Deep learning-based gestational hypertension risk prediction method and system

Abstract

The invention relates to the technical field of pregnant woman health management, and discloses a gestational hypertension risk prediction method and system based on deep learning, and the method comprises the steps: collecting physical examination data and past medical history records of a gestational woman, and recognizing a risk evaluation index of the gestational woman; identifying a physical sign parameter corresponding to the risk evaluation index, and analyzing a parameter change mode of the physical sign parameter; identifying potential hypertension disease characteristics of the gestational women, and analyzing gestational hypertension risks of the gestational women; extracting risk factors of gestational hypertension risks, and identifying risk levels of the risk factors; key risk factors of the gestational hypertension risk are extracted, a risk formation mechanism of the gestational hypertension risk is identified, and a risk early warning network of the gestational hypertension risk is set; and in combination with the risk formation mechanism and the risk early warning network, carrying out risk analysis on the gestational hypertension risk to obtain a risk analysis result. The accuracy and efficiency of pregnancy hypertension risk prediction can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for predicting the risk of pregnancy-induced hypertension based on deep learning, and belongs to the technical field of pregnant women's health management. Background Art

[0002] The prediction of the risk of pregnancy-induced hypertension refers to the process of predicting the possibility of a pregnant woman developing hypertension during pregnancy. Through risk prediction, high-risk pregnant women who may develop pregnancy-induced hypertension can be identified in the early stage of pregnancy, so as to carry out early intervention and management. With the continuous improvement of people's attention to maternal and child health, pregnancy-induced hypertension, as one of the common complications during pregnancy, has received extensive research and attention. Pregnancy-induced hypertension will not only bring diseases such as preeclampsia, heart failure, and renal failure to pregnant women, but may also have an adverse impact on the growth and development of the fetus, such as placental abruption. Therefore, in order to detect high-risk pregnant women in advance, it is necessary to accurately predict the risk of pregnancy-induced hypertension in pregnant women.

[0003] Traditional methods for predicting the risk of pregnancy-induced hypertension mainly evaluate the risk by measuring clinical indicators such as the blood pressure, weight, and proteinuria of pregnant women. However, this method can often only detect abnormalities when the disease has developed to a certain extent, which may lead to missed diagnosis or misdiagnosis and affect the accuracy of pregnancy-induced hypertension risk prediction.

[0004] Therefore, there is an urgent need for a solution to improve the accuracy and efficiency of pregnancy-induced hypertension risk prediction. Summary of the Invention

[0005] The present invention provides a method and system for predicting the risk of pregnancy-induced hypertension based on deep learning, and its main purpose is to improve the accuracy and efficiency of pregnancy-induced hypertension risk prediction.

[0006] To achieve the above object, a method for predicting the risk of pregnancy-induced hypertension based on deep learning provided by the present invention includes:

[0007] Obtain pregnant women during pregnancy to be analyzed, collect the physical examination data and past medical history records of the pregnant women, and based on the physical examination data and the past medical history records, identify the risk evaluation indicators of the pregnant women;

[0008] Identify the physical sign parameters corresponding to the risk evaluation indicators, extract the time series data of the physical sign parameters, based on the time series data, analyze the change trend of the physical sign parameters, and according to the change trend, identify the parameter change pattern of the physical sign parameters;

[0009] Extract the classification index features of the risk evaluation index, identify the potential hypertensive disease features of the pregnant women based on the classification index features and the parameter change pattern, and analyze the pregnancy-induced hypertension risk of the pregnant women according to the potential hypertensive disease features;

[0010] Analyze the formation causes of the pregnancy-induced hypertension risk, extract the risk factors of the pregnancy-induced hypertension risk, identify the proportion of the role of the risk factors in the pregnancy-induced hypertension risk, analyze the risk occurrence probability of the risk factors according to the formation causes, and identify the risk level of the risk factors by combining the proportion of the role and the risk occurrence probability;

[0011] Based on the risk level, extract the key risk factors of the pregnancy-induced hypertension risk, identify the risk formation mechanism of the pregnancy-induced hypertension risk according to the key risk factors, and set up a risk warning network for the pregnancy-induced hypertension risk based on the risk formation mechanism;

[0012] Combine the risk formation mechanism and the risk warning network to conduct a risk analysis of the pregnancy-induced hypertension risk and obtain a risk analysis result.

[0013] Optionally, the identifying the risk evaluation index of the pregnant women based on the physical examination data and the past medical history record includes:

[0014] Analyze the single data type of the physical examination data and identify the normal parameter range corresponding to the single data type;

[0015] Extract the abnormal parameters of the physical examination data according to the normal parameter range;

[0016] Analyze the degree of variation of the abnormal parameters and analyze the risk effect of the abnormal parameters on the pregnant women according to the degree of variation;

[0017] Identify the current physical state of the pregnant women based on the past medical history record;

[0018] Extract the key physical sign parameters of the pregnant women according to the risk effect and the current physical state;

[0019] Identify the risk evaluation index of the pregnant women based on the key physical sign parameters.

[0020] Optionally, the identifying the parameter change pattern of the physical sign parameter according to the change trend includes:

[0021] Identify the regression trend and divergence pattern of the physical sign parameter according to the change trend;

[0022] Extract the key points of variation of the physical sign parameters based on the regression trend and the divergence pattern;

[0023] Define the variation stages of the physical sign parameters according to the key points of variation, and analyze the phased changes of the physical sign parameters in the variation stages;

[0024] Identify the variation cycle of the physical sign parameters based on the phased changes;

[0025] Analyze the trend characteristics of the physical sign parameters in the variation cycle;

[0026] Combine the phased changes, the variation cycle and the trend characteristics to identify the parameter change pattern of the physical sign parameters.

[0027] Optionally, identifying the potential hypertensive disease characteristics of the pregnant women based on the classification index characteristics and the parameter change pattern includes:

[0028] Collect the associated data of the classification index characteristics and the parameter change pattern;

[0029] Analyze the blood pressure conditions of the pregnant women based on the associated data;

[0030] Identify the index parameters corresponding to the blood pressure conditions according to the classification index characteristics;

[0031] Identify the parameter change characteristics of the index parameters based on the parameter change pattern;

[0032] Analyze the blood pressure abnormality risk of the pregnant women according to the parameter change characteristics;

[0033] Identify the blood pressure risk type of the pregnant women based on the blood pressure abnormality risk;

[0034] Identify the potential hypertensive disease characteristics of the pregnant women according to the risk type.

[0035] Optionally, analyzing the pregnancy-induced hypertension risk of the pregnant women according to the potential hypertensive disease characteristics includes:

[0036] Identify the potential hypertensive risks of the pregnant women according to the potential hypertensive disease characteristics;

[0037] Analyze the key influencing factors of the potential hypertensive risks;

[0038] Identify the potential symptoms of the pregnant women according to the key influencing factors;

[0039] Collect the blood pressure characteristic data of the pregnant women;

[0040] Combined with the key influencing factors and the potential symptoms, perform a risk assessment on the blood pressure characteristic data to obtain a risk assessment result;

[0041] Calculate the reliability coefficient of the risk assessment result;

[0042] According to the reliability coefficient, define the result interpretation of the risk assessment result;

[0043] Combined with the risk analysis model and the result interpretation, analyze the pregnancy-induced hypertension risk of the pregnant women.

[0044] Optionally, analyzing the formation causes of the pregnancy-induced hypertension risk includes:

[0045] Obtain the patients with the pregnancy-induced hypertension risk and their corresponding medical data;

[0046] Based on the medical data, identify the pathological characteristics of the patients;

[0047] According to the pathological characteristics, perform a gene heterogeneity analysis on the patients to obtain a gene heterogeneity analysis result;

[0048] According to the medical data, analyze the group characteristics of the pregnancy-induced hypertension risk, and based on the group characteristics, analyze the influencing factors of the pregnancy-induced hypertension risk;

[0049] Identify the interrelationships between the influencing factors, and according to the interrelationships, analyze the individual effects and synergistic effects of the influencing factors on the pregnancy-induced hypertension risk;

[0050] Combined with the gene heterogeneity analysis result, the individual effect and the synergistic effect, analyze the formation causes of the pregnancy-induced hypertension risk.

[0051] Optionally, analyzing the risk occurrence probability of the risk factors according to the formation causes includes:

[0052] According to the formation causes, analyze the health outcomes of the risk factors;

[0053] Based on the health outcomes, identify the risk events corresponding to the risk factors;

[0054] Analyze the event types of the risk events and define the risk turning points of the event types;

[0055] According to the risk turning points, determine the risk occurrence conditions of the risk factors;

[0056] Based on the risk occurrence conditions, identify the occurrence frequencies of the risk factors;

[0057] Analyze the risk occurrence probability of the risk factors according to the risk occurrence conditions and the occurrence frequency.

[0058] Optionally, identifying the risk formation mechanism of the pregnancy-induced hypertension risk according to the key risk factors includes:

[0059] Identify the pregnant women with pregnancy-induced hypertension corresponding to the pregnancy-induced hypertension risk;

[0060] Analyze the mutual influence relationship between the key risk factors;

[0061] Identify the comprehensive effect of the key risk factors on the pregnant women with pregnancy-induced hypertension;

[0062] According to the mutual influence relationship and the comprehensive effect, analyze the change characteristics of the physiological indexes of the pregnant women with pregnancy-induced hypertension;

[0063] Based on the change characteristics of the physiological indexes, identify the body reactions of the pregnant women with pregnancy-induced hypertension;

[0064] According to the change characteristics of the physiological indexes and the body reactions, analyze the pathological pattern of the pregnancy-induced hypertension risk;

[0065] Combining the change characteristics of the physiological indexes, the body reactions and the pathological pattern, identify the risk formation mechanism of the pregnancy-induced hypertension risk.

[0066] Optionally, setting the risk warning network for the pregnancy-induced hypertension risk based on the risk formation mechanism includes:

[0067] Collect the physiological index data corresponding to the pregnancy-induced hypertension risk and identify the pregnant women corresponding to the physiological index data;

[0068] Based on the risk formation mechanism, extract the abnormal index characteristics of the physiological index data;

[0069] According to the abnormal index characteristics, construct the physiological index monitoring network for the pregnant women;

[0070] Based on the physiological index monitoring network, identify the personalized index characteristics of the pregnant women;

[0071] According to the personalized index characteristics, define the personalized warning mechanism for the pregnant women;

[0072] Based on the physiological index monitoring network and the personalized warning mechanism, set the risk warning network for the pregnancy-induced hypertension risk.

[0073] To solve the above problems, the present invention also provides a pregnancy-induced hypertension risk prediction system based on deep learning, and the system includes:

[0074] A risk index identification module, which is used to obtain pregnant women to be analyzed, collect the physical examination data and past medical history records of the pregnant women, and identify the risk assessment indicators of the pregnant women based on the physical examination data and the past medical history records;

[0075] A physical sign pattern analysis module, which is used to identify the physical sign parameters corresponding to the risk assessment indicators, extract the time series data of the physical sign parameters, analyze the change trend of the physical sign parameters based on the time series data, and identify the parameter change pattern of the physical sign parameters according to the change trend;

[0076] A potential risk analysis module, which is used to extract the classification index features of the risk assessment indicators, identify the potential hypertensive disease characteristics of the pregnant women based on the classification index features and the parameter change pattern, and analyze the pregnancy-induced hypertension risk of the pregnant women according to the potential hypertensive disease characteristics;

[0077] A key risk factor extraction module, which is used to analyze the formation reasons of the pregnancy-induced hypertension risk, extract the risk factors of the pregnancy-induced hypertension risk, identify the proportion of the role of the risk factors in the pregnancy-induced hypertension risk, analyze the risk occurrence probability of the risk factors according to the formation reasons, and identify the risk level of the risk factors by combining the proportion of the role and the risk occurrence probability;

[0078] A risk handling module, which is used to extract the key risk factors of the pregnancy-induced hypertension risk based on the risk level, identify the risk formation mechanism of the pregnancy-induced hypertension risk according to the key risk factors, and set up a risk warning network for the pregnancy-induced hypertension risk based on the risk formation mechanism;

[0079] A risk prediction module, which is used to perform risk analysis on the pregnancy-induced hypertension risk by combining the risk formation mechanism and the risk warning network to obtain a risk analysis result.

[0080] Compared with the problems described in the background art, in the embodiments of the present invention, by identifying the risk assessment indicators of pregnant women based on the physical examination data and the past medical history records, the development trend of pregnancy-induced hypertension in pregnant women can be predicted, which helps to formulate effective risk prevention measures; further, in the embodiments of the present invention, by analyzing the change trend of the physical sign parameters based on the time series data, the change pattern of the physical sign parameters of pregnant women can be identified according to the trend change rule, providing a feature reference for subsequent pregnancy-induced hypertension risk prediction, so as to identify the parameter change pattern of the physical sign parameters, help to evaluate the risk of pregnant women developing into specific diseases, and predict the development trend of the risk; secondly, in the embodiments of the present invention, by identifying the potential hypertension disease characteristics of pregnant women based on the classification index characteristics and the parameter change pattern, it helps to construct a risk prediction model for pregnant women, so as to identify patients with a high risk of pregnancy-induced hypertension in advance, and provide personalized medical advice and treatment plans for them, improve the health level of patients, and analyze the pregnancy-induced hypertension risk of pregnant women according to the potential hypertension disease characteristics, so as to identify and intervene in the early stage of the development of pregnancy-induced hypertension, thereby preventing the occurrence of the disease or reducing its severity, and improving the maternal and perinatal outcomes; thirdly, in the embodiments of the present invention, by analyzing the causes of the formation of the pregnancy-induced hypertension risk, the occurrence and development mechanism of pregnancy-induced hypertension diseases can be understood, providing a theoretical basis for subsequent prevention and treatment, and analyzing the risk occurrence probability of the risk factors according to the formation causes, more accurately evaluating the possibility of pregnant women suffering from pregnancy-induced hypertension, so as to help doctors take closer monitoring and targeted prevention measures; in addition, in the embodiments of the present invention, according to the key risk factors, identifying the risk formation mechanism of the pregnancy-induced hypertension risk can help to detect high-risk pregnant women early and take corresponding prevention and intervention measures to reduce the occurrence risk of pregnancy-induced hypertension, improve the maternal and child prognosis, and based on the risk formation mechanism, setting up a risk warning network for the pregnancy-induced hypertension risk can effectively monitor and warn pregnant women, reduce the risks brought by pregnancy-induced hypertension, thereby improving the prevention and treatment level of pregnancy-induced hypertension and ensuring the health of the mother and child; finally, in the embodiments of the present invention, by combining the risk formation mechanism and the risk warning network, analyzing the pregnancy-induced hypertension risk to obtain a risk analysis result, it can achieve accurate prediction and early warning of the pregnancy-induced hypertension risk, help doctors understand the health status of pregnant women more comprehensively, and formulate more effective prevention and treatment strategies, so as to reduce the incidence and complication rate of pregnancy-induced hypertension and improve the health level of the mother and child. Therefore, the pregnancy-induced hypertension risk prediction method and system based on deep learning provided by the embodiments of the present invention can improve the accuracy and efficiency of pregnancy-induced hypertension risk prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1Schematic flowchart of a method for predicting the risk of pregnancy-induced hypertension based on deep learning provided by an embodiment of the present invention;

