End-edge-cloud collaborative pregnancy risk assessment system

Abstract

The invention discloses an end-edge-cloud collaborative pregnancy risk assessment system, and relates to the field of medicine. The system comprises three modules which are fetal heart and pregnant woman heart rate monitoring equipment of end equipment, a pregnant woman user mobile phone APP of edge equipment and a high-risk pregnancy risk degree evaluation system of a cloud platform. The monitoring device is composed of a fetal heart device and an ECG device which are used for collecting heartbeat data of a fetus and a pregnant woman in real time and calculating the heart rate. During detection, the monitoring device needs to be placed at a corresponding position of a pregnant woman, and the pregnant woman uses the mobile phone APP for control. By optimizing the cooperative work of the doctor management end, the mobile phone edge device and the monitoring device, the system not only can improve the working efficiency of doctors, but also can enhance the self health management ability of pregnant women.

Description

Technical Field

[0001] The present invention relates to the medical field, and in particular to an end-edge-cloud collaborative pregnancy risk assessment system. Background Art

[0003] There are many deficiencies in the existing maternal and infant health monitoring equipment. On the one hand, it is difficult to strike a balance between price, portability and monitoring accuracy for monitoring equipment on the market. Equipment with high monitoring accuracy is often expensive and inconvenient to carry, while the monitoring accuracy of low-priced and easy-to-carry equipment is difficult to be satisfactory. This not only fails to provide effective protection for maternal and infant health, but may even be misleading due to inaccurate data. On the other hand, the functions of existing equipment are relatively simple. Most of them can only calculate heart rate and lack the ability to conduct in-depth analysis of heart rate data to derive health status. For ordinary people, due to the lack of professional medical knowledge, it is difficult to accurately interpret heart rate data, and thus it is impossible to accurately know the health status of mothers and babies.

[0004] In addition, with the widespread use of mobile devices, the integration of many maternal and child health monitoring systems with mobile devices is not good. These systems have cumbersome operating procedures and complex button logic, which cannot meet the needs of users to conveniently view and analyze data with the help of mobile devices. Due to the limited storage and computing resources of mobile devices themselves, many key functions are greatly restricted, making it difficult to provide users with comprehensive, accurate and timely health monitoring services. Moreover, the existing maternal and child health monitoring systems are also relatively lacking in functions on mobile edge devices. Most of them cannot realize convenient viewing of data, nor can they solve doubts in time by asking doctors questions and obtain references about the health status of the fetus. Pregnancy knowledge is very important for pregnant women, but the current channels for obtaining this knowledge are still not convenient enough.

[0005] In addition, there are many problems with the existing doctor management side. When managing maternal and child health data, doctors lack efficient and convenient tools and systems, and often need to switch between different platforms and systems, which not only consumes a lot of time and energy, but also easily leads to information errors and timeliness cannot be guaranteed. In addition, the existing doctor management side is relatively weak in data analysis and processing capabilities, making it difficult to quickly and accurately analyze a large amount of maternal and child health data, making it impossible to timely discover potential health risks and problems and provide timely and effective medical advice and intervention measures for mothers and children.

[0006] Specifically, the patent with Chinese patent application number 202410144776.4 is mainly aimed at medical staff and lacks functions for pregnant women, such as pregnant women actively learning maternal and child knowledge, consulting doctors on mobile terminals, and viewing health assessment information. In addition, the monitoring equipment is complex and has poor portability, which limits the daily use of pregnant women. The patent with Chinese patent application number 202310724249.6 provides relatively simple information, which is only fetal heart rate data and pregnant women's location data. It is difficult to meet the diverse needs of pregnant women for comprehensive health information, and lacks convenient channels for pregnant women to actively learn maternal and child knowledge. There are no interactive functions such as mobile terminal consultation with doctors, doctors publishing maternal and child knowledge and answering pregnant women's questions, which is not conducive to pregnant women obtaining effective health support and a good user experience. The patent with Chinese patent application number 202310724249.6 lacks a special way for pregnant women to actively learn maternal and child knowledge, as well as interactive functions such as consulting doctors on mobile terminals, doctors publishing maternal and child knowledge and answering pregnant women's questions. In addition, there is a lack of relevant operations on the doctor management side, making it difficult to effectively integrate professional knowledge into the system, and it is impossible to provide pregnant women with comprehensive, professional and timely health support. The patent of Chinese patent application No. 202410559153.3 has a large device size and insufficient portability, and the information display method is not as convenient as that of mobile devices, and the information that can be obtained is relatively limited. The patent of Chinese patent application No. 202122341073.5 discloses a fetal heart monitor with relatively low portability in shape design, which cannot well meet the needs of pregnant women to carry and use at any time. The patent of Chinese patent application No. 202310122319.0 can only obtain heart rate data, but cannot provide more comprehensive health monitoring information, which is difficult to meet the needs of pregnant women for a comprehensive understanding of their own and fetal health status, and like the other patents mentioned above, there is a lack of terminal and cloud side functions. For the doctor management side, the existing related patents and systems have obvious deficiencies. The patent of Chinese patent application No. 202411135190.8, entitled "Lymphedema Limb Volume Measurement Data Management System and Method", only contains analysis of limb volume data and treatment data in lymphedema, but lacks channels for patient users to actively consult and information such as patient test data, smoking and drinking, and parents' medical history. The Chinese patent application number 202210180575.0, entitled "Closed-loop management method and system for pre-pregnancy eugenics", contains medical information of pregnant women, but is limited to data retrieval, and does not have maternal and child health risk assessment information obtained through machine learning methods. This shows that when doctors manage health data, they lack a centralized and efficient platform and cannot easily integrate and process maternal and child health monitoring data from different channels. This causes doctors to spend a lot of time and energy to screen and organize information when analyzing data, which is inefficient and difficult for doctors to intuitively view and understand the health status of mothers and babies, thus affecting the accuracy and timeliness of diagnosis.

