CN117524451A - Pregnancy hypertension nursing system based on remote monitoring technology - Google Patents
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Abstract
A pregnancy hypertension nursing system based on remote monitoring technology is provided with a monitoring module for collecting and analyzing physiological data and biological identification data of pregnant women; the pressure management module is used for providing VR experience, generating pressure data and evaluating the pressure level of the pregnant woman; the data sharing module is used for encrypting the collected data, storing the data in the block chain and controlling the access of the data in the block chain; the medical advice module is used for receiving the processed data, carrying out AI model prediction on the data and generating personalized medical advice; the remote service module is used for video call and 5G communication. According to the invention, various data of the pregnant woman are collected and analyzed, personalized medical advice is generated according to the prediction result of the AI model, the health condition of the pregnant woman is accurately reflected, and the pressure management of the pregnant woman is effectively assisted, so that more effective medical service is provided; meanwhile, the real-time remote medical service is provided for the pregnant woman, so that the pregnant woman can receive the medical service at home.
Description
Technical Field
The invention relates to the technical field of remote monitoring systems, in particular to a pregnancy hypertension nursing system based on a remote monitoring technology.
Background
Pregnancy hypertension is one of the common complications of pregnant women, and if the pregnancy hypertension is not monitored and managed effectively at any time, the pregnancy hypertension can have serious influence on the health of the pregnant women and infants.
Traditional pregnancy hypertension monitoring and management relies mainly on regular checks by hospitals, but this approach has some problems: firstly, periodic inspection may not be able to discover and address health problems in time for a pregnant woman, especially when sudden symptoms occur in the pregnant woman; second, periodic inspections require the pregnant woman to go to the hospital on a regular basis, which can be a traffic and time hazard for many pregnant women; in addition, the examination result of the hospital can only be checked and interpreted by doctors, and the health condition of the pregnant women and families may not be fully known, so that a pregnancy hypertension nursing system based on a remote monitoring technology is needed, and the problems that the pregnant women need to go to the hospital frequently during pregnancy and the pregnancy of the pregnant women is not monitored in real time are solved.
Disclosure of Invention
In order to solve the problems, the invention provides a pregnancy hypertension nursing system based on a remote monitoring technology, which comprises the following specific technical scheme:
a pregnancy hypertension care system based on a remote monitoring technology, comprising:
and a monitoring module: for collecting and analyzing physiological data and biometric data of the pregnant woman;
the pressure management module: for providing a VR experience, generating stress data to provide a stress management scheme by assessing a stress level of the pregnant woman;
the data sharing module is used for encrypting the collected data, storing the data in the blockchain and controlling the access of the data in the blockchain;
the medical advice module is used for receiving the processed data, carrying out AI model prediction on the data and generating personalized medical advice; and
remote service module: the method is used for video call and 5G communication, and real-time telemedicine communication is provided;
all the modules are connected to a main control server, and data transmission, access and control command sending are performed through the main control server.
Further, the monitoring module receives data monitored by the mobile terminal, the intelligent equipment and the instrument probe, and transmits the data to the main control server through wireless or wired transmission.
Further, the main control server is connected with a data processing module, and the data processing module processes and analyzes the collected and received time or non-time sequence data by adopting a data processing algorithm and a machine learning model so as to realize accurate prediction and analysis.
Further, algorithms employed by the pressure management module, medical advice module, and data processing module include deep learning, support vector machines, or decision trees.
Further, the VR experience includes meditation, relaxation training, or respiratory training, and the stress data is transmitted to the master server by wireless or wired.
Further, the master control server is connected with a pressure level evaluation module, the pressure level evaluation module receives pressure data, adopts a pressure evaluation method, and evaluates the pressure level of the pregnant woman according to the physiological data and the biological identification data of the pregnant woman.
Further, the pressure evaluation method employed includes heart rate variability analysis or skin conductance analysis.
Further, a multi-modal pressure evaluation model, a personalized medical advice generation model, a health risk early warning model and an AI model of a context awareness model can be adopted or constructed, and building, training and optimizing of the model can be achieved by using a Tensorflow or PyTorch algorithm.
