Early hypertension screening method based on maternal and fetal characteristics

Abstract

The invention discloses an early hypertension screening method based on maternal and fetal characteristics, which comprises the following steps: monitoring the blood pressure change of a pregnant woman through an electronic sphygmomanometer, and identifying potential high-risk people; secondly, hormone level, blood sugar and blood fat indexes of the pregnant women are detected, and high-risk pregnant women are screened; thirdly, processing the health data of the pregnant woman by using a support vector machine, a decision tree and a deep learning neural network data analysis technology, and evaluating the hypertension risk of the pregnant woman; and then, through dynamic time sequence analysis, the risk that the pregnant woman suffers from hypertension in a certain time period in the future is predicted, accurate health intervention measures are provided according to personalized results, the high-risk pregnant woman can be recognized in the early stage, and threats of hypertension and related complications to mother and infant health are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of early screening, and in particular to an early screening method for hypertension based on maternal and fetal characteristics. Background Art

[0002] Hypertension is one of the most common complications during pregnancy. In severe cases, it can lead to serious consequences such as gestational hypertension, preeclampsia, fetal growth retardation, premature birth, and even threaten the lives of mothers and babies. Currently, screening for hypertension during pregnancy usually relies on regular measurement of the pregnant woman's blood pressure, but this method is not sensitive enough for some pregnant women, especially those whose blood pressure does not fluctuate much or who do not have obvious symptoms of hypertension at certain stages;

[0003] Most existing screening methods focus on single physiological characteristics of maternal blood pressure and weight or ultrasonic detection of fetal development, and do not effectively combine the dual characteristics of mother and fetus for comprehensive analysis. Therefore, existing screening methods often cannot accurately identify high-risk pregnant women in the early stages of hypertension, and cannot intervene and treat in time, causing some pregnant women to miss the best time for treatment.

[0004] Based on this, an early screening method for hypertension based on maternal and fetal characteristics is needed. Summary of the invention

[0005] To achieve the above object, the present invention provides the following scheme: an early screening method for hypertension based on maternal and fetal characteristics, comprising the following steps:

[0006] Step 1: Monitor the blood pressure of pregnant women using an electronic sphygmomanometer at least once a month and record the trend of blood pressure changes; if the systolic blood pressure (SBP) or diastolic blood pressure (DBP) of pregnant women exceeds the set threshold, SBP ≥ 140 mmHg or DBP ≥ 90 mmHg, they are considered as a potential high blood pressure risk group and enter the screening process;

[0007] Step 2: Detect the hormone levels of pregnant women. Use blood tests to measure the concentrations of progesterone, estrogen, and adrenaline. The specific measurements are performed using a blood test kit. The reference range of hormone concentrations is: progesterone: 15-50ng / ml, estrogen: 80-350pg / ml, adrenaline: 50-250pg / ml. If the hormone levels are abnormal, the pregnant woman is considered to be at high risk and needs to be monitored.

[0008] Step 3: Oral glucose tolerance test (OGTT) is used for gestational diabetes screening. The OGTT test standard is that blood glucose ≥7.8mmol / L after 2 hours is considered positive. Blood lipid levels are determined by blood tests, and the reference standards are: total cholesterol>5.2mmol / L, low-density lipoprotein LDL>3.4mmol / L. If the test results are abnormal, the patient will be included in the high-risk group for hypertension.

[0009] Step 4: Use multiple regression analysis, support vector machine (SVM) and deep learning neural network (DNN) algorithms to analyze data, assess the risk of pregnant women developing hypertension, and generate personalized risk assessment results;

[0010] Step 5: Provide personalized health advice to pregnant women. If the risk score is higher than 70%, it is recommended to increase the frequency of blood pressure monitoring and adjust the health management plan based on the health status of the pregnant woman.

[0011] Further preferably, the data analysis adopts a combination of multiple regression analysis, support vector machine SVM and deep learning neural network DNN, wherein:

[0012] Multiple regression analysis was used to identify the linear relationship between maternal physiological data of weight, blood glucose, blood lipids, and age and the risk of hypertension, and to generate corresponding regression coefficients for each feature;

[0013] Support vector machine SVM is used to construct nonlinear classification boundaries based on regression analysis results;

[0014] The deep learning neural network DNN optimizes classification decisions by automatically extracting deep features from maternal health data, identifying nonlinear relationships, and finally weighted averaging the results of the three models to derive a hypertension risk score.