[0007] Therefore, it is urgent to propose an end-edge-cloud collaborative pregnancy risk assessment system and method to solve the above problems. Summary of the invention

[0008] In view of the above-mentioned defects in the prior art, the technical problem to be solved by the present invention is how to improve the efficiency and accuracy of pregnancy monitoring.

[0009] To achieve the above-mentioned purpose, the present invention provides an end-edge-cloud collaborative pregnancy risk assessment system, characterized in that the present invention comprises three modules, namely, a fetal heart and maternal heart rate monitoring device of an end device, a mobile phone APP of a pregnant user of an edge device, and a high-risk pregnancy risk assessment system of a cloud platform; the monitoring device consists of a fetal heart device and an ECG device, which are respectively used to collect the heartbeat data of the fetus and the pregnant woman in real time, and calculate the heart rate; during detection, the monitoring device needs to be placed in the corresponding position of the pregnant woman, and the pregnant woman uses the mobile phone APP to control it.

[0010] Furthermore, the monitoring device uses a carefully designed hardware circuit to amplify and filter the collected original signal to effectively eliminate interference signals; the software of the monitoring device uses an optimized heart rate calculation algorithm to quickly and accurately calculate the heart rate of the fetus and pregnant woman in the device.

[0011] Furthermore, the monitoring device communicates with the mobile phone APP in real time through a stable wireless method to ensure the timeliness and accuracy of data transmission; the monitoring device has a convenient charging management function, and the user can charge it directly through a wireless charging device, which is simple and fast without complicated wiring operations.

[0012] Furthermore, the mobile phone APP has multiple functions, among which, the mobile phone APP in the "Measurement" interface can be used to view and control the real-time measurement of the device, and send the collected data of the device to the system and make an evaluation; the mobile phone APP in the "I Learn" interface can be used to learn pregnancy learning resources pulled from the system; the mobile phone APP in the "I Ask" interface can be used to interact and communicate with medical staff; in the "Health Record" option of the mobile phone APP, users can upload detailed personal health record information, such as personal information, last menstrual period date and other important information, for more accurate evaluation.

[0013] Furthermore, the collection process of the monitoring device by the mobile phone APP includes the following steps:

[0014] Step S11, entering the measurement interface, after the pregnant woman opens the mobile phone APP, she can clearly see the "measurement" button, and clicks the "measurement" button to enter the fetal heart measurement interface;

[0015] Step S12, connect the device, click the "My Device" button in the upper right corner of the fetal heart measurement interface to enter the Bluetooth device scanning interface, scan the monitoring device and click to connect;

[0016] Step S13, real-time data measurement, after successful connection, the fetal heart device is gently attached to the abdomen of the pregnant woman's uterus and kept still, the ECG device is placed above the pregnant woman's heart, and then the fetal heart measurement interface is entered in the mobile phone APP; accordingly, the pregnant woman can clearly and intuitively see the accurate display of real-time data, including clear fetal heart rate and maternal heart rate;

[0017] Step S14: When the pregnant woman is ready to record heart rate data, she needs to click the "Collect" button to collect it. During the sampling process, the pregnant woman can click the "Stop" button at an appropriate time to stop the collection according to the actual situation. The data is finally saved in the mobile phone APP and uploaded to the system by the user;

[0018] The fetal heart rate and maternal heart rate are sent to the APP via stable and reliable Bluetooth transmission technology;

[0019] Step S15, data viewing: the pregnant woman can click on "local measurement data" in the mobile phone APP to enter and view the collected data that has not been uploaded.

