 12-20 times/minute for normal adults in a resting state; normal range of blood oxygen saturation: 95%-100%; 所述生化特征标准阈值范围，具体为：The biochemical characteristic standard threshold range is specifically: 血糖正常范围：空腹血糖正常范围是3.9-6.1mmol/L，餐后1小时血糖为6.7-9.4mmol/L，餐后2小时血糖小于7.8mmol/L；血脂正常范围：总胆固醇正常范围为小于5.2mmol/L，甘油三酯正常范围小于1.7mmol/L，对于有心血管疾病风险的人群，低密度脂蛋白胆固醇控制在2.6mmol/L以下，正常人适宜范围小于3.4mmol/L，高密度脂蛋白胆固醇男性大于1.0mmol/L，女性大于1.3mmol/L；Normal range of blood sugar: The normal range of fasting blood sugar is 3.9-6.1mmol/L, the blood sugar 1 hour after a meal is 6.7-9.4mmol/L, and the blood sugar 2 hours after a meal is less than 7.8mmol/L; Normal range of blood lipids: The normal range of total cholesterol is less than 5.2mmol/L, and the normal range of triglycerides is less than 1.7mmol/L. For people at risk of cardiovascular disease, low-density lipoprotein cholesterol is controlled below 2.6mmol/L, and the appropriate range for normal people is less than 3.4mmol/L. High-density lipoprotein cholesterol is greater than 1.0mmol/L for men and greater than 1.3mmol/L for women; 肝肾功能指标正常范围：对于肝功能，谷丙转氨酶正常范围是0-40U/L，谷草转氨酶正常范围是0-40U/L，总胆红素正常范围是3.4-17.1μmol/L，对于肾功能，血肌酐：男性正常范围是53-106μmol/L，女性是44-97μmol/L，尿素氮正常范围是2.9-7.2mmol/L。Normal range of liver and kidney function indicators: For liver function, the normal range of alanine aminotransferase is 0-40U/L, the normal range of aspartate aminotransferase is 0-40U/L, the normal range of total bilirubin is 3.4-17.1μmol/L, for kidney function, blood creatinine: the normal range for men is 53-106μmol/L, for women is 44-97μmol/L, the normal range of urea nitrogen is 2.9-7.2mmol/L. 5.根据权利要求4所述的一种基于深度学习的远程监测护理预警系统，其特征在于：所述生理状态趋向指数的计算公式为：5. According to the deep learning-based remote monitoring nursing early warning system of claim 4, it is characterized in that: the calculation formula of the physiological state trend index is: ； ; 其中，为生理状态趋向指数，为第i个生理特征的实际测量值，代表各个生理特征的实际测量值，分别对应心率、血压、体温、呼吸频率和血氧饱和度，为第i个生理特征的标准阈值，代表对应的生理特征标准阈值，分别对应心率、血压、体温、呼吸频率和血氧饱和度的标准阈值，的取值范围为；in, is the physiological state trend index, is the actual measured value of the ith physiological characteristic, representing the actual measured value of each physiological characteristic, Corresponding to heart rate, blood pressure, body temperature, respiratory rate and blood oxygen saturation, respectively. is the standard threshold of the ith physiological feature, representing the corresponding standard threshold of the physiological feature, Corresponding to the standard thresholds of heart rate, blood pressure, body temperature, respiratory rate and blood oxygen saturation, The value range is ; 所述生化状态评估指数的计算公式为：The calculation formula of the biochemical status assessment index is: ； ; 其中，为生化状态评估指数，为第j个生化特征的实际测量值，代表各个生化特征的实际测量值，分别对应血糖、血脂和肝肾功能指标，为第j个生化特征的标准阈值，代表对应的生化特征标准阈值，分别对应血糖、血脂和肝肾功能指标的标准阈值，的取值范围为。in, is the biochemical status assessment index, is the actual measured value of the jth biochemical characteristic, representing the actual measured value of each biochemical characteristic, Corresponding to blood sugar, blood lipids and liver and kidney function indicators, is the standard threshold of the jth biochemical feature, represents the corresponding biochemical feature standard threshold, The standard thresholds corresponding to blood sugar, blood lipids and liver and kidney function indicators, The value range is . 6.根据权利要求5所述的一种基于深度学习的远程监测护理预警系统，其特征在于：所述护理预警评价模块中，患者生理健康趋势的评估过程包括：6. A remote monitoring nursing early warning system based on deep learning according to claim 5, characterized in that: in the nursing early warning evaluation module, the evaluation process of the patient's physiological health trend includes: 综合护理预警特征集中的特征信息，将护理预警特征集划分为训练集和测试集，训练集用于模型的训练和学习，测试集用于评估模型的性能；Comprehensive feature information in the nursing warning feature set is used to divide the nursing warning feature set into a training set and a test set. The training set is used for model training and learning, and the test set is used to evaluate the performance of the model. 使用训练集数据结合卷积神经网络构建护理预警模型，学习和识别患者健康状态模式，并使用测试集数据评估模型的性能，其中，在护理预警模型中，输入层接受生理数据序列，输出层输出患者的健康状态预测结果；Use the training set data combined with the convolutional neural network to build a nursing early warning model to learn and identify the patient's health status pattern, and use the test set data to evaluate the performance of the model. In the nursing early warning model, the input layer accepts the physiological data sequence, and the output layer outputs the patient's health status prediction result; 利用护理预警模型结合患者的生理数据，评价患者的健康状态，并结合生理状态趋向指数和生化状态评估指数，计算预警评价系数，综合评估患者的生理健康状态。The nursing early warning model is used in combination with the patient's physiological data to evaluate the patient's health status, and the early warning evaluation coefficient is calculated in combination with the physiological status trend index and biochemical status assessment index to comprehensively evaluate the patient's physiological health status. 7.