Intelligent perinatal maternal and infant health monitoring and early warning system

Abstract

The invention discloses an intelligent perinatal maternal and infant health monitoring and early warning system, which relates to the technical field of medical health monitoring and comprises a data acquisition module, an evaluation module, an early warning module and a feedback module, the collected maternal and infant physiological data are transmitted to the data processing center in real time; the evaluation module obtains maternal and infant health data, constructs a maternal and infant health evaluation model by combining the maternal and infant physiological data and applying a machine learning algorithm, and calculates the maternal and infant physiological data deviation degree; the early warning module is used for comparing and analyzing the maternal and infant physiological data deviation degree with a preset threshold value, and sending out mild, moderate and severe early warning information according to a comparison and analysis result; and the feedback module is used for medical staff to check the early warning information and the maternal and infant physiological data deviation degree, formulate a personalized treatment scheme in combination with clinical experience and feed back through the system. According to the invention, the response speed to the maternal and infant health problem is improved, and the health risk is reduced.

Description

Technical Field

[0001] The present invention relates to the field of medical health monitoring technology, and more specifically, to an intelligent perinatal maternal and infant health monitoring and early warning system. Background Art

[0002] Perinatal maternal and infant health monitoring refers to the health monitoring and care of pregnant women and fetuses during the perinatal period to ensure the safety and health of mothers and infants during this critical period. The perinatal period refers to the period from 28 weeks of pregnancy to 42 days after delivery. This period is crucial to the health of mothers and infants, and any health problems may affect the life and health of mothers and infants. The perinatal period is the most vulnerable stage for mothers and infants. Timely and effective monitoring can help doctors identify potential health problems and conduct early intervention, thereby reducing the risks of maternal and infant mortality, premature birth, low birth weight, postpartum complications, etc., and ensuring that mothers and infants spend this critical period safely and healthily. Through perinatal health monitoring, pregnancy complications, delivery risks, and postpartum health problems can be minimized, maternal and infant health can be promoted, and the arrival of new life and the smooth recovery of pregnant women can be guaranteed.

[0003] Deficiencies of existing technologies:

[0004] Existing technologies may not be able to collect and monitor maternal and infant physiological data in real time and continuously, resulting in some sudden physiological changes not being discovered in time. The data acquisition module of this system collects and transmits maternal and infant physiological data in real time, which solves this problem and can capture the dynamic changes of maternal and infant physiological status in time. Traditional maternal and infant health assessment may rely only on limited indicators or empirical judgments, lacking scientific quantitative analysis and comprehensive considerations. The evaluation module uses machine learning algorithms to combine a variety of maternal and infant health data to build an evaluation model, calculate the deviation of physiological data, and achieve a more accurate assessment of maternal and infant health status, avoiding misjudgment or missed judgments that may be caused by relying solely on experience. Previous early warning mechanisms may not have clear quantitative standards, and the timeliness and accuracy of early warnings are insufficient. The early warning module compares and analyzes the deviation of physiological data with the preset threshold, and can accurately issue mild, moderate and severe early warning information according to the degree of deviation, which improves the timeliness and accuracy of early warnings and enables medical staff to take corresponding measures more promptly. Existing technologies may find it difficult to formulate personalized treatment plans based on the specific conditions of each mother and child, and often adopt more general treatment methods. The feedback module allows medical staff to develop personalized treatment plans based on early warning information, physiological data deviations and clinical experience, which solves this problem and can better meet the individual health needs of mothers and babies.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] To overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an intelligent perinatal maternal and child health monitoring and early warning system, which monitors and warns the health of perinatal mothers and children to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An intelligent perinatal maternal and child health monitoring and early warning system includes a data acquisition module, an evaluation module, an early warning module, and a feedback module, and there are connections between the modules:

[0009] The data acquisition module is used to collect maternal and child physiological data and transmit the collected maternal and child physiological data to the data processing center in real time;

[0010] The evaluation module is used to obtain maternal and child health data, construct a maternal and child health evaluation model by using machine learning algorithms in combination with maternal and child physiological data, and calculate the deviation degree of maternal and child physiological data;

[0011] The early warning module is used to compare and analyze the deviation degree of maternal and child physiological data with a preset threshold, and automatically issue mild, moderate, and severe early warning information according to the comparison and analysis results;

[0012] The feedback module is used for medical staff to log in to the system, view the early warning information and the deviation degree of maternal and child physiological data, formulate a personalized treatment plan in combination with clinical experience, and feedback through the system.