[0020] Furthermore, the step S15 also includes that if the data in the "local measurement data" is uploaded to the system, the pregnant woman can click on the "cloud measurement data" to enter and view the collected uploaded data; the uploaded data will be evaluated by the system to obtain an evaluation report of the fetal heart data.

[0021] Furthermore, the system is divided into two parts: a maternal and infant data management and control platform and a maternal and infant data analysis platform; the system is developed based on a web browser and can be compatible and viewed on multiple systems; the management and control platform is responsible for receiving and storing data transmitted from the APP used by multiple users, and the analysis platform is used to evaluate fetal heart data and assess pregnancy risks;

[0022] The management and control platform supports the display and modification of data, making it convenient for medical staff to view user data.

[0023] Furthermore, the use of the control platform includes the following steps:

[0024] Step S21, entering the system, the medical staff enters the website in the browser, enters the login interface and enters the user name and password for login, and then enters the home page;

[0025] The users of the system are all medical staff, and the management authority is divided according to the department. The management authority can be modified in the system;

[0026] Step S22, view the uploaded data. The medical staff can click "Fetal Heart Assessment" to enter the fetal heart assessment interface. In this interface, the medical staff can view the fetal heart data uploaded by all users within the management authority; the medical staff can search the fetal heart data according to the conditions such as the name of the pregnant woman and the time range, and click "Details" to enter the detailed information chart of the current fetal heart data, including the fetal heart rate and the pregnant woman's heart rate; the specific data values ​​can be viewed in the chart, and the progress bar of the chart can be adjusted to facilitate viewing the fetal heart data in different intervals, so as to fully understand the fetal heart condition;

[0027] Click "Export" to export the current data in PDF or original data format for further analysis, and click "Delete" to delete the current data;

[0028] Step S23, pregnancy knowledge learning, corresponding to the "I Learn" of the mobile phone APP, medical staff upload various pregnancy-related knowledge in the "I Learn Management" of the system;

[0029] The pregnancy-related knowledge may be classified into three categories: "sharing your experience", "sharing your knowledge" and "sharing your knowledge".

[0030] Medical staff can search according to the information title or category of the pregnancy-related knowledge. In addition, click "Add" or "Delete" to add or delete the pregnancy-related knowledge, and click "Edit" in "Operation" to modify it to ensure the accuracy and timeliness of the knowledge;

[0031] Step S24, medical staff communicate with pregnant women, corresponding to the "I ask" of the mobile phone APP, and medical staff interact and communicate with pregnant women in the "Pregnancy Q&A" of the system; the red color in the upper right corner of the "Pregnancy Q&A" title indicates that the medical staff has not replied, and the number represents the number of unanswered messages, so that the medical staff can clearly understand the situation of unresolved issues; click on the message to view details, and the medical staff can see detailed information such as the questioner, question time, reply time, and respondent. In addition, the medical staff can give professional replies to questions and provide timely help and guidance to pregnant women.

[0032] Step S25, manage pregnant women's data. Medical staff can view the information uploaded by registered users in the "Health Records" of the APP in the "Maternal Information" of the system. Medical staff can use this information to make better judgments on the assessment of pregnancy risks; medical staff can click "Edit" to modify the changeable information of pregnant women; click "Delete" to delete the pregnant women's information.

[0033] Furthermore, the analysis platform supports analysis and evaluation of uploaded data, and uses machine learning algorithms to evaluate fetal heart data and assess pregnancy risks; the analysis platform takes into account a variety of key factors, such as the age, gestational age, past medical history, family inheritance, etc. of the pregnant woman, and by building a complex and accurate machine learning model, it can accurately calculate the health status of the pregnant woman and the fetus; the steps for evaluating fetal heart data are relatively simple. After logging into the system, medical staff can click "fetal heart evaluation" to enter the fetal heart evaluation interface. In this interface, medical staff can choose to click the "Evaluation" button to run the fetal heart evaluation algorithm to evaluate different fetal heart data;

[0034] The fetal heart rate assessment algorithm is based on machine learning. By analyzing a large amount of clinical fetal heart rate monitoring data and indicators, a prediction model is established. The machine learning model of the fetal heart rate assessment algorithm can be redeployed in the background to adapt to new data input and improve the algorithm accuracy.