Further, the data sharing module is connected to the main control server and comprises a data encryption module, a data storage module and an access control module, wherein the data encryption module encrypts collected data by adopting an encryption algorithm, the data storage module stores the encrypted data in a blockchain by adopting a distributed storage technology, and the access control module controls access of the data by adopting an authority control technology.
Further, the remote service module comprises video call equipment and a 5G communication module, and the 5G communication module is installed on the main control server.
The beneficial effects are that:
according to the invention, physiological data, biological identification data and pressure data of the pregnant woman are collected and analyzed, personalized medical advice is generated according to the prediction result of the AI model, the quality and efficiency of medical service are improved, more personalized medical advice and service are provided, the health condition of the pregnant woman is accurately reflected, and the pressure management of the pregnant woman is effectively assisted, so that more effective medical service is provided; meanwhile, the real-time remote medical service is provided for the pregnant woman, so that the pregnant woman can receive the medical service at home.
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Fig. 1 is an overall flow chart of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise. In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically connected, electrically connected or can be communicated with each other; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.
As shown in fig. 1, a pregnancy hypertension nursing system based on a telemonitoring technology includes:
and a monitoring module: for collecting and analyzing physiological data and biometric data of the pregnant woman;
the pressure management module: for providing a VR experience, generating stress data to provide a stress management scheme by assessing a stress level of the pregnant woman;
the data sharing module is used for encrypting the collected data, storing the data in the blockchain and controlling the access of the data in the blockchain;
the medical advice module is used for receiving the processed data, carrying out AI model prediction on the data and generating personalized medical advice;
remote service module: the method is used for video call and 5G communication, and real-time telemedicine communication is provided;
all the modules are connected to a main control server, and data transmission, access and control command sending are performed through the main control server.
As shown in fig. 1, the monitoring module is used for collecting and analyzing physiological data and biological identification data of pregnant women, wherein the biological identification data comprises heart rate, blood pressure, blood sugar and body temperature, and the data can be from a mobile terminal or a smart device with various sensors, an instrument probe or the like; the heart rate sensor can be arranged on an intelligent bracelet or a watch, the sphygmomanometer and the glucometer can be portable equipment or internet equipment with data communication, and the thermometer is an ear thermometer or a forehead thermometer; physiological data (e.g., heart rate, blood pressure, skin resistance, etc.) of the pregnant woman may also be collected using the smart wearable device. The intelligent devices can be directly connected with a main control server of the system through Bluetooth or Wi-Fi, and transmit data to the main control server, and also can manually input data on a terminal connected with the main control server.
In one embodiment, the main control server is connected with a data processing module, and the data processing module processes and analyzes the collected and received time or non-time series data by adopting a data processing algorithm and a machine learning model so as to realize accurate prediction and analysis and more accurately reflect the health condition of the pregnant woman, thereby providing more effective medical service. Meanwhile, the main control server outputs the prediction and analysis results to a terminal, and the terminal can be a display device at a user end or a doctor end.
As shown in fig. 1, the data processing module processes and analyzes time series data, such as heart rate, blood pressure, etc., using a series of advanced data processing algorithms and machine learning models, using deep learning models, convolutional Neural Networks (CNNs), or Recurrent Neural Networks (RNNs) in processing and analyzing physiological data and biometric data; these models learn and identify complex patterns from the data for more accurate predictions and analyses. In addition, a Support Vector Machine (SVM) or a decision tree and other traditional machine learning algorithms are used for processing and analyzing non-time series data such as blood sugar, body temperature and the like; the algorithms construct a classification or regression model according to the characteristics of the data, so that more accurate prediction and analysis are performed, and the collected data are subjected to advanced treatment and analysis to realize personalized monitoring of pregnant women. The data processing module runs on a high-performance processor, and is of ARM Cortex-A series or Intel Core series and the like.
As shown in fig. 1, the stress management module comprises a VR device, and the VR experience comprises meditation, relaxation training or respiration training, and transmits stress data to a master server through wireless or wired lines.
Preferably, the VR device may be an Oculus lift or HTC drive, etc., placed in a quiet, comfortable environment for pregnant women to use when needed; this device is connected to the host server via USB or HDMI.