根据权利要求6所述的一种基于深度学习的远程监测护理预警系统，其特征在于：所述预警评价系数的计算公式为：7. According to a remote monitoring and nursing early warning system based on deep learning according to claim 6, it is characterized in that: the calculation formula of the early warning evaluation coefficient is: ； ; 其中，为预警评价系数，为生理状态趋向指数的权重系数，为生化状态评估指数的权重系数，和用于平衡和对的贡献，为生理状态趋向指数，为生化状态评估指数，为阈值参数，的取值范围在0至1之间。in, is the early warning evaluation coefficient, is the weight coefficient of the physiological state trend index, is the weight coefficient of the biochemical status assessment index, and Used for balance and right Contribution is the physiological state trend index, is the biochemical status assessment index, is the threshold parameter, The value range is between 0 and 1. 8.根据权利要求7所述的一种基于深度学习的远程监测护理预警系统，其特征在于：所述预警分级响应模块中，预警等级的设定及预警响应机制的匹配过程包括：8. A remote monitoring and nursing early warning system based on deep learning according to claim 7, characterized in that: in the early warning graded response module, the setting of the early warning level and the matching process of the early warning response mechanism include: 接收来自护理预警评价模块的输出结果，包括患者的健康状态预测、预警评价系数以及相关的生理特征和生化特征数据；Receive the output results from the nursing early warning evaluation module, including the patient's health status prediction, early warning evaluation coefficient, and related physiological and biochemical characteristics data; 根据护理预警评价模块的结果，设定不同的预警等级，结合预警评价系数将预警等级划分为低预警等级、中预警等级、高预警等级以及紧急预警等级，每个预警等级对应不同的风险程度和紧急程度；According to the results of the nursing early warning evaluation module, different early warning levels are set, and the early warning levels are divided into low warning level, medium warning level, high warning level and emergency warning level according to the early warning evaluation coefficient. Each early warning level corresponds to different risk levels and urgency levels. 分析历史数据为各预警等级匹配相对应的预警阈值，并为每个预警等级设计相应的响应机制。Analyze historical data to match corresponding warning thresholds for each warning level, and design corresponding response mechanisms for each warning level. 9.根据权利要求8所述的一种基于深度学习的远程监测护理预警系统，其特征在于：多个所述预警等级对应多个所述预警阈值，具体满足以下关系：9. A remote monitoring and nursing early warning system based on deep learning according to claim 8, characterized in that: a plurality of the early warning levels correspond to a plurality of the early warning thresholds, specifically satisfying the following relationship: 低预警等级：；Low alert level: ; 中预警等级：；Medium warning level: ; 高预警等级：；High alert level: ; 紧急预警等级：；Emergency warning level: ; 其中，为预警评价系数，为低预警等级对应的下阈值与中预警等级对应的上阈值，为中预警等级对应的下阈值与高预警等级对应的上阈值，为高预警等级对应的下阈值与紧急预警等级对应的上阈值，，，。in, is the early warning evaluation coefficient, is the lower threshold corresponding to the low warning level and the upper threshold corresponding to the medium warning level, is the lower threshold corresponding to the medium warning level and the upper threshold corresponding to the high warning level, is the lower threshold corresponding to the high warning level and the upper threshold corresponding to the emergency warning level, , , . 10.根据权利要求9所述的一种基于深度学习的远程监测护理预警系统，其特征在于：所述用户交互模块具体包括：10. A remote monitoring and nursing early warning system based on deep learning according to claim 9, characterized in that: the user interaction module specifically includes: 根据远程监测护理预警系统的需求，设计包含监测数据展示区、预警信息提示区、健康建议区的用户交互界面；According to the needs of the remote monitoring and nursing early warning system, a user interaction interface including monitoring data display area, early warning information prompt area, and health advice area is designed; 用户交互模块接收来自远程监测护理预警平台的实时数据，包括患者的生理特征、生化特征监测结果，并将患者的生理数据实时更新到用户交互界面上；The user interaction module receives real-time data from the remote monitoring nursing early warning platform, including the patient's physiological characteristics and biochemical characteristics monitoring results, and updates the patient's physiological data to the user interaction interface in real time; 当触发预警条件时，用户交互模块接收到预警信息，在用户交互界面上的预警信息提示区进行展示，同时发出闪烁提醒，用户通过点击预警信息，查看详细的预警详情，包括预警等级、触发条件、建议的响应措施；When the warning condition is triggered, the user interaction module receives the warning information and displays it in the warning information prompt area on the user interaction interface, and issues a flashing reminder. The user clicks on the warning information to view detailed warning details, including the warning level, triggering conditions, and recommended response measures; 根据患者的监测数据和预警等级，提供个性化的健康建议和干预措施，并在界面上的健康建议区展示健康建议。Provide personalized health advice and intervention measures based on the patient's monitoring data and warning level, and display health advice in the health advice area on the interface.
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CN118230936A (en) * 2024-03-05 2024-06-21 中南大学 A health diagnosis system integrated with intelligent medical equipment
CN118969288A (en) * 2024-10-16 2024-11-15 吉林大学第一医院 A risk assessment and early warning method and system for rehabilitation nursing
CN119153121A (en) * 2024-11-19 2024-12-17 长春中医药大学 Orthopedics patient data management and remote monitoring platform

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