[0035] Furthermore, the purpose of assessing pregnancy risk is to analyze the risk of adverse pregnancy outcomes due to various characteristics of pregnant women, and the implementation steps are as follows:

[0036] Step S31, after logging into the system, the medical staff can click "Risk Assessment" to enter the pregnancy risk assessment interface;

[0037] Step S32, calculating the contribution scores of the features associated with adverse outcomes. To evaluate the pregnancy risk, it is necessary to first calculate the contribution scores of different features to the adverse pregnancy outcomes, that is, the contribution scores of the features associated with adverse outcomes, and provide them to medical staff to view the weight and influence of each feature in the risk assessment. Medical staff can view the contribution scores of the features associated with adverse outcomes in the system;

[0038] The adverse outcome-associated characteristics may be birth weight, gestational age, advanced age, threatened abortion, hypertension, etc.;

[0039] The adverse pregnancy outcomes may be intrahepatic cholestasis of pregnancy (ICP), eclampsia, amniotic fluid embolism, etc.;

[0040] The contribution score is obtained through a machine learning model;

[0041] The machine learning model is obtained after training with a large amount of clinical data and deployed in the analysis platform;

[0042] Step S33, submitting the test data, which is the basis for accurately generating an adverse pregnancy outcome assessment report, and the test data of the pregnant woman is uploaded to the system;

[0043] The test data is imported into the system by a doctor or an administrator;

[0044] Step S34, adverse outcome risk assessment, the medical staff clicks "adverse outcome risk assessment" in the system to view pregnant women whose pregnancy risks can be assessed; for pregnant women who can be assessed, click the "assessment" button, the analysis platform runs the adverse outcome risk assessment algorithm, and generates a risk report of adverse pregnancy outcomes for the pregnant women, so as to provide relevant risk information and suggestions;

[0045] The adverse outcome risk assessment algorithm is based on a machine learning algorithm, and the risk report of adverse pregnancy outcomes is obtained through a machine learning model;

[0046] The machine learning model is obtained after training based on the adverse outcome-associated features and a large amount of clinical test data and is deployed in the analysis platform.

[0047] The present invention has the following technical effects:

[0048] (1) By optimizing the collaborative work of the doctor management terminal, mobile edge devices, and monitoring equipment, the system can not only improve the work efficiency of doctors, but also enhance the self-health management capabilities of pregnant women. The design of the system takes into account the integration of real-time data transmission, comprehensive management, and educational resources, providing a comprehensive and efficient solution for high-risk pregnancy risk assessment.

[0049] (2) The present invention uses mobile edge devices to effectively integrate the data collected by monitoring equipment and display the analysis results of the cloud platform. Pregnant women can not only conveniently view real-time data through their mobile phones, but also obtain rich educational resources and improve their self-management capabilities. At the same time, the optimized monitoring equipment ensures the accuracy of heart rate monitoring, and combines the collaborative processing of mobile phones and cloud platforms to achieve real-time feedback and obtain professional advice from medical staff. This efficient data transfer and real-time monitoring greatly enhances the practicality and reliability of the system.

[0050] (3) The present invention builds an efficient and convenient management cloud platform, which enables doctors to centrally integrate maternal data and conduct powerful analysis and processing. This optimization can simplify doctors' workflow and improve data analysis capabilities, thereby quickly identifying high-risk pregnancies. In addition, the platform also establishes a communication channel with pregnant women, which can display evaluation results in a timely manner, allowing doctors to provide more personalized care plans and ensure the safety of pregnant women.

[0051] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is an architecture diagram of a pregnancy risk assessment system with end-edge-cloud collaboration according to a preferred embodiment of the present invention;

[0053] Figure 2 It is a mobile phone APP function interface of a preferred embodiment of the present invention;

[0054] Figure 3 This is a process of collecting monitoring equipment data by a mobile phone APP in a preferred embodiment of the present invention;

[0055] Figure 4 It is a schematic diagram of a high-risk pregnancy risk assessment system according to a preferred embodiment of the present invention;

[0056] Figure 5 This is a pregnancy risk assessment process of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following describes several preferred embodiments of the present invention with reference to the drawings in the specification, so that the technical content is clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0058] In the drawings, components with the same structure are indicated by the same numerical reference numerals, and components with similar structures or functions are indicated by similar numerical reference numerals. The size and thickness of each component shown in the drawings are arbitrarily shown, and the present invention does not limit the size and thickness of each component. In order to make the illustration clearer, the thickness of the components is appropriately exaggerated in some places in the drawings.

[0059] The present invention proposes a pregnancy risk assessment system and method that is collaborative between an end, an edge and a cloud. The system architecture is as follows: Figure 1 As shown, the modules included are as follows: fetal heart rate and maternal heart rate monitoring equipment of the end device (hereinafter referred to as equipment), mobile phone APP of pregnant users of the edge device (hereinafter referred to as APP) and high-risk pregnancy risk assessment system of the cloud platform (hereinafter referred to as system).

[0060] The device consists of a fetal heart device and an ECG device, which are used to collect heartbeat data of the fetus and pregnant woman in real time, and calculate the heart rate. Furthermore, during the test, the device needs to be placed in the corresponding position of the pregnant woman, and the pregnant woman uses the APP to control it.

[0061] The device uses a carefully designed hardware circuit to amplify and filter the collected original signal, effectively eliminating interference signals and ensuring the purity of the data. Furthermore, the software of the device can quickly and accurately calculate the heart rate of the fetus and pregnant woman in the device by virtue of an optimized heart rate calculation algorithm.