In one embodiment, the master control server is connected with a pressure level assessment module, the pressure level assessment module receives pressure data, adopts a pressure assessment method, measures the pressure level of the pregnant woman in real time according to the physiological data and the biological identification data of the pregnant woman by using a machine learning model, assesses the pressure level of the pregnant woman, and can dynamically adjust a pressure relief scheme (for example, recommending different types of meditation exercises, musical therapies and the like).
In particular, in assessing the stress level of a pregnant woman, a series of advanced stress assessment algorithms are used, using Heart Rate Variability (HRV) analysis, which is a very effective stress assessment method, HRV being the interval variation between heart rates; research shows that the change of HRV has strong correlation with the pressure level, and the pressure level of pregnant women is accurately estimated by analyzing the HRV. In addition, skin Conductance (SC) analysis, which is another very effective pressure assessment method, is also used, SC refers to the conductivity of the skin; studies show that the change of the SC has strong correlation with the pressure level, the pressure level of the pregnant woman is accurately estimated by analyzing the SC, and the pressure level of the pregnant woman is accurately estimated by combining the physiological data and the biological identification data of the pregnant woman, so that the management pressure of the pregnant woman is effectively assisted, and the life quality of the pregnant woman is improved. This module runs on a high performance processor, ARM Cortex-A series or Intel Core series, etc., and algorithms used by the module include deep learning, support vector machines, decision trees, etc.
The pressure management module may also employ deep learning in combination with physiological biomarkers; wherein:
biomarker analysis: the physiological stress level of a pregnant woman is obtained by analyzing its saliva, biomarkers (e.g., cortisol levels) in the blood.
Deep learning model: behavioral data, physiological data, and biomarker data of the pregnant woman are analyzed using a deep learning model to predict their mental stress level.
The biomarker data and the traditional physiological data, behavior data and the like are subjected to multi-source data fusion, so that the accuracy of pressure evaluation is improved, real-time monitoring and prediction are performed, the current pressure level is monitored, and the pressure change trend in a future period of time can be predicted.
The stress management module can also adopt the combination of emotion recognition and stress assessment; wherein:
emotion recognition: by analyzing information such as facial expression, voice and the like of the pregnant woman, the current emotion state of the pregnant woman is identified by using a machine learning model.
Pressure evaluation: and taking the emotion recognition result as one of the inputs, and carrying out pressure assessment by using a deep learning model in combination with other physiological data and behavior data.
Analysis of emotion and stress correlation: by analyzing the correlation between emotion and stress, the predictive power of the stress assessment model is enhanced.
Dynamic adjustment: the model can dynamically adjust the pressure evaluation result according to the real-time emotion change of the pregnant woman.
The pressure management module can also adopt a personalized pressure evaluation model; wherein:
training a personalized model: a personalized stress assessment model is trained based on historical data for each pregnant woman.
Continuous optimization: as more data is accumulated, the predictive power of the personalized model is continually optimized.
Consideration of individual differences: the individual difference of each pregnant woman is fully considered, and the prediction precision of the model is improved.
Self-adaptive learning: the model can adaptively learn the pressure response mode of each pregnant woman, and continuously improves the evaluation effect.
The pressure management module can also perform pressure source identification and intervention suggestion generation; wherein:
and (3) identifying a pressure source: by analyzing the behavioral logs of the pregnant woman, environmental data, etc., possible sources of stress are identified.
Intervention suggestion generation: personalized stress relief advice is provided using natural language generation techniques depending on the source of stress and the health of the pregnant woman.
Active intervention: not only is the pressure assessed, but also the pressure source can be actively identified, and intervention advice is provided, so that the transition from passive to active is realized.
Comprehensive care: attention is paid to the overall health condition of pregnant women, and an omnibearing pressure management scheme is provided.
As shown in fig. 1, the data sharing module is connected to the main control server and comprises a data encryption module, a data storage module and an access control module, wherein the data encryption module encrypts collected data by adopting an encryption algorithm to ensure the safety of the data, the data storage module stores the encrypted data in a block chain by adopting a distributed storage technology to realize the persistent storage of the data, and the access control module controls the access of the data by adopting an authority control technology to ensure the safety of the data and prevent the data from being accessed by unauthorized persons.