[0013] In a preferred embodiment, the maternal and child physiological data includes pregnant woman physiological parameters and fetal physiological parameters;

[0014] The pregnant woman physiological parameters include blood pressure fluctuation coefficient and hemoglobin concentration;

[0015] The fetal physiological parameters include fetal heart rate stability coefficient and growth and development index data.

[0016] In a preferred embodiment, the specific acquisition process of the blood pressure fluctuation coefficient is as follows:

[0017] Measure the systolic blood pressure and diastolic blood pressure of the pregnant woman;

[0018] Compare the systolic blood pressure and diastolic blood pressure measured each time with the normal reference range, and record the number of times the systolic blood pressure and diastolic blood pressure exceed the normal range respectively;

[0019] Record the values of the systolic blood pressure and diastolic blood pressure that exceed the normal range each time, and calculate the blood pressure fluctuation coefficient in combination with the number of times the systolic blood pressure and diastolic blood pressure exceed the normal range. The specific calculation formula is as follows:

[0020]

[0021] Wherein, X is the blood pressure fluctuation coefficient, S and D are respectively the number of times that the systolic blood pressure and the diastolic blood pressure exceed the normal range, and ΔM SBP and ΔM DBP are respectively the values by which the systolic blood pressure and the diastolic blood pressure exceed the normal range. is the systolic blood pressure measured at time t i , and is the diastolic blood pressure measured at time t i .

[0022] In a preferred embodiment, the process for obtaining the hemoglobin concentration is as follows:

[0023] Obtain the hemoglobin mass through a complete blood count test;

[0024] Obtain the weight of the pregnant woman and estimate the blood volume based on the weight;

[0025] Divide the hemoglobin mass by the blood volume to obtain the hemoglobin concentration. The specific calculation formula is as follows:

[0026]

[0027] Wherein, is the hemoglobin concentration at time t i , is the hemoglobin mass at time t i , and is the weight of the pregnant woman at the time.

[0028] In a preferred embodiment, the process for specifically obtaining the fetal heart rate stability coefficient is as follows:

[0029] Use an ultrasonic Doppler fetal heart rate monitor to continuously collect fetal heart rate data at regular time intervals, which are respectively data points

[0030] Calculate the average fetal heart rate based on the data points, and calculate the fetal heart rate stability coefficient in combination with the fetal heart rate values and the number of data points of the data points. The specific calculation formula is as follows:

[0031]

[0032] Wherein, is the fetal heart rate stability coefficient, is the fetal heart rate value collected at time t i , n is the number of fetal heart rate data points collected, and is the average fetal heart rate.

[0033] In a preferred embodiment, the process for obtaining the growth and development index data is as follows:

[0034] At different time points t iand t i+1 Measure the biparietal diameter value and the femur length value of the fetus respectively;

[0035] According to the measured biparietal diameter value and femur length value of the fetus, calculate the ratio of the biparietal diameter to the femur length, and combine different time points t i and t i+1 Calculate the growth rate of the fetal biparietal diameter and the growth rate of the fetal femur length;

[0036] Calculate the growth and development index data according to the ratio of the biparietal diameter to the femur length, the growth rate of the fetal biparietal diameter and the growth rate of the fetal femur length. The specific formula is as follows:

[0037]

[0038] In the formula, I grow is the growth and development index data, and are the biparietal diameter values of the fetus at time points t i and t i+1

[0039] respectively, and are the femur length values of the fetus at time points t i and t i+1 respectively, u1 is the ratio weight of the biparietal diameter to the femur length, u2 is the growth rate weight of the fetal biparietal diameter, and u3 is the growth rate weight of the fetal femur length.