[0062] The device communicates with the APP in real time through a stable wireless method to ensure the timeliness and accuracy of data transmission.

[0063] The device has a convenient charging management function, and users can charge it directly through a wireless charging device, which is simple and fast and does not require complicated wiring operations.

[0064] The APP has multiple functions, Figure 2 An embodiment is given, wherein the APP can be used to view and control the real-time measurement of the device in the "measurement" interface, and send the collected data of the device to the system for evaluation.

[0065] Furthermore, in the "I Learn" interface of the APP, the user can learn pregnancy learning resources pulled from the system.

[0066] Furthermore, the APP can interact and communicate with medical staff in the "I Ask" interface.

[0067] Furthermore, in the "Health Record" option of the APP, users can upload detailed personal health record information, such as personal information, last menstrual period date and other important information, for more accurate assessment.

[0068] The APP has a simple, intuitive and easy-to-use interface, which is convenient for pregnant women to operate. The following is an example of the collection process of the device by the APP, such as Figure 3 shown.

[0069] Step S11: Enter the measurement interface. After the pregnant woman opens the APP, she can clearly see the "measurement" button, and click the "measurement" button to enter the fetal heart measurement interface.

[0070] Step S12: Connect the device. In the fetal heart measurement interface, click the "My Device" button in the upper right corner to enter the Bluetooth device scanning interface, and click to connect after scanning the device.

[0071] Furthermore, the connection may be disconnected after the connection is made.

[0072] Step S13: Real-time data measurement. After the connection is successful, the fetal heart device is gently attached to the abdomen of the pregnant woman's uterus and kept still, the ECG device is placed above the pregnant woman's heart, and then the fetal heart measurement interface is entered in the APP.

[0073] Based on this, pregnant women can clearly and intuitively see the accurate display of real-time data, including clear fetal heart rate and maternal heart rate.

[0074] Furthermore, when a pregnant woman is ready to record heart rate data, she needs to click the "Collect" button to collect it. During the sampling process, the pregnant woman can click the "Stop" button at an appropriate time to stop the collection according to the actual situation, and finally save it in the APP and wait for the user to upload it to the system.

[0075] Preferably, the fetal heart rate and the maternal heart rate are sent to the APP via stable and reliable Bluetooth transmission technology.

[0076] Step S14: Data viewing: Pregnant women can click on "local measurement data" in the APP to enter and view the collected data that has not been uploaded.

[0077] Furthermore, if the data in the "local measurement data" is uploaded to the system, the pregnant woman can click on "cloud measurement data" in the APP to enter and view the collected uploaded data.

[0078] Furthermore, the uploaded data will be evaluated by the system to obtain an evaluation report of the fetal heart data.

[0079] The system is divided into two parts: a maternal and infant data management and control platform (hereinafter referred to as the management and control platform) and a maternal and infant data analysis platform (hereinafter referred to as the analysis platform). The system is developed based on a web browser and can be compatible and viewed in multiple systems. Figure 4 As shown, the management and control platform is responsible for receiving and storing data transmitted from the APP used by multiple users, and the analysis platform is used to evaluate fetal heart data, assess pregnancy risks, etc.

[0080] The control platform supports the display and modification of data, making it convenient for medical staff to view user data. The control platform is used as follows.

[0081] Step S21: Entering the system. The medical staff enters the website in the browser, enters the login interface and enters the user name and password for login, and then enters the home page.

[0082] Furthermore, the users of the system are all medical staff, and the management authority is divided according to the department. The management authority can be modified in the system.

[0083] Step S22: View uploaded data. The medical staff can click "Fetal Heart Assessment" to enter the fetal heart assessment interface. In this interface, the medical staff can view the fetal heart data uploaded by all users within the management authority.

[0084] Furthermore, the fetal heart rate data may be searched based on conditions such as the name of the pregnant woman and the time range.

[0085] Furthermore, by clicking “Details”, you can enter and view detailed information charts of the current fetal heart data, including the fetal heart rate and the maternal heart rate.

[0086] Furthermore, specific data values ​​can be viewed in the chart, and the fetal heart data in different intervals can be conveniently viewed by adjusting the progress bar of the chart, thereby fully understanding the fetal heart condition.

[0087] Furthermore, click "Export" to export the current data in PDF or raw data format for further analysis. Click "Delete" to delete the current data.

[0088] Step S23: Pregnancy knowledge learning. Corresponding to the "I Learn" of the APP, medical staff upload various pregnancy-related knowledge in the "I Learn Management" of the system.

[0089] Preferably, the pregnancy-related knowledge may be divided into three categories: "sharing experience" and "sharing knowledge".