The data sharing module comprises a blockchain node, ethereum or Hyperledger Fabric and the like, wherein the blockchain node is connected with the master control server through Ethernet or Wi-Fi and transmits encrypted data to the data encryption module, the data storage module and the data access control module; the block chain link points are installed on a safe and stable server so as to ensure the safety and stability of data.
As shown in fig. 1, the medical advice module includes an AI model prediction module, an advice generation module, and a data input module, wherein the AI model prediction module is provided with an AI model, and predicts according to the data received by the data input module; the advice generation submodule analyzes health data and environment data of the pregnant woman by using the AI model, predicts and generates personalized medical advice and preventive measures, improves the quality and efficiency of medical service, and provides more personalized medical service; the main control server outputs the personalized medical advice produced by the main control server to a terminal, wherein the terminal can be display equipment of a user side or a doctor side; meanwhile, health risk early warning can be provided, potential health risks (such as pregnancy diabetes, hypertension and the like) can be identified by using the AI model, and early warning and intervention suggestions can be timely given. The data input module receives data input from other modules or data processed and analyzed by the data processing module. The medical advice module operates on a high-performance processor installed in the main control server, and the ARM Cortex-A series or Intel Core series and the like, and algorithms used by the module comprise deep learning, support vector machines, decision trees and the like.
The specific applicable or constructed AI model and algorithm are realized as follows:
(1) Multi-modal pressure assessment model
A. Data source
Physiological data: heart rate, blood pressure, skin resistance, etc.
Behavior data: activity level, sleep quality, etc.
Environmental data: ambient noise, light, etc.
B. Model architecture
Input layer: and (5) multi-source data input.
Feature extraction layer: features of the time series data are extracted using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Fusion layer: multimodal features are integrated using an attention mechanism or other fusion strategy.
Output layer: the pressure level is predicted.
C. Algorithm implementation
Model building, training and optimization are achieved using Tensorflow or PyTorch.
(2) Personalized medical advice generation model
A. Data source
Health data: physiological index, medical history, etc.
Environmental data: living environment, working environment, etc.
B. Model architecture
Input layer: health data and environmental data input.
Reasoning layer: health risk reasoning is performed using decision trees, random forests, or gradient lifting trees.
And (3) generating a layer: personalized suggestions are generated using a Natural Language Generation (NLG) model.
C. Algorithm implementation
Model building and training of the inference layer was achieved using Scikit-learn.
Medical advice is generated using GPT-3 or other NLG models.
(3) Health risk early warning model
A. Data source
Physiological data: and (5) monitoring physiological indexes in real time.
Historical data: past health records.
B. Model architecture
Input layer: real-time data and historical data.
Prediction layer: future health risks are predicted using long-short-term memory network (LSTM) or Transformer (Transformer) models.
Output layer: and outputting a risk early warning signal.
C. Algorithm implementation
Model building, training and optimization are achieved using either PyTorch or TensorFlow.
(4) Context awareness model
A. Data source
Behavior data: a log of the user's behavior.
Physiological data: physiological index of the user.
B. Model architecture
Input layer: behavioral data and physiological data input.
Perception layer: the RNN or transducer model is used to sense the behavioral patterns and physiological responses of the user.
Output layer: and outputting the situation label.
C. Algorithm implementation
Model building, training and optimization are achieved using Tensorflow or PyTorch.
As shown in fig. 1, the remote service module is used for video call and 5G communication to provide real-time remote medical service, which can improve the accessibility and convenience of medical service, so that pregnant women can receive medical service at home. The telemedicine service module comprises a video call equipment module for high-definition video call service and a 5G communication module for high-speed data transmission service.
Illustratively, the video call device module may be Webcam or Smartphone, etc., and the 5G communication module may be 5G NR or 5G NR mmWave, etc., and these devices are connected to the master server through USB or HDMI to transmit the video call to the doctor's device; the video call equipment is installed in the family of pregnant women, and the 5G communication module is installed in the master control server.
The working process of the system comprises the following steps:
(1) The pregnant woman transmits the detected data to the monitoring module through the intelligent equipment and the inspection instrument, the monitoring module performs preliminary analysis, arrangement and classification on the physiological data and the biological identification data of the received pregnant woman, and then the data is transmitted and stored in the main control server.