[0015] Further preferably, the probability score calculation method for the occurrence of hypertension is:

[0016] The preliminary risk score for each pregnant woman is calculated through multiple regression analysis and SVM algorithm, and the preliminary probability of hypertension is generated based on the model output; the score is optimized using the deep learning neural network DNN, and the final hypertension risk probability is obtained through weighted calculation of different input features. The outputs of all models are integrated, and the probability of hypertension is calculated by the weighted average method. If the risk probability score of a pregnant woman is ≥70%, the pregnant woman is judged to be at high risk and requires health management and intervention.

[0017] Further preferably, the risk assessment is trained based on known clinical data, and the model is optimized through regression analysis, SVM and DNN machine learning methods. During the risk assessment process, the risk assessment results are dynamically adjusted by real-time monitoring of the blood pressure, weight and hormone level data of pregnant women.

[0018] Further preferably, the screening method also includes real-time monitoring through smart wearable devices, and the smart blood pressure monitor transmits data to a mobile phone application via Bluetooth. The mobile phone application performs real-time analysis on the blood pressure and hormone level health data of the pregnant woman and provides feedback.

[0019] Further preferably, the health data of pregnant women include physiological parameters such as blood sugar, blood lipids, hormone levels, and body mass index (BMI), and the data collection method is a combination of regular testing and real-time monitoring, and the data is transmitted wirelessly to the analysis system.

[0020] Further preferably, the screening results also include a predicted time window for the development of hypertension, and by analyzing the health data of pregnant women, the possibility of them developing hypertension in a certain period of time in the future is predicted, and intervention measures can be taken in advance.

[0021] Further preferably, the predicted time window for the occurrence of hypertension is calculated using a dynamic time series analysis method based on individual health data. By combining the pregnant woman's physiological data of blood sugar, blood lipids, blood pressure fluctuation trends, hormone levels and weight changes, a time window prediction model is constructed to analyze the health data of the pregnant woman at different time points, dynamically assess the risk of developing hypertension in a certain time period in the future, and provide precise health intervention measures for the pregnant woman based on the predicted time window.

[0022] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0023] The present invention can accurately assess the risk of hypertension in pregnant women by comprehensively analyzing the multi-dimensional physiological data of the mother and fetus. Different from the traditional single blood pressure monitoring method, the present invention comprehensively assesses the health status of pregnant women by integrating multiple physiological indicators such as blood sugar, blood lipids, weight changes, hormone levels, and fetal development, effectively improving the accuracy and sensitivity of hypertension risk prediction. By establishing a personalized risk assessment, high-risk pregnant women can be identified in a timely manner, providing doctors with accurate intervention plans, taking corresponding health management measures in advance, and reducing the risk of hypertension in pregnant women. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] Example

[0029] like Figure 1 As shown, an early screening method for hypertension based on maternal and fetal characteristics in this embodiment includes:

[0030] Step 1: Pregnant women’s blood pressure monitoring and screening

[0031] In this implementation scheme, the first step is to regularly monitor the blood pressure of pregnant women through an electronic sphygmomanometer and record the trend of blood pressure changes. Pregnant women need to measure their blood pressure at least once a month, and must measure it multiple times at different time periods to better reflect the changes in the blood pressure of pregnant women. The measurement process should be carried out in a quiet environment to ensure the accuracy of the measurement data. The electronic sphygmomanometer is an automated measurement tool that can quickly and accurately record blood pressure through smart sensors and transmit the results to the hospital's health management system;

[0032] When the measured systolic blood pressure (SBP) of pregnant women is ≥140 mmHg or diastolic blood pressure (DBP) is ≥90 mmHg, they can be identified as people at potential risk of hypertension and enter the further screening process. This screening standard refers to the international clinical hypertension diagnostic standard. Taking into account the physiological changes of pregnant women during pregnancy, this standard can effectively screen out the early risk of hypertension. Each blood pressure record of pregnant women during pregnancy should be compared with the previous measurement value to analyze whether there is a significant increase or fluctuation in blood pressure. If the blood pressure of pregnant women continues to increase or fluctuates in several consecutive measurements, further screening and health intervention measures should be taken in time;

[0033] To improve the accuracy of screening, data analysis is conducted based on the pregnant woman's weight and pregnancy changes to determine whether she is at high risk of hypertension.

[0034] For example, pregnant women who experience rapid weight gain or are obese are often at higher risk for high blood pressure, and this information will be included in subsequent analyses along with blood pressure measurements.

[0035] Step 2: Hormone levels and pregnancy-related disease screening

[0036] The second step is to monitor the hormone levels of pregnant women and screen for pregnancy-related diseases through blood tests, including hormone tests for progesterone, estrogen and adrenaline. Because changes in hormone levels are often closely related to the occurrence of hypertension complications during pregnancy, blood test kits are used to accurately measure the concentrations of these hormones.

[0037] The normal reference ranges of progesterone, estrogen and adrenaline are: progesterone 15-50ng / ml, estrogen 80-350pg / ml, adrenaline 50-250pg / ml. If the test results show that the hormone concentration exceeds the normal reference range, it means that the pregnant woman has a potential risk of hypertension and needs to strengthen health management and monitoring. For the screening of gestational diabetes, the oral glucose tolerance test OGTT is used. In this test, the pregnant woman needs to drink a certain amount of glucose solution. After waiting for 2 hours, the blood sugar level is measured. If the blood sugar level after 2 hours is ≥7.8mmol / L, it is diagnosed as positive for gestational diabetes, indicating that the pregnant woman is in a high-risk group for hypertension.

[0038] In addition, if the blood lipid levels of pregnant women, including total cholesterol >5.2mmol / L and low-density lipoprotein LDL >3.4mmol / L, are abnormal, they will need to enter the high-risk management process.

[0039] Step 3: Data collection and risk assessment model construction

[0040] The third step is to collect data based on the pregnant woman's blood pressure, weight, blood sugar, blood lipids, and hormone levels, and apply machine learning technology to establish a hypertension risk assessment model. The blood sugar, blood lipids, weight, and blood pressure data of pregnant women are collected through regular health checks. Data collection is strictly carried out in accordance with standard procedures to ensure the accuracy and representativeness of the data. At the same time, the physiological parameters of pregnant women are monitored in real time through smart wearable devices, and the data is transmitted to the hospital's health management system or cloud platform through wireless technology to ensure real-time data updates;

[0041] After data collection is completed, the collected data are comprehensively analyzed using machine learning algorithms including multiple regression analysis, support vector machine (SVM) and deep learning neural network (DNN). Multiple regression analysis is used to identify the linear relationship between pregnant women's weight, blood sugar, blood lipids, age and hypertension risk, and generate regression coefficients; the SVM algorithm is used to construct nonlinear classification boundaries for hypertension risk to ensure more accurate risk prediction; DNN uses deep neural networks to learn complex nonlinear relationships in the data, automatically extracts features, optimizes classification decisions, and further improves prediction accuracy.

[0042] Ultimately, the results will be integrated using the weighted average method to calculate a hypertension risk score for each pregnant woman. This score will serve as the basis for assessing the risk of hypertension in pregnant women and help doctors determine whether the pregnant woman belongs to a high-risk group.

[0043] Step 4: Real-time monitoring and feedback from smart wearable devices

[0044] The fourth step is to use smart wearable devices for real-time monitoring, and analyze and provide feedback on the health data of pregnant women through mobile phone applications. Smart blood pressure monitors and smart bracelet devices can monitor the blood pressure, weight, and blood sugar health data of pregnant women in real time, and transmit the data to mobile phone applications through Bluetooth technology. After the data is transmitted to the application, the blood pressure changes, hormone levels, and weight fluctuations of the pregnant women are analyzed in real time.

[0045] Step 5: Prediction of hypertension development and personalized intervention

[0046] Based on steps 1 to 4, the health data of pregnant women have been fully collected and processed, the risk of hypertension in pregnant women has been preliminarily assessed, and a risk prediction model can be established based on individual physiological characteristics and historical data. This step 5 uses dynamic time series analysis to accurately predict the risk of hypertension in pregnant women in the future, and on this basis, develop a personalized health intervention plan, specifically:

[0047] Collect the blood pressure data, weight data, blood sugar, and blood lipid physiological indicators of pregnant women in the past period of time, input the data as a time series, combine the historical change trend, and predict the probability of hypertension in the future period of time. Use ARIMA model or LSTM neural network. These models can process continuous data with time series, capture the time dependence and nonlinear relationship between the physiological parameters of pregnant women, and thus make accurate predictions. In risk assessment, in addition to the individual blood pressure value, the weight fluctuation of pregnant women will also be combined. Rapid weight gain is an important early warning signal for hypertension during pregnancy. Excessive weight gain in pregnant women indicates that the risk of high blood pressure is also increasing. Therefore, weight monitoring and blood pressure monitoring will be integrated. Therefore, changes in hormone levels such as progesterone and estrogen will also affect the blood pressure fluctuations of pregnant women, especially in the later stages of pregnancy. Changes in hormone levels will further increase the risk of hypertension. The probability of hypertension in pregnant women at different time points in the future is evaluated through the model. If the hypertension risk score of pregnant women reaches or exceeds 70%, they will be judged as high-risk pregnant women. At this time, remind doctors to strengthen blood pressure monitoring and health management of pregnant women;

[0048] For high-risk pregnant women with a risk score ≥ 70%, in addition to routine regular blood pressure monitoring, the system will arrange more frequent blood pressure checks and transmit blood pressure data in real time to the health management platform for monitoring by doctors and pregnant women; pregnant women will monitor blood pressure fluctuations at any time through smart wearable devices and feedback data through mobile phone applications to ensure that any blood pressure abnormalities can be dealt with quickly.

[0049] The personalized intervention plan will provide dietary and lifestyle adjustment suggestions based on the specific situation of the pregnant woman. For pregnant women who gain weight too quickly, it is recommended that they control their weight through a reasonable diet, avoid the intake of high-salt and high-fat foods, and increase the intake of foods rich in potassium, calcium, and magnesium to help lower blood pressure. In addition, a low-salt diet can be used to prevent and control hypertension during pregnancy.

[0050] In terms of exercise, pregnant women are advised to do moderate aerobic exercise to improve blood circulation, improve cardiovascular health, help reduce the weight of pregnant women, and reduce the incidence of gestational hypertension. This recommendation is adjusted according to the actual health status of pregnant women and the doctor's advice to ensure safety;

[0051] If the blood pressure of a high-risk pregnant woman continues to rise and lifestyle adjustments fail to effectively control blood pressure, medication intervention is required to reduce the risks of high blood pressure. The choice and dosage of medications are determined by professional doctors based on the specific health conditions of the pregnant woman and must be adjusted and used under the supervision of a doctor.

[0052] While taking medication, pregnant women will continue to use smart devices to monitor their blood pressure to ensure continuous tracking of treatment effects and adjust treatment plans based on real-time data;

[0053] Mental health during pregnancy also has an important impact on the occurrence of hypertension. Long-term psychological stress and anxiety may increase the risk of hypertension in pregnant women. Therefore, personalized intervention plans also include mental health management. By providing psychological counseling, meditation exercises, and breathing relaxation training to pregnant women, stress can be reduced and mental health can be promoted. This can effectively relieve the tension of pregnant women and reduce the risk of hypertension.

[0054] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0055] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for early screening of hypertension based on maternal and fetal characteristics, characterized in that: The following steps are involved: Step 1: Monitor the blood pressure of pregnant women using an electronic sphygmomanometer at least once a month and record the trend of blood pressure changes; if the systolic blood pressure (SBP) or diastolic blood pressure (DBP) of pregnant women exceeds the set threshold, SBP ≥ 140 mmHg or DBP ≥ 90 mmHg, they are considered as a potential high blood pressure risk group and enter the screening process; Step 2: Detect the hormone levels of pregnant women. Use blood tests to measure the concentrations of progesterone, estrogen, and adrenaline. The specific measurements are performed using a blood test kit. The reference range of hormone concentrations is: progesterone: 15-50ng / ml, estrogen: 80-350pg / ml, adrenaline: 50-250pg / ml. If the hormone levels are abnormal, the pregnant woman is considered to be at high risk and needs to be monitored. Step 3: Oral glucose tolerance test (OGTT) is used for gestational diabetes screening. The OGTT test standard is that blood glucose ≥7.8mmol / L after 2 hours is considered positive. Blood lipid levels are determined by blood tests, and the reference standards are: total cholesterol>5.2mmol / L, low-density lipoprotein LDL>3.4mmol / L. If the test results are abnormal, the patient will be included in the high-risk group for hypertension. Step 4: Use multiple regression analysis, support vector machine (SVM) and deep learning neural network (DNN) algorithms to analyze data, assess the risk of pregnant women developing hypertension, and generate personalized risk assessment results; Step 5: Provide personalized health advice to pregnant women. If the risk score is higher than 70%, it is recommended to increase the frequency of blood pressure monitoring and adjust the health management plan based on the health status of the pregnant woman.

2. The method for early screening of hypertension based on maternal and fetal characteristics according to claim 1, characterized in that: The data analysis adopts a combination of multiple regression analysis, support vector machine SVM and deep learning neural network DNN, where: Multiple regression analysis was used to identify the linear relationship between maternal physiological data of weight, blood glucose, blood lipids, and age and the risk of hypertension, and to generate corresponding regression coefficients for each feature; Support vector machine SVM is used to construct nonlinear classification boundaries based on regression analysis results; The deep learning neural network DNN optimizes classification decisions by automatically extracting deep features from maternal health data, identifying nonlinear relationships, and finally weighted averaging the results of the three models to derive a hypertension risk score.

3. The method for early screening of hypertension based on maternal and fetal characteristics according to claim 1, characterized in that: The probability score calculation method for the occurrence of hypertension is: The preliminary risk score for each pregnant woman is calculated through multiple regression analysis and SVM algorithm, and the preliminary probability of hypertension is generated based on the model output; the score is optimized using the deep learning neural network DNN, and the final hypertension risk probability is obtained through weighted calculation of different input features. The outputs of all models are integrated, and the probability of hypertension is calculated by the weighted average method. If the risk probability score of a pregnant woman is ≥70%, the pregnant woman is judged to be at high risk and requires health management and intervention.

4. The method for early screening of hypertension based on maternal and fetal characteristics according to claim 1, characterized in that: The risk assessment is trained based on known clinical data, and the model is optimized through regression analysis, SVM and DNN machine learning methods. During the risk assessment process, the risk assessment results are dynamically adjusted by real-time monitoring of the blood pressure, weight and hormone level data of pregnant women.

5. The method for early screening of hypertension based on maternal and fetal characteristics according to claim 1, characterized in that: The screening method also includes real-time monitoring through smart wearable devices. The smart blood pressure monitor transmits data to a mobile phone application via Bluetooth. The mobile phone application analyzes the blood pressure and hormone level health data of pregnant women in real time and provides feedback.

6. The method for early screening of hypertension based on maternal and fetal characteristics according to claim 1, characterized in that: The health data of pregnant women include physiological parameters such as blood sugar, blood lipids, hormone levels, and body mass index (BMI), and the data collection method is a combination of regular testing and real-time monitoring, and the data is transmitted wirelessly to the analysis system.

7. The method for early screening of hypertension based on maternal and fetal characteristics according to claim 6, characterized in that: The screening results also include a predicted time window for the development of hypertension. By analyzing the health data of pregnant women, the possibility of them developing hypertension in a certain period of time in the future is predicted, and intervention measures can be taken in advance.

8. The method for early screening of hypertension based on maternal and fetal characteristics according to claim 7, characterized in that: The predicted time window for the occurrence of hypertension is calculated using a dynamic time series analysis method based on individual health data. By combining the pregnant woman's physiological data of blood sugar, blood lipids, blood pressure fluctuation trends, hormone levels and weight changes, a time window prediction model is constructed to analyze the health data of the pregnant woman at different time points, dynamically assess the risk of developing hypertension in a certain period of time in the future, and provide precise health intervention measures for the pregnant woman based on the predicted time window.

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