[0090] Furthermore, medical staff can search according to the information title or category of the pregnancy-related knowledge. In addition, click "Add" or "Delete" to add or remove the pregnancy-related knowledge, and click "Edit" in "Operation" to modify it to ensure the accuracy and timeliness of the knowledge.

[0091] Step S24: Medical staff communicates with the pregnant woman. Corresponding to the "Ask Me" function of the APP, the medical staff interacts and communicates with the pregnant woman in the "Pregnancy Q&A" function of the system.

[0092] Furthermore, the red color in the upper right corner of the “Pregnancy Q&A” title indicates that the medical staff has not responded, and the number represents the number of unanswered messages, so that the medical staff has a clear understanding of the status of unresolved issues.

[0093] Furthermore, by clicking on the message to view the details, medical staff can see detailed information such as the questioner, question time, reply time, and replyer. In addition, medical staff can give professional replies to questions and provide timely help and guidance to pregnant women.

[0094] Step S25: Manage pregnant women's data. Medical staff can view the information uploaded by registered users in the "Health Records" of the APP in the "Maternity Information" of the system. Furthermore, medical staff can use this information to make better judgments on the assessment of pregnancy risks.

[0095] Furthermore, medical staff can click "Edit" to modify the maternal and infant information that can be modified; click "Delete" to delete the maternal and infant information.

[0096] The analysis platform supports analysis and evaluation of uploaded data, using machine learning algorithms to evaluate fetal heart data and assess pregnancy risks.

[0097] Furthermore, the analysis platform takes into account a variety of key factors, such as the age of the pregnant woman, gestational age, medical history, family genetics, etc., and can accurately calculate the health of the pregnant woman and the fetus by building a complex and accurate machine learning model. The use of the analysis platform is to evaluate fetal heart data and assess pregnancy risks as an example.

[0098] The steps of evaluating the fetal heart data are relatively simple. After logging into the system, the medical staff can click "Fetal Heart Evaluation" to enter the fetal heart evaluation interface. In this interface, the medical staff can choose to click the "Evaluation" button to run the fetal heart evaluation algorithm to evaluate different fetal heart data.

[0099] Furthermore, the fetal heart rate assessment algorithm is based on machine learning, and a prediction model is established by analyzing a large amount of clinical fetal heart rate monitoring data and indicators. The machine learning model of the fetal heart rate assessment algorithm can be redeployed in the background to adapt to new data input and improve the accuracy of the algorithm.

[0100] The purpose of assessing pregnancy risk is to analyze the risk of adverse pregnancy outcomes caused by various characteristics of pregnant women. The steps of the embodiment are as follows: Figure 5 The detailed steps are as follows.

[0101] Step S31: After logging into the system, the medical staff can click "Risk Assessment" to enter the pregnancy risk assessment interface.

[0102] Step S32: Calculate the contribution score of the adverse outcome-related feature. To assess the pregnancy risk, it is necessary to first calculate the contribution scores of different features to the adverse pregnancy outcome, that is, the contribution scores of the adverse outcome-related features, and provide them to medical staff to view the weight and influence of each feature in the risk assessment. Medical staff can view the contribution scores of the adverse outcome-related features in the system.

[0103] Furthermore, the adverse outcome-associated characteristics may be birth weight, gestational age, advanced age, threatened abortion, hypertension, etc.

[0104] Furthermore, the adverse pregnancy outcome may be intrahepatic cholestasis of pregnancy (ICP), eclampsia, amniotic fluid embolism, etc.

[0105] Furthermore, the contribution score is obtained through a machine learning model.

[0106] Furthermore, the machine learning model is obtained after training with a large amount of clinical data and deployed in the analysis platform.

[0107] Step S33: Submitting the test data. The test data is the basis for accurately generating an adverse pregnancy outcome assessment report, and the test data of the pregnant woman is uploaded to the system.

[0108] Furthermore, the test data is imported into the system by a doctor or an administrator.

[0109] Step S34: Adverse outcome risk assessment. The medical staff clicks "Adverse outcome risk assessment" in the system to view pregnant women who can be assessed for pregnancy risk. For pregnant women who can be assessed, click the "Assess" button, and the analysis platform runs the adverse outcome risk assessment algorithm and generates a risk report for adverse pregnancy outcomes for the pregnant woman to provide relevant risk information and suggestions.

[0110] Furthermore, the adverse outcome risk assessment algorithm is based on a machine learning algorithm. The risk report of adverse pregnancy outcomes is obtained through a machine learning model.

[0111] Furthermore, the machine learning model is obtained after training based on the adverse outcome association characteristics and a large amount of clinical test data and deployed in the analysis platform.