(2) The data processing module reads the data in the main control server, processes and analyzes the data by adopting a data processing algorithm and a machine learning model, and outputs the predicted and analyzed results to the terminal.
(3) And the pregnant woman performs VR experience and transmits the data to the pressure management module, generates pressure data, and transmits and stores the pressure data to the master control server.
(4) The medical advice module reads the physiological data, the biological identification data and the pressure data of the pregnant woman in the main control server through the data input module, predicts by utilizing the AI model prediction module, generates personalized medical advice, and outputs the personalized medical advice to the terminal.
(5) The pregnant woman can initiate video call at any time through the remote service module, or the doctor actively contacts the pregnant woman to provide medical service according to the result or the proposal if the doctor considers necessary.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A pregnancy hypertension nursing system based on remote monitoring technology, comprising:
and a monitoring module: for collecting and analyzing physiological data and biometric data of the pregnant woman;
the pressure management module: for providing a VR experience, generating stress data to provide a stress management scheme by assessing a stress level of the pregnant woman;
the data sharing module is used for encrypting the collected data, storing the data in the blockchain and controlling the access of the data in the blockchain;
the medical advice module is used for receiving the processed data, carrying out AI model prediction on the data and generating personalized medical advice; and
remote service module: the method is used for video call and 5G communication, and real-time telemedicine communication is provided;
all the modules are connected to a main control server, and data transmission, access and control command sending are performed through the main control server.
2. The pregnancy and hypertension nursing system based on the remote monitoring technology according to claim 1, wherein the monitoring module receives the data monitored by the mobile terminal, the intelligent device and the instrument probe, and transmits the data to the main control server through wireless or wire.
3. The pregnancy-induced hypertension nursing system according to claim 2, wherein the main control server is connected with a data processing module, and the data processing module processes and analyzes the collected and received time or non-time series data by using a data processing algorithm and a machine learning model, so as to realize accurate prediction and analysis.
4. A pregnancy-induced hypertension care system according to claim 3, wherein the algorithms employed by the pressure management module, medical advice module and data processing module comprise deep learning, support vector machine or decision tree.
5. The system of claim 1, wherein the VR experience comprises meditation, relaxation training or respiratory training and the stress data is transmitted to the host server wirelessly or by wire.
6. The pregnancy-induced hypertension nursing system according to claim 5, wherein the main control server is connected with a pressure level evaluation module, and the pressure level evaluation module receives pressure data, adopts a pressure evaluation method, and evaluates the pressure level of the pregnant woman according to the physiological data and the biological identification data of the pregnant woman.
7. The system of claim 6, wherein the pressure assessment method comprises heart rate variability analysis or skin conductance analysis.
8. The system according to any one of claims 4 to 7, wherein a multimodal pressure assessment model, a personalized medical advice generation model, a health risk early warning model, an AI model of a context awareness model can be used or constructed, and the model can be built, trained and optimized using a TensorFlow or PyTorch algorithm.
9. The pregnancy-induced hypertension nursing system according to claim 1, wherein the data sharing module is connected to the main control server, and comprises a data encryption module, a data storage module and an access control module, wherein the data encryption module encrypts the collected data by using an encryption algorithm, the data storage module stores the encrypted data in a blockchain by using a distributed storage technology, and the access control module controls access to the data by using a permission control technology.
10. The pregnancy-induced hypertension nursing system according to claim 1, wherein the remote service module comprises a video call device and a 5G communication module, and the 5G communication module is installed on the main control server.
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| CN117831745A (en) * | 2024-03-06 | 2024-04-05 | 中国人民解放军海军青岛特勤疗养中心 | Remote nursing management method and system based on data analysis |
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| CN117831745A (en) * | 2024-03-06 | 2024-04-05 | 中国人民解放军海军青岛特勤疗养中心 | Remote nursing management method and system based on data analysis |
| CN117831745B (en) * | 2024-03-06 | 2024-05-07 | 中国人民解放军海军青岛特勤疗养中心 | Remote nursing management method and system based on data analysis |
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