[0082] Figure 2 Schematic diagram of a module for implementing the method for predicting the risk of pregnancy-induced hypertension based on deep learning provided by an embodiment of the present invention.

[0083] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0084] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0085] An embodiment of the present application provides a method for predicting the risk of pregnancy-induced hypertension based on deep learning. The execution subject of the method for predicting the risk of pregnancy-induced hypertension based on deep learning includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for predicting the risk of pregnancy-induced hypertension based on deep learning can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0086] Embodiment 1:

[0087] Refer to Figure 1 As shown, it is a schematic flowchart of a method for predicting the risk of pregnancy-induced hypertension based on deep learning provided by an embodiment of the present invention. In this embodiment, the method for predicting the risk of pregnancy-induced hypertension based on deep learning includes:

[0088] S1. Obtain pregnant women to be analyzed, collect the physical examination data and past medical history records of the pregnant women, and identify the risk assessment indicators of the pregnant women based on the physical examination data and the past medical history records.

[0089] In the embodiment of the present invention, by obtaining pregnant women to be analyzed, data support can be provided for subsequent prediction of the risk of pregnancy-induced hypertension. The pregnant women refer to women during pregnancy.

[0090] Furthermore, in the embodiment of the present invention, by collecting the physical examination data and past medical history records of the pregnant women, it can help doctors diagnose the pregnancy-induced hypertension disease of the pregnant woman. The physical examination data refers to physiological parameters and health index data obtained through medical examinations, and the past medical history records refer to the past medical and health records of pregnant women, including information such as previous diseases, treatments, surgeries, and drug use.

[0091] Optionally, the collection of the physical examination data and the record of the previous medical history of the pregnant women can be implemented by using an electronic health record system.

[0092] In the embodiment of the present invention, by identifying the risk evaluation indicators of the pregnant women based on the physical examination data and the record of the previous medical history, the development trend of pregnancy-induced hypertension in pregnant women can be predicted, which helps to formulate effective risk prevention measures. The risk evaluation indicators refer to various parameters and measurement values for evaluating the risk of pregnancy-induced hypertension, such as the urine protein level.

[0093] As an embodiment of the present invention, the identifying the risk evaluation indicators of the pregnant women based on the physical examination data and the record of the previous medical history includes: analyzing the single data type of the physical examination data and identifying the normal parameter range corresponding to the single data type; extracting the abnormal parameters of the physical examination data according to the normal parameter range; analyzing the degree of variation of the abnormal parameters and, according to the degree of variation, analyzing the risk effect of the abnormal parameters on the pregnant women; identifying the current physical state of the pregnant women based on the record of the previous medical history; extracting the key physical sign parameters of the pregnant women according to the risk effect and the current physical state; and identifying the risk evaluation indicators of the pregnant women based on the key physical sign parameters.

[0094] Among them, the single data type refers to the attribute corresponding to each parameter in the physical examination data, such as blood pressure, urine protein, liver function, etc. The normal parameter range refers to the normal value range of medically recognized healthy adults for specific parameters. For example, a systolic blood pressure of 90 - 120 mmHg and a diastolic blood pressure of 60 - 80 mmHg are normal. The abnormal parameters refer to the physical examination data that exceeds the normal parameter range. The degree of variation refers to the degree of deviation of the abnormal parameters from the normal range. The risk effect refers to the proportion of the role of the abnormal parameters in the health risk of pregnant women. The current physical state refers to the current health status of the patient comprehensively evaluated based on the physical examination data and the medical history record. The key physical sign parameters refer to the physical examination parameters that are crucial for the risk assessment of pregnant women, such as continuously elevated blood pressure.

[0095] Optionally, the analysis of the degree of variation of the abnormal parameters can be determined by the standard deviation between the normal parameter value and the abnormal parameter value. The analysis of the risk effect of the abnormal parameters on the pregnant women according to the degree of variation can be realized by using the Kaplan - Meier survival analysis method. The identification of the current physical state of the pregnant women based on the record of the previous medical history can be obtained by using the time series analysis method.

[0096] S2. Identify the physical sign parameters corresponding to the risk assessment indicators, extract the time series data of the physical sign parameters, analyze the change trend of the physical sign parameters based on the time series data, and identify the parameter change pattern of the physical sign parameters according to the change trend.

[0097] In the embodiment of the present invention, by identifying the physical sign parameters corresponding to the risk assessment indicators and extracting the time series data of the physical sign parameters, the future health status and disease risks of pregnant women can be predicted, and it helps to establish an early warning system to timely detect the health changes of pregnant women. The physical sign parameters refer to the key physiological measurement values that can reflect an individual's health status, such as blood pressure parameters. The time series data refers to the data points of the physical sign parameters recorded in chronological order.

[0098] Optionally, the identification of the physical sign parameters corresponding to the risk assessment indicators can be obtained through laboratory tests, and the extraction of the time series data of the physical sign parameters can be realized by using data acquisition software, such as LabVIEW software.

[0099] Furthermore, in the embodiment of the present invention, by analyzing the change trend of the physical sign parameters based on the time series data, the parameter change pattern of the physical sign parameters of pregnant women can be identified according to the trend change law, providing a feature reference for subsequent prediction of pregnancy-induced hypertension risks. The change trend refers to the development direction and pattern of the physical examination parameters within a certain time range, such as a rapid increase in blood pressure.

[0100] Optionally, the analysis of the change trend of the physical sign parameters based on the time series data can be realized by using the ARIMA time series analysis model.

[0101] In the embodiment of the present invention, by identifying the parameter change pattern of the physical sign parameters according to the change trend, it can help to evaluate the risk of pregnant women developing into specific diseases and predict the development trend of the risks. The parameter change pattern refers to the regular, trend or periodic change characteristics shown by the physical sign parameters over time, such as circadian rhythm.

[0102] As an embodiment of the present invention, the identification of the parameter change pattern of the physical sign parameters according to the change trend includes: identifying the regression trend and divergence pattern of the physical sign parameters according to the change trend; extracting the key points of abnormal change of the physical sign parameters based on the regression trend and the divergence pattern; defining the abnormal change stage of the physical sign parameters according to the key points of abnormal change, and analyzing the stage change situation of the physical sign parameters in the abnormal change stage; identifying the change cycle of the physical sign parameters based on the stage change situation; analyzing the trend characteristics of the physical sign parameters under the change cycle; and identifying the parameter change pattern of the physical sign parameters by combining the stage change situation, the change cycle and the trend characteristics.

[0103] Among them, the regression trend refers to the trend that the physical sign parameters gradually return to the normal range or the expected level as time changes. The divergence mode refers to the trend that the physical sign parameters deviate from the normal range or the expected level as time changes. The key mutation point refers to the key time point or data point in the change trend of the physical sign parameters. The mutation stage refers to different stages in the change process of the physical sign parameters, such as the initial stage, the middle stage, the severe stage, etc. The stage change situation refers to the specific change situation of the physical sign parameters in the mutation stage, including the direction, speed, and amplitude of the change. The change cycle refers to the periodic law of the change of the physical sign parameters. The trend feature refers to the specific attribute of the change trend of the physical sign parameters, such as linear or non-linear.

[0104] Optionally, according to the change trend, the recognition of the regression trend and divergence mode of the physical sign parameters can be achieved by using trend line analysis. Based on the regression trend and the divergence mode, the extraction of the key mutation points of the physical sign parameters can be obtained through an anomaly detection algorithm. Based on the stage change situation, the recognition of the change cycle of the physical sign parameters can be achieved by using Fourier transform.

[0105] S3. Extract the classification index features of the risk evaluation index. Based on the classification index features and the parameter change pattern, identify the potential hypertensive disease features of the pregnant women. According to the potential hypertensive disease features, analyze the pregnancy-induced hypertension risk of the pregnant women.

[0106] In the embodiment of the present invention, by extracting the classification index features of the risk evaluation index, a more accurate prediction model can be constructed, and the accuracy of pregnancy-induced hypertension risk prediction can be improved. The classification index features refer to the characteristics of physiological, biochemical, or clinical parameters that can divide patients into different risk categories, such as low hemoglobin level, rapid weight gain, etc.

[0107] Optionally, the extraction of the classification index features of the risk evaluation index can be achieved by using the analytic hierarchy process.

[0108] Furthermore, in the embodiment of the present invention, by identifying the potential hypertensive disease features of the pregnant women based on the classification index features and the parameter change pattern, it is helpful to construct a risk prediction model for pregnant women, so as to identify patients with a high pregnancy-induced hypertension risk in advance and provide them with personalized medical advice and treatment plans, improving the health level of the patients. The potential hypertensive disease features refer to the features that can indicate the existence, development, or possible risk of pregnancy-induced hypertension disease, such as an increase in the level of inflammatory markers in the blood.

[0109] As an embodiment of the present invention, identifying the potential hypertensive disease characteristics of the pregnant woman based on the classification index features and the parameter change pattern includes: collecting the associated data of the classification index features and the parameter change pattern; analyzing the blood pressure condition of the pregnant woman based on the associated data; identifying the index parameters corresponding to the blood pressure condition according to the classification index features; identifying the parameter change characteristics of the index parameters based on the parameter change pattern; analyzing the blood pressure abnormality risk of the pregnant woman according to the parameter change characteristics; identifying the blood pressure risk type of the pregnant woman based on the blood pressure abnormality risk; and identifying the potential hypertensive disease characteristics of the pregnant woman according to the risk type.

[0110] Wherein, the associated data refers to medical data associated with the classification index features and the parameter change pattern, the blood pressure condition refers to the current blood pressure state of the pregnant woman, the index parameters refer to specific physiological or biochemical indicators used to evaluate the blood pressure condition, the parameter change characteristics refer to the characteristics of the index parameters changing over time, such as the increasing or decreasing trend of blood pressure readings, the blood pressure abnormality risk refers to the risk that the pregnant woman develops abnormal blood pressure (such as hypertension) based on the index parameters and the parameter change characteristics, and the blood pressure risk type refers to different disease types classified for the pregnant woman according to the blood pressure abnormality risk assessment results, such as hypertension and hypotension.

[0111] Optionally, the analysis of the blood pressure condition of the pregnant woman based on the associated data can be determined by a regression analysis model, the analysis of the blood pressure abnormality risk of the pregnant woman according to the parameter change characteristics can be realized by using a risk analysis model, and the identification of the blood pressure risk type of the pregnant woman based on the blood pressure abnormality risk can be determined by the blood pressure measurement value.

[0112] By analyzing the pregnancy-induced hypertension risk of the pregnant woman according to the potential hypertensive disease characteristics in the embodiment of the present invention, it is possible to identify and intervene in the early stage of the development of pregnancy-induced hypertension, thereby preventing the occurrence of the disease or reducing its severity and improving the maternal and perinatal outcomes. The pregnancy-induced hypertension risk refers to a disease in which the pregnant woman has symptoms such as hypertension, proteinuria, and edema.

[0113] As an embodiment of the present invention, analyzing the pregnancy-induced hypertension risk of the pregnant woman according to the potential hypertension disease characteristics includes: identifying the potential hypertension risk of the pregnant woman according to the potential hypertension disease characteristics; analyzing the key influencing factors of the potential hypertension risk; identifying the potential symptoms of the pregnant woman according to the key influencing factors; collecting the blood pressure characteristic data of the pregnant woman; combining the key influencing factors and the potential symptoms to perform a risk assessment on the blood pressure characteristic data to obtain a risk assessment result; calculating the reliability coefficient of the risk assessment result; defining the result interpretation of the risk assessment result according to the reliability coefficient; and analyzing the pregnancy-induced hypertension risk of the pregnant woman in combination with the risk analysis model and the result interpretation.

[0114] Among them, the potential hypertension risk refers to the disease phenomenon that pregnant women may have symptoms such as hypertension, proteinuria, and edema. The key influencing factors refer to the factors that have a significant impact on the hypertension risk of pregnant women, such as a history of pregnancy-induced hypertension in the past. The potential symptoms refer to the symptoms that may indicate the pregnancy-induced hypertension risk, such as upper abdominal pain. The blood pressure characteristic data refers to the blood pressure measurement data of pregnant women at different time points. The risk assessment result refers to the conclusion of the possibility that a pregnant woman develops pregnancy-induced hypertension. The reliability coefficient refers to the statistical index for evaluating the credibility of the risk assessment result. The result interpretation refers to the explanation and meaning description of the risk assessment result.

[0115] Optionally, the analysis of the key influencing factors of the potential hypertension risk can be achieved by using the multi-factor analysis method. The risk assessment of the blood pressure characteristic data by combining the key influencing factors and the potential symptoms can be achieved through a deep learning model, such as a convolutional neural network model. The definition of the result interpretation of the risk assessment result according to the reliability coefficient can be achieved by using a visual display icon.

[0116] In an alternative embodiment of the present invention, the reliability coefficient of the risk assessment result is calculated using the following formula:

[0117]

[0118] Among them, r represents the reliability coefficient of the risk assessment result, n represents the total number of test samples corresponding to the risk assessment result, z i represents the true value of the i-th test sample, represents the predicted value of the i-th test sample, and i represents the index of a single test sample corresponding to the risk assessment result.

[0119] S4. Analyze the formation causes of the pregnancy-induced hypertension risk, extract the risk factors of the pregnancy-induced hypertension risk, identify the proportion of the role of the risk factors in the pregnancy-induced hypertension risk, analyze the risk occurrence probability of the risk factors according to the formation causes, and combine the proportion of the role and the risk occurrence probability to identify the risk level of the risk factors.

[0120] In an embodiment of the present invention, by analyzing the formation causes of the pregnancy-induced hypertension risk, the occurrence and development mechanism of the pregnancy-induced hypertension disease can be understood, providing a theoretical basis for subsequent prevention and treatment. The formation causes refer to various factors that promote the occurrence of pregnancy-induced hypertension, such as genetic factors, placental factors, immune factors, etc.

[0121] As an embodiment of the present invention, the analysis of the formation causes of the pregnancy-induced hypertension risk includes: obtaining the patients with the pregnancy-induced hypertension risk and their corresponding medical data; based on the medical data, identifying the pathological characteristics of the patients; according to the pathological characteristics, performing gene heterogeneity analysis on the patients to obtain the gene heterogeneity analysis results; according to the medical data, analyzing the population characteristics of the pregnancy-induced hypertension risk, and based on the population characteristics, analyzing the influencing factors of the pregnancy-induced hypertension risk; identifying the mutual relationship between the influencing factors, and according to the mutual relationship, analyzing the individual effect and the synergistic effect of the influencing factors on the pregnancy-induced hypertension risk; combining the gene heterogeneity analysis results, the individual effect and the synergistic effect to analyze the formation causes of the pregnancy-induced hypertension risk.

[0122] Among them, the patients refer to women in pregnancy with the pregnancy-induced hypertension risk. The medical data refer to all records and information related to the health of the patients. The pathological characteristics refer to the pathological indexes related to pregnancy-induced hypertension identified from the medical data, such as abnormally elevated blood pressure. The gene heterogeneity analysis results refer to the results of genetic variation and gene expression patterns of the patients obtained through gene detection and analysis. The population characteristics refer to the common characteristics of the pregnancy-induced hypertension risk group, such as age distribution, pregnancy duration, etc. The influencing factors refer to various factors that may cause the pregnancy-induced hypertension risk, such as chronic diseases, genetic factors. The mutual relationship refers to the correlation between the influencing factors, such as genetic factors may interact with environmental exposure. The individual effect refers to the independent influence of a single influencing factor on the pregnancy-induced hypertension risk. The synergistic effect refers to the enhanced effect on the pregnancy-induced hypertension risk when two or more influencing factors act together.

[0123] Optionally, the identification of the patient's pathological features based on the medical data can be obtained through laboratory tests and imaging examinations. The identification of the interrelationships between the influencing factors based on the pathological features can be achieved using the correlation coefficient method. The analysis of the patient's genetic heterogeneity can be determined by detecting the differences in the expression levels of genes in different individuals or tissues. The analysis of the synergistic effects of the influencing factors on the risk of pregnancy-induced hypertension based on the interrelationships can be achieved using interaction analysis. For example, in a regression model, two or more influencing factors and their interaction terms are simultaneously included. If the coefficient of the interaction term is statistically significant, it indicates the existence of a synergistic effect between these factors.

[0124] Furthermore, by extracting the risk factors for the risk of pregnancy-induced hypertension in the embodiments of the present invention, it can help medical professionals assess the risks of pregnant women and take appropriate preventive and intervention measures. The risk factors refer to factors that may increase the likelihood of pregnant women developing pregnancy-induced hypertension diseases, such as metabolic syndrome.

[0125] Optionally, the extraction of the risk factors for the risk of pregnancy-induced hypertension can be achieved using a recurrent neural network.

[0126] By identifying the proportion of the role of the risk factors in the risk of pregnancy-induced hypertension in the embodiments of the present invention, the probability of the occurrence of pregnancy-induced hypertension can be predicted more accurately, so as to take corresponding preventive and intervention measures to reduce the risks of pregnant women and fetuses. The proportion of the role refers to the intensity or importance of each risk factor in the risk of the occurrence of pregnancy-induced hypertension.

[0127] Optionally, the identification of the proportion of the role of the risk factors in the risk of pregnancy-induced hypertension can be determined by the importance analysis method, such as the key factor analysis method.

[0128] Furthermore, by analyzing the probability of the occurrence of the risk of the risk factors according to the formation reasons in the embodiments of the present invention, the likelihood of pregnant women suffering from pregnancy-induced hypertension can be evaluated more accurately to help doctors take closer monitoring and targeted preventive measures. The probability of the occurrence of the risk refers to the frequency or likelihood degree of the risk event occurring within a certain period of time or under specific circumstances.

[0129] As an embodiment of the present invention, analyzing the risk occurrence probability of the risk factor according to the formation cause includes: analyzing the health outcome of the risk factor according to the formation cause; identifying the risk event corresponding to the risk factor based on the health outcome; analyzing the event type of the risk event and defining the risk turning point of the event type; determining the risk occurrence condition of the risk factor according to the risk turning point; identifying the occurrence frequency of the risk factor based on the risk occurrence condition; and analyzing the risk occurrence probability of the risk factor according to the risk occurrence condition and the occurrence frequency.

[0130] Wherein, the health outcome refers to the health state or result that may be produced due to the action of the risk factor, such as pregnancy-induced hypertension. The risk event refers to a specific event or condition related to a specific health outcome, such as preeclampsia. The event type refers to different classifications or categories of risk events. The risk turning point refers to a quantitative index that has a significant impact on the severity or occurrence probability of the health outcome, such as the appearance of proteinuria in urine tests. The risk occurrence condition refers to the specific conditions or environment required for the occurrence of the risk event. The occurrence frequency refers to the number of times the risk factor appears within a certain period of time.

[0131] Optionally, the identification of the risk event corresponding to the risk factor based on the health outcome can be determined through medical examinations, such as urine protein level tests. The definition of the risk turning point of the event type can be achieved by using the physiological and pathological mechanisms of the event type. The identification of the occurrence frequency of the risk factor based on the risk occurrence condition can be obtained by setting monitoring alarms, such as setting an alarm prompt for abnormal blood glucose on a blood glucose meter.

[0132] By combining the action ratio and the risk occurrence probability, the present invention embodiment can identify the risk level of the risk factor, optimize the monitoring and management of pregnancy-induced hypertension patients, and reduce the occurrence risk of pregnancy-induced hypertension. The risk level refers to the risk level divided according to the possibility of risk occurrence and the degree of impact caused by the risk.

[0133] Optionally, the identification of the risk level of the risk factor by combining the action ratio and the risk occurrence probability can be obtained through a decision tree model.

[0134] S5. Based on the risk level, extract the key risk factors of the pregnancy-induced hypertension risk. According to the key risk factors, identify the risk formation mechanism of the pregnancy-induced hypertension risk. Based on the risk formation mechanism, set up the risk warning network for the pregnancy-induced hypertension risk.

[0135] In the embodiments of the present invention, by extracting the key risk factors of pregnancy-induced hypertension based on the risk level, it can help medical professionals provide more accurate preventive measures for pregnant women with different risk levels, reduce the occurrence and development of pregnancy-induced hypertension. The key risk factors refer to the factors that play an important role in the development of pregnancy-induced hypertension, such as multiple pregnancy.

[0136] Optionally, based on the risk level, the extraction of the key risk factors of pregnancy-induced hypertension can be obtained by the principal component analysis method.

[0137] Furthermore, in the embodiments of the present invention, by identifying the risk formation mechanism of pregnancy-induced hypertension according to the key risk factors, it can help to detect high-risk pregnant women at an early stage and take corresponding preventive and intervention measures to reduce the risk of occurrence of pregnancy-induced hypertension and improve the prognosis of the mother and baby. The risk formation mechanism refers to the cause and process of the formation of pregnancy-induced hypertension.

[0138] As an embodiment of the present invention, the identification of the risk formation mechanism of pregnancy-induced hypertension according to the key risk factors includes: identifying the pregnant women with pregnancy-induced hypertension corresponding to the risk of pregnancy-induced hypertension; analyzing the mutual influence relationship between the key risk factors; identifying the comprehensive effect of the key risk factors on the pregnant women with pregnancy-induced hypertension; analyzing the change characteristics of the physiological indexes of the pregnant women with pregnancy-induced hypertension according to the mutual influence relationship and the comprehensive effect; identifying the body reaction of the pregnant women with pregnancy-induced hypertension based on the change characteristics of the physiological indexes; analyzing the pathological pattern of the risk of pregnancy-induced hypertension according to the change characteristics of the physiological indexes and the body reaction; combining the change characteristics of the physiological indexes, the body reaction and the pathological pattern to identify the risk formation mechanism of the risk of pregnancy-induced hypertension.

[0139] Among them, the pregnant women with pregnancy-induced hypertension refer to the pregnant women who have hypertension symptoms during pregnancy. The mutual influence relationship refers to the interaction and mutual influence between different risk factors. The comprehensive effect refers to the result effect of multiple risk factors acting on the pregnant women with pregnancy-induced hypertension. The change characteristics of the physiological indexes refer to the change pattern of the physiological parameters in the pregnant women with pregnancy-induced hypertension, such as continuous increase in blood pressure. The body reaction refers to the biological reaction of the pregnant women with pregnancy-induced hypertension to physiological changes, such as vasoconstriction and kidney function changes. The pathological pattern refers to the pathophysiological characteristics of pregnancy-induced hypertension, such as vascular endothelial dysfunction and inflammatory reaction.

[0140] Optionally, the identification of the comprehensive effect of the key risk factors on the pregnant women with pregnancy-induced hypertension can be realized by using the multivariate statistical analysis method. The analysis of the change characteristics of the physiological indexes of the pregnant women with pregnancy-induced hypertension according to the mutual influence relationship and the comprehensive effect can be realized by using the SPSS analysis model.

[0141] In an embodiment of the present invention, by setting up a risk early warning network for pregnancy-induced hypertension based on the risk formation mechanism, pregnant women can be effectively monitored and warned, the risks brought by pregnancy-induced hypertension can be reduced, thereby improving the prevention and treatment level of pregnancy-induced hypertension, ensuring the health of the mother and baby. The risk early warning network refers to a comprehensive system for monitoring, analyzing, and warning of the risks of pregnancy-induced hypertension.

[0142] As an embodiment of the present invention, setting up the risk early warning network for pregnancy-induced hypertension based on the risk formation mechanism includes: collecting physiological index data corresponding to the risk of pregnancy-induced hypertension and identifying the pregnant women corresponding to the physiological index data; extracting abnormal index characteristics of the physiological index data based on the risk formation mechanism; constructing a physiological index monitoring network for the pregnant women according to the abnormal index characteristics; identifying personalized index characteristics of the pregnant women based on the physiological index monitoring network; defining a personalized early warning mechanism for the pregnant women according to the personalized index characteristics; and setting up the risk early warning network for the risk of pregnancy-induced hypertension based on the physiological index monitoring network and the personalized early warning mechanism.

[0143] Among them, the physiological index data refers to various physiological measurement data of pregnant women during pregnancy, the abnormal index characteristics refer to physiological indexes that deviate significantly within the normal physiological range, the physiological index monitoring network refers to a system or tool for continuously tracking and recording the physiological indexes of pregnant women, the personalized index characteristics refer to the physiological index reference ranges customized according to the specific conditions of each pregnant woman (such as age, weight, pre-pregnancy health status, etc.), and the personalized early warning mechanism refers to an early warning system set according to the personalized index characteristics of each pregnant woman.

[0144] Optionally, the construction of the physiological index monitoring network for the pregnant women can be obtained through medical monitoring equipment according to the abnormal index characteristics, the identification of the personalized index characteristics of the pregnant women can be defined by using the historical health data of the pregnant women based on the physiological index monitoring network, and the definition of the personalized early warning mechanism for the pregnant women can be obtained through a recurrent neural network according to the personalized index characteristics.

[0145] S6. Combining the risk formation mechanism and the risk early warning network, performing risk analysis on the risk of pregnancy-induced hypertension to obtain a risk analysis result.

[0146] In the embodiments of the present invention, by combining the risk formation mechanism and the risk early warning network, risk analysis is performed on the pregnancy-induced hypertension risk, and a risk analysis result is obtained, which can achieve accurate prediction and early warning of the pregnancy-induced hypertension risk, help doctors understand the health status of pregnant women more comprehensively, formulate more effective prevention and treatment strategies, reduce the incidence of pregnancy-induced hypertension and the incidence of complications, and improve the health level of the mother and baby. The risk analysis result refers to the conclusion or report obtained after identifying, evaluating, and analyzing pregnancy-induced hypertension events or situations, such as the degree of influence of the pregnancy-induced hypertension risk, the driving factors of the pregnancy-induced hypertension risk, etc.

[0147] Compared with the problems described in the background art, in the embodiments of the present invention, by identifying the risk assessment indicators of pregnant women based on the physical examination data and the previous medical history records, the development trend of pregnancy-induced hypertension in pregnant women can be predicted, which helps to formulate effective risk prevention measures; further, in the embodiments of the present invention, by analyzing the change trend of the physical sign parameters based on the time series data, the change pattern of the physical sign parameters of pregnant women can be identified according to the trend change law, providing a feature reference for subsequent pregnancy-induced hypertension risk prediction to identify the parameter change pattern of the physical sign parameters, helping to evaluate the risk of pregnant women developing specific diseases and predicting the development trend of the risk; secondly, in the embodiments of the present invention, by identifying the potential hypertension disease characteristics of pregnant women based on the classification index characteristics and the parameter change pattern, it helps to construct a risk prediction model for pregnant women to identify patients with a high risk of pregnancy-induced hypertension in advance and provide them with personalized medical advice and treatment plans, improving the health level of patients, and analyzing the pregnancy-induced hypertension risk of pregnant women according to the potential hypertension disease characteristics, which can be identified and intervened at the initial stage of the development of pregnancy-induced hypertension, thereby preventing the occurrence of the disease or reducing its severity and improving the maternal and perinatal outcomes; thirdly, in the embodiments of the present invention, by analyzing the causes of the formation of the pregnancy-induced hypertension risk, the occurrence and development mechanism of pregnancy-induced hypertension disease can be understood, providing a theoretical basis for subsequent prevention and treatment, and analyzing the risk occurrence probability of the risk factors according to the formation causes to more accurately evaluate the possibility of pregnant women suffering from pregnancy-induced hypertension, so as to help doctors take closer monitoring and targeted prevention measures; in addition, in the embodiments of the present invention, according to the key risk factors, identifying the risk formation mechanism of the pregnancy-induced hypertension risk can help to detect high-risk pregnant women early and take corresponding prevention and intervention measures to reduce the occurrence risk of pregnancy-induced hypertension, improve the maternal and child prognosis, and based on the risk formation mechanism, setting up a risk warning network for the pregnancy-induced hypertension risk can effectively monitor and warn pregnant women, reducing the risks brought by pregnancy-induced hypertension, thereby improving the prevention and treatment level of pregnancy-induced hypertension and ensuring the health of the mother and child; finally, in the embodiments of the present invention, by combining the risk formation mechanism and the risk warning network, performing risk analysis on the pregnancy-induced hypertension risk to obtain a risk analysis result, the accurate prediction and early warning of the pregnancy-induced hypertension risk can be realized, which helps doctors to more comprehensively understand the health status of pregnant women and formulate more effective prevention and treatment strategies to reduce the incidence rate and complication rate of pregnancy-induced hypertension and improve the health level of the mother and child. Therefore, the pregnancy-induced hypertension risk prediction method and system based on deep learning provided by the embodiments of the present invention can improve the accuracy and efficiency of pregnancy-induced hypertension risk prediction.

[0148] Example 2:

[0149] Such as Figure 2As shown, it is a functional module diagram of a pregnancy-induced hypertension risk prediction system based on deep learning according to the present invention.

[0150] The pregnancy-induced hypertension risk prediction system 200 based on deep learning according to the present invention can be installed in an electronic device. According to the implemented functions, the pregnancy-induced hypertension risk prediction system based on deep learning can include a risk index identification module 201, a physical sign pattern analysis module 202, a potential risk analysis module 203, a key risk factor extraction module 204, a risk processing module 205, and a risk prediction module 206. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0151] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0152] The risk index identification module 201 is used to obtain pregnant women to be analyzed, collect the physical examination data and past medical history records of the pregnant women, and identify the risk evaluation indicators of the pregnant women based on the physical examination data and the past medical history records;

[0153] The physical sign pattern analysis module 202 is used to identify the physical sign parameters corresponding to the risk evaluation indicators, extract the time series data of the physical sign parameters, analyze the change trend of the physical sign parameters based on the time series data, and identify the parameter change pattern of the physical sign parameters according to the change trend.

[0154] The potential risk analysis module 203 is used to extract the classification index features of the risk evaluation indicators, identify the potential hypertension disease characteristics of the pregnant women based on the classification index features and the parameter change pattern, and analyze the pregnancy-induced hypertension risk of the pregnant women according to the potential hypertension disease characteristics;

[0155] The key risk factor extraction module 204 is used to analyze the formation reasons of the pregnancy-induced hypertension risk, extract the risk factors of the pregnancy-induced hypertension risk, identify the proportion of the role of the risk factors in the pregnancy-induced hypertension risk, analyze the risk occurrence probability of the risk factors according to the formation reasons, and identify the risk level of the risk factors by combining the proportion of the role and the risk occurrence probability;

[0156] The risk processing module 205 is used to extract the key risk factors of the pregnancy-induced hypertension risk based on the risk level, identify the risk formation mechanism of the pregnancy-induced hypertension risk according to the key risk factors, and set up a risk warning network for the pregnancy-induced hypertension risk based on the risk formation mechanism;

[0157] The risk prediction module 206 is configured to perform risk analysis on the pregnancy-induced hypertension risk by combining the risk formation mechanism and the risk early warning network, and obtain a risk analysis result.

[0158] Specifically, when the modules in the pregnancy-induced hypertension risk prediction system 200 based on deep learning in the embodiments of the present invention are used, they adopt the same technical means as those in the Figure 1 pregnancy-induced hypertension risk prediction method based on deep learning described above, and can produce the same technical effects, which will not be elaborated here.

[0159] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for predicting the risk of pregnancy-induced hypertension based on deep learning, characterized in that: The method comprises: Acquire a pregnant woman to be analyzed, collect physical examination data and medical history records of the pregnant woman, and identify risk assessment indicators of the pregnant woman based on the physical examination data and medical history records; Identify the physical sign parameters corresponding to the risk assessment indicators, extract time series data of the physical sign parameters, analyze the change trend of the physical sign parameters based on the time series data, and identify the parameter change pattern of the physical sign parameters according to the change trend; Extracting the classification index characteristics of the risk assessment index, identifying the potential hypertension disease characteristics of the pregnant woman based on the classification index characteristics and the parameter change pattern, and analyzing the pregnancy-induced hypertension risk of the pregnant woman according to the potential hypertension disease characteristics; Analyze the formation causes of the risk of gestational hypertension, extract the risk factors of the risk of gestational hypertension, identify the role ratio of the risk factors in the risk of gestational hypertension, analyze the risk probability of the risk factors according to the formation causes, and identify the risk level of the risk factors by combining the role ratio and the risk probability; Based on the risk level, extract the key risk factors of the risk of pregnancy-induced hypertension, identify the risk formation mechanism of the risk of pregnancy-induced hypertension according to the key risk factors, and set up a risk early warning network for the risk of pregnancy-induced hypertension based on the risk formation mechanism; In combination with the risk formation mechanism and the risk early warning network, a risk analysis is performed on the risk of pregnancy-induced hypertension to obtain a risk analysis result.

2. The method for predicting pregnancy-induced hypertension risk based on deep learning according to claim 1, characterized in that: The step of identifying risk assessment indicators for the pregnant woman based on the physical examination data and the previous medical history records includes: Analyze the individual data types of the physical examination data, and identify the normal parameter ranges corresponding to the individual data types; Extracting abnormal parameters of the physical examination data according to the normal parameter range; Analyzing the degree of variation of the abnormal parameters, and analyzing the risk effect of the abnormal parameters on the pregnant woman according to the degree of variation; Based on the medical history records, identifying the current physical condition of the pregnant woman; Extracting key physical sign parameters of the pregnant woman according to the risk effect and the current physical condition; Based on the key physical sign parameters, risk assessment indicators for the pregnant woman are identified.

3. The method for predicting pregnancy-induced hypertension risk based on deep learning according to claim 1, characterized in that: The step of identifying the parameter change pattern of the vital sign parameter according to the change trend includes: According to the change trend, identifying the regression trend and divergence pattern of the physical sign parameters; Extracting the key points of abnormal changes of the physical sign parameters based on the regression trend and the divergence pattern; According to the mutation key points, defining the mutation stage of the physical sign parameters, and analyzing the stage-by-stage changes of the physical sign parameters in the mutation stage; Based on the stage-by-stage changes, identifying the change cycle of the vital sign parameters; Analyzing trend characteristics of the vital sign parameters in the change period; The parameter change pattern of the vital sign parameter is identified by combining the stage-by-stage change situation, the change cycle and the trend characteristics.

4. The method for predicting pregnancy-induced hypertension risk based on deep learning according to claim 1, characterized in that: The step of identifying potential hypertension characteristics of the pregnant woman based on the classification index characteristics and the parameter change pattern includes: Collecting the associated data of the classification index characteristics and the parameter change pattern; Analyzing the blood pressure of the pregnant woman based on the associated data; According to the classification indicator characteristics, identifying the indicator parameters corresponding to the blood pressure condition; Based on the parameter change pattern, identifying parameter change characteristics of the indicator parameter; Analyzing the risk of abnormal blood pressure of the pregnant woman according to the parameter change characteristics; Based on the risk of abnormal blood pressure, identifying the blood pressure risk type of the pregnant woman; Based on the risk type, potential hypertensive disease characteristics of the pregnant woman are identified.

5. The method for predicting pregnancy-induced hypertension risk based on deep learning according to claim 1, characterized in that: The step of analyzing the risk of gestational hypertension in the pregnant woman according to the potential characteristics of the hypertension disease includes: Identifying the potential risk of hypertension in the pregnant woman based on the potential hypertensive disease characteristics; Analyze the key influencing factors of the potential hypertension risk; Identify potential symptoms for the pregnant woman based on the key influencing factors; Collecting blood pressure characteristic data of the pregnant women; Combining the key influencing factors and the potential symptoms, performing risk assessment on the blood pressure characteristic data to obtain a risk assessment result; Calculating the reliability coefficient of the risk assessment result; Defining the interpretation of the risk assessment result according to the reliability coefficient; The risk of gestational hypertension in the pregnant women is analyzed by combining the risk analysis model and the result interpretation.

6. The method for predicting pregnancy-induced hypertension risk based on deep learning according to claim 1, characterized in that: The analysis of the causes of the risk of gestational hypertension includes: Obtaining patients at risk of gestational hypertension and their corresponding medical data; Based on the medical data, identifying pathological characteristics of the patient; According to the pathological characteristics, performing gene heterogeneity analysis on the patient to obtain a gene heterogeneity analysis result; Analyzing the population characteristics of the risk of gestational hypertension according to the medical data, and analyzing the influencing factors of the risk of gestational hypertension based on the population characteristics; Identify the interrelationships among the influencing factors, and analyze the individual effects and synergistic effects of the influencing factors on the risk of gestational hypertension based on the interrelationships; The causes of the risk of gestational hypertension are analyzed by combining the results of the genetic heterogeneity analysis, the individual effects and the synergistic effects.

7. The method for predicting risk of pregnancy-induced hypertension based on deep learning according to claim 1, characterized in that: The step of analyzing the risk occurrence probability of the risk factor according to the formation cause includes: Analyzing the health consequences of the risk factors according to the formation causes; Based on the health results, identifying risk events corresponding to the risk factors; Analyze the event type of the risk event and define the risk turning point of the event type; Determining the risk occurrence conditions of the risk factor according to the risk turning point; Based on the risk occurrence conditions, identifying the occurrence frequency of the risk factors; The risk occurrence probability of the risk factor is analyzed according to the risk occurrence condition and the occurrence frequency.

8. The method for predicting pregnancy-induced hypertension risk based on deep learning according to claim 1, characterized in that: The step of identifying the risk formation mechanism of the risk of pregnancy-induced hypertension according to the key risk factors comprises: identifying a patient with gestational hypertension corresponding to the risk of gestational hypertension; Analyze the mutual influence relationship between the key risk factors; Identifying the combined effects of the key risk factors on the patient with pregnancy-induced hypertension; Analyzing the changing characteristics of the physiological indicators of the patient with pregnancy-induced hypertension according to the mutual influence relationship and the comprehensive effect; Based on the change characteristics of the physiological indicators, identifying the physical response of the patient with pregnancy-induced hypertension; Analyzing the pathological pattern of the risk of pregnancy-induced hypertension according to the changing characteristics of the physiological indicators and the body response; The risk formation mechanism of the gestational hypertension risk is identified by combining the changing characteristics of the physiological indicators, the body reactions and the pathological patterns.

9. The method for predicting pregnancy-induced hypertension risk based on deep learning according to claim 1, characterized in that: The step of setting up a risk early warning network for the risk of pregnancy-induced hypertension based on the risk formation mechanism includes: Collecting physiological indicator data corresponding to the risk of pregnancy-induced hypertension, and identifying pregnant women corresponding to the physiological indicator data; Based on the risk formation mechanism, extracting abnormal indicator features of the physiological indicator data; According to the abnormal index characteristics, constructing a physiological index monitoring network for the pregnant woman; Based on the physiological indicator monitoring network, identifying personalized indicator characteristics of the pregnant woman; Defining a personalized early warning mechanism for the pregnant woman according to the personalized indicator characteristics; Based on the physiological index monitoring network and the personalized early warning mechanism, a risk early warning network for the risk of pregnancy-induced hypertension is set up.

10. A system for predicting pregnancy-induced hypertension risk based on deep learning, the system implementing the method according to claim 1, characterized in that: The system comprises: A risk indicator identification module is used to obtain a pregnant woman to be analyzed, collect the physical examination data and past medical history records of the pregnant woman, and identify the risk assessment indicators of the pregnant woman based on the physical examination data and the past medical history records; The physical sign pattern analysis module is used to identify the physical sign parameters corresponding to the risk assessment indicators, and extract the time series data of the physical sign parameters, analyze the change trend of the physical sign parameters based on the time series data, and identify the parameter change pattern of the physical sign parameters according to the change trend. A potential risk analysis module, used to extract the classification index characteristics of the risk assessment index, identify the potential hypertension disease characteristics of the pregnant woman based on the classification index characteristics and the parameter change pattern, and analyze the pregnancy hypertension risk of the pregnant woman according to the potential hypertension disease characteristics; A key risk factor extraction module is used to analyze the causes of the risk of gestational hypertension, extract the risk factors of the risk of gestational hypertension, identify the role of the risk factors in the risk of gestational hypertension, analyze the risk probability of the risk factors according to the causes, and identify the risk level of the risk factors in combination with the role proportion and the risk probability; A risk processing module, configured to extract key risk factors of the risk of pregnancy-induced hypertension based on the risk level, identify the risk formation mechanism of the risk of pregnancy-induced hypertension based on the key risk factors, and set a risk early warning network of the risk of pregnancy-induced hypertension based on the risk formation mechanism; The risk prediction module is used to combine the risk formation mechanism and the risk early warning network to perform risk analysis on the risk of pregnancy-induced hypertension and obtain risk analysis results.

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