[0112] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. An end-edge-cloud collaborative pregnancy risk assessment system, characterized in that: The present invention comprises three modules, namely, a fetal heart and maternal heart rate monitoring device of an end device, a mobile phone APP of a pregnant user of an edge device, and a high-risk pregnancy risk assessment system of a cloud platform; the monitoring device is composed of a fetal heart device and an ECG device, which are respectively used to collect heartbeat data of the fetus and the pregnant woman in real time and calculate the heart rate; During the test, the monitoring device needs to be placed at the corresponding position of the pregnant woman, and the pregnant woman uses the mobile phone APP to control it.

2. The end-edge-cloud collaborative pregnancy risk assessment system according to claim 1, characterized in that: The monitoring device uses a carefully designed hardware circuit to amplify and filter the collected original signal, effectively eliminating interference signals; the software of the monitoring device uses an optimized heart rate calculation algorithm to quickly and accurately calculate the heart rate of the fetus and pregnant woman in the device.

3. The end-edge-cloud collaborative pregnancy risk assessment system according to claim 2, characterized in that: The monitoring device communicates with the mobile phone APP in real time through a stable wireless method to ensure the timeliness and accuracy of data transmission; the monitoring device has a convenient charging management function, and the user can charge it directly through a wireless charging device, which is simple and fast without complicated wiring operations.

4. The end-edge-cloud collaborative pregnancy risk assessment system according to claim 3, characterized in that: The mobile phone APP has multiple functions. Among them, the mobile phone APP can be used to view and control the real-time measurement of the device in the "Measurement" interface, and send the collected data of the device to the system and make an evaluation; the mobile phone APP can learn pregnancy learning resources pulled from the system in the "I Learn" interface; the mobile phone APP can interact and communicate with medical staff in the "I Ask" interface; in the "Health Record" option of the mobile phone APP, users can upload detailed personal health record information, such as personal information, last menstrual period date and other important information, for more accurate evaluation.

5. The end-edge-cloud collaborative pregnancy risk assessment system according to claim 4, characterized in that: The collection process of the monitoring device by the mobile phone APP includes the following steps: Step S11, entering the measurement interface, after the pregnant woman opens the mobile phone APP, she can clearly see the "measurement" button, and clicks the "measurement" button to enter the fetal heart measurement interface; Step S12, connect the device, click the "My Device" button in the upper right corner of the fetal heart measurement interface to enter the Bluetooth device scanning interface, scan the monitoring device and click to connect; Step S13, real-time data measurement, after successful connection, the fetal heart device is gently attached to the abdomen of the pregnant woman's uterus and kept still, the ECG device is placed above the pregnant woman's heart, and then the fetal heart measurement interface is entered in the mobile phone APP; accordingly, the pregnant woman can clearly and intuitively see the accurate display of real-time data, including clear fetal heart rate and maternal heart rate; Step S14: When the pregnant woman is ready to record heart rate data, she needs to click the "Collect" button to collect it. During the sampling process, the pregnant woman can click the "Stop" button at an appropriate time to stop the collection according to the actual situation. The data is finally saved in the mobile phone APP and uploaded to the system by the user; The fetal heart rate and maternal heart rate are sent to the APP via stable and reliable Bluetooth transmission technology; Step S15, data viewing: pregnant women can click "local measurement data" in the mobile phone APP to enter and view the collected but not uploaded data.

6. The end-edge-cloud collaborative pregnancy risk assessment system according to claim 5, characterized in that: The step S15 also includes that if the data in the "local measurement data" is uploaded to the system, the pregnant woman can click on the "cloud measurement data" to enter and view the collected uploaded data; the uploaded data will be evaluated by the system to obtain an evaluation report of the fetal heart data.

7. The end-edge-cloud collaborative pregnancy risk assessment system according to claim 6, characterized in that: The system is divided into two parts: maternal and infant data management and control platform and maternal and infant data analysis platform; the system is developed based on a web browser and can be compatible and viewed in multiple systems; The management and control platform is responsible for receiving and storing data transmitted from the APP used by multiple users, and the analysis platform is used to evaluate fetal heart data and assess pregnancy risks; The management and control platform supports the display and modification of data, making it convenient for medical staff to view user data.

8. The end-edge-cloud collaborative pregnancy risk assessment system according to claim 7, characterized in that: The use of the control platform includes the following steps: Step S21, entering the system, the medical staff enters the website in the browser, enters the login interface and enters the user name and password for login, and then enters the home page; The users of the system are all medical staff, and the management authority is divided according to the department. The management authority can be modified in the system; Step S22, view the uploaded data. The medical staff can click "Fetal Heart Assessment" to enter the fetal heart assessment interface. In this interface, the medical staff can view the fetal heart data uploaded by all users within the management authority; the medical staff can search the fetal heart data according to the conditions such as the name of the pregnant woman and the time range, and click "Details" to enter the detailed information chart of the current fetal heart data, including the fetal heart rate and the pregnant woman's heart rate; the specific data values ​​can be viewed in the chart, and the progress bar of the chart can be adjusted to facilitate viewing the fetal heart data in different intervals, so as to fully understand the fetal heart condition; Click "Export" to export the current data in PDF or original data format for further analysis, and click "Delete" to delete the current data; Step S23, pregnancy knowledge learning, corresponding to the "I Learn" of the mobile phone APP, medical staff upload various pregnancy-related knowledge in the "I Learn Management" of the system; The pregnancy-related knowledge may be classified into three categories: "sharing experience" and "sharing knowledge"; Medical staff can search according to the information title or category of the pregnancy-related knowledge. In addition, click "Add" or "Delete" to add or delete the pregnancy-related knowledge, and click "Edit" in "Operation" to modify it to ensure the accuracy and timeliness of the knowledge; Step S24, medical staff communicate with pregnant women, corresponding to the "I ask" of the mobile phone APP, and medical staff interact and communicate with pregnant women in the "Pregnancy Q&A" of the system; the red color in the upper right corner of the "Pregnancy Q&A" title indicates that the medical staff has not replied, and the number represents the number of unreplied messages, so that the medical staff can clearly understand the situation of unresolved issues; click on the message to view details, and the medical staff can see detailed information such as the questioner, question time, reply time, and respondent. In addition, the medical staff can give professional replies to questions and provide timely help and guidance to pregnant women. Step S25, manage pregnant women's data. Medical staff can view the information uploaded by registered users in the "Health Records" of the APP in the "Maternal Information" of the system. Medical staff can use this information to make better judgments on the assessment of pregnancy risks; medical staff can click "Edit" to modify the changeable information of pregnant women; click "Delete" to delete the pregnant women's information.

9. The end-edge-cloud collaborative pregnancy risk assessment system according to claim 8, characterized in that: The analysis platform supports analysis and evaluation of uploaded data, and uses machine learning algorithms to evaluate fetal heart data and pregnancy risks; the analysis platform takes into account a variety of key factors, such as the age, gestational age, medical history, family inheritance, etc. of the pregnant woman, and can accurately calculate the health status of the pregnant woman and the fetus by building a complex and accurate machine learning model; the steps for evaluating fetal heart data are relatively simple. After logging into the system, medical staff can click "Fetal Heart Evaluation" to enter the fetal heart evaluation interface. In this interface, medical staff can choose to click the "Evaluation" button to run the fetal heart evaluation algorithm to evaluate different fetal heart data; The fetal heart rate assessment algorithm is based on machine learning. By analyzing a large amount of clinical fetal heart rate monitoring data and indicators, a prediction model is established. The machine learning model of the fetal heart rate assessment algorithm can be redeployed in the background to adapt to new data input and improve the algorithm accuracy.

10. The end-edge-cloud collaborative pregnancy risk assessment system according to claim 9, characterized in that: The purpose of assessing pregnancy risk is to analyze the risk of adverse pregnancy outcomes due to various characteristics of pregnant women. The implementation steps are as follows: Step S31, after logging into the system, the medical staff can click "risk assessment" to enter the pregnancy risk assessment interface; Step S32, calculating the contribution scores of the features associated with adverse outcomes. To evaluate the pregnancy risk, it is necessary to first calculate the contribution scores of different features to the adverse pregnancy outcomes, that is, the contribution scores of the features associated with adverse outcomes, and provide them to medical staff to view the weight and influence of each feature in the risk assessment. Medical staff can view the contribution scores of the features associated with adverse outcomes in the system; The adverse outcome-associated characteristics may be birth weight, gestational age, advanced age, threatened abortion, hypertension, etc.; The adverse pregnancy outcomes may be intrahepatic cholestasis of pregnancy (ICP), eclampsia, amniotic fluid embolism, etc.; The contribution score is obtained through a machine learning model; The machine learning model is obtained after training with a large amount of clinical data and deployed in the analysis platform; Step S33, submitting the test data, which is the basis for accurately generating an adverse pregnancy outcome assessment report, and the test data of the pregnant woman is uploaded to the system; The test data is imported into the system by a doctor or an administrator; Step S34, adverse outcome risk assessment, the medical staff clicks "adverse outcome risk assessment" in the system to view pregnant women whose pregnancy risks can be assessed; for pregnant women who can be assessed, click the "assessment" button, the analysis platform runs the adverse outcome risk assessment algorithm, and generates a risk report of adverse pregnancy outcomes for the pregnant women, so as to provide relevant risk information and suggestions; The adverse outcome risk assessment algorithm is based on a machine learning algorithm, and the risk report of adverse pregnancy outcomes is obtained through a machine learning model; The machine learning model is obtained after training based on the adverse outcome-associated features and a large amount of clinical test data and is deployed in the analysis platform.

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