[0040] In a preferred embodiment, the maternal and child health data includes pregnant woman health data and fetal health data;

[0041] The maternal and child health assessment model includes a pregnant woman health assessment model and a fetal health assessment model;

[0042] The deviation degree of the maternal and child physiological data includes the deviation degree of the pregnant woman physiological data and the deviation degree of the fetal physiological data.

[0043] In a preferred embodiment, the construction of the pregnant woman health assessment model is as follows:

[0044] According to the blood pressure fluctuation coefficient and the hemoglobin concentration, combined with the pregnant woman health data, construct the pregnant woman health assessment model. The specific formula is as follows:

[0045]

[0046] In the formula, P mom is the deviation degree of the pregnant woman physiological data; w1, w2 are the weight coefficients of the blood pressure fluctuation coefficient and the hemoglobin concentration respectively, and are both greater than 0, obtained from the historical importance; is t iThe hemoglobin concentration at a certain moment, X is the blood pressure fluctuation coefficient, and N is the health data of pregnant women.

[0047] In a preferred embodiment, the process of constructing the fetal health assessment model is as follows:

[0048] Construct a fetal health assessment model based on the fetal heart rate stability coefficient, growth and development index data, and combined with fetal health data. The specific formula is:

[0049]

[0050] In the formula, P baby is the deviation degree of fetal physiological data, a1 and a2 are the fetal heart rate stability coefficients respectively, is the fetal heart rate stability coefficient, I grow is the growth and development index data, I s is the upper limit of the growth and development index data, I x is the lower limit of the growth and development index data.

[0051] In a preferred embodiment, the process of comparing and analyzing the deviation degree of maternal and fetal physiological data with a preset threshold and automatically sending out mild, moderate, and severe warning messages according to the comparison and analysis results is as follows:

[0052] Compare and analyze the deviation degree of pregnant women's physiological data and the deviation degree of fetal physiological data with the physiological data deviation threshold respectively;

[0053] If the deviation degree of pregnant women's physiological data is less than the pregnant women's physiological data deviation threshold and the deviation degree of fetal physiological data is less than the fetal physiological data deviation threshold, no warning notice will be sent, and continuous monitoring of the health of the mother and fetus will be carried out;

[0054] If the deviation degree of pregnant women's physiological data is less than the pregnant women's physiological data deviation threshold and the deviation degree of fetal physiological data is greater than the fetal physiological data deviation threshold, a mild warning notice will be sent;

[0055] If the deviation degree of pregnant women's physiological data is greater than the pregnant women's physiological data deviation threshold and the deviation degree of fetal physiological data is less than the fetal physiological data deviation threshold, a moderate warning notice will be sent;

[0056] If the deviation degree of pregnant women's physiological data is greater than the pregnant women's physiological data deviation threshold and the deviation degree of fetal physiological data is greater than the fetal physiological data deviation threshold, a severe warning notice will be sent, and medical staff will be immediately notified for diagnosis and treatment.

[0057] The technical effects and advantages of an intelligent perinatal maternal and fetal health monitoring and warning system of the present invention:

[0058] 1. The present invention improves the real-time performance and accuracy of monitoring and evaluating the health status of mothers and infants by collecting and transmitting their physiological data in real time, and by applying scientific evaluation models and accurate early warning mechanisms. It helps to detect potential health problems at an early stage and provides strong support for timely intervention. It can formulate personalized treatment plans based on the physiological data and health status of each mother and infant, improving the pertinence and effectiveness of medical services and better ensuring the health of mothers and infants. The system automatically collects, analyzes, and issues early warnings, reducing the workload of medical staff, enabling them to focus more on handling early warning situations and formulating treatment plans, and improving the efficiency of medical work. The data processing center can accumulate a large amount of physiological data and health assessment data of mothers and infants, which can provide valuable references for further medical research and clinical practice, promoting the development of the field of maternal and infant health. Timely and accurate early warnings and personalized treatment plans can effectively reduce the probability of health risks for mothers and infants, enhance the safety of mothers and infants during the medical process, and strengthen the trust of family members in medical services.

[0059] 2. The data collection module of the present invention can collect the physiological data of mothers and infants in real time and quickly transmit it to the data processing center to ensure the timeliness and accuracy of the data. It enables medical staff to promptly grasp the latest physiological conditions of mothers and infants, provides data support for timely detection of potential health problems, helps with early intervention and treatment, and improves the level of maternal and infant health protection. The evaluation module uses machine learning algorithms to build an evaluation model in combination with maternal and infant health data, accurately calculates the deviation degree of maternal and infant physiological data, and realizes the quantitative evaluation of the health status of mothers and infants. Compared with the traditional evaluation method relying on experience and limited indicators, it is more scientific and accurate, can avoid subjective judgment errors, and can more accurately identify the health risks of mothers and infants. The early warning module compares and analyzes the physiological data deviation degree with a preset threshold, and automatically issues early warning messages of different levels according to the results, with clear quantitative standards and a fast response mechanism. Medical staff can take corresponding measures in a timely manner according to the early warning information, improving the response speed to maternal and infant health problems, reducing health risks, and reducing the occurrence of adverse events. The feedback module supports medical staff to log in to the system to view relevant information, formulate personalized treatment plans in combination with clinical experience, and through system feedback, realizes the personalization of medical services. It can meet the health needs of different maternal and infant individuals, improve the treatment effect, enhance the quality of medical services and patient satisfaction. The system automatically completes tasks such as data collection, analysis, and early warning, reducing the burden on medical staff, enabling them to devote more energy to the diagnosis and treatment of patients. It improves the efficiency of medical work, optimizes the allocation of medical resources, and helps to provide high-quality services for more patients with limited medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic structural diagram of an intelligent perinatal maternal and infant health monitoring and early warning system of the present invention. Detailed implementation mode

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] Embodiment 1 Figure 1 An intelligent perinatal maternal and child health monitoring and early warning system of the present invention is given.

[0063] A data acquisition module is used to collect maternal and child physiological data and transmit the collected maternal and child physiological data to the data processing center in real time;

[0064] The maternal and child physiological data includes pregnant woman physiological parameters and fetal physiological parameters;

[0065] The pregnant woman physiological parameters include blood pressure fluctuation coefficient and hemoglobin concentration;

[0066] The specific acquisition process of the blood pressure fluctuation coefficient is as follows:

[0067] According to the set acquisition frequency, use an electronic sphygmomanometer, wrap the cuff around the appropriate position of the pregnant woman's upper arm, and measure the systolic blood pressure and diastolic blood pressure of the pregnant woman according to the instrument operation specifications;

[0068] And compare the systolic blood pressure and diastolic blood pressure measured each time with the normal reference range, and record the number of times that the systolic blood pressure and diastolic blood pressure exceed the normal range respectively;

[0069] And record the values of the systolic blood pressure and diastolic blood pressure that exceed the normal range each time, and calculate the blood pressure fluctuation coefficient in combination with the number of times that the systolic blood pressure and diastolic blood pressure exceed the normal range. The specific calculation formula is as follows:

[0070]

[0071] In the formula, X is the blood pressure fluctuation coefficient, S and D are the number of times that the systolic blood pressure and diastolic blood pressure exceed the normal range respectively, ΔM SBP and ΔM DBP are the values of the systolic blood pressure and diastolic blood pressure that exceed the normal range respectively, is the systolic blood pressure measured at time t i , is the diastolic blood pressure measured at time t i .

[0072] The acquisition process of the hemoglobin concentration is as follows:

[0073] Obtain the hemoglobin mass through blood routine detection;

[0074] Obtain the weight of the pregnant woman and estimate the blood volume based on the weight;

[0075] Divide the hemoglobin mass by the blood volume to obtain the hemoglobin concentration. The specific calculation formula is as follows:

[0076]

[0077] In the formula, is the hemoglobin concentration at time t i , is the hemoglobin mass at time t i , is the weight of the pregnant woman at the time.

[0078] Fetal physiological parameters include the fetal heart rate stability coefficient and growth and development index data;

[0079] The specific process for obtaining the fetal heart rate stability coefficient is as follows:

[0080] Use an ultrasonic Doppler fetal heart monitor to continuously collect fetal heart rate data at regular time intervals; during the collection process, ensure the correct use of the device and the accuracy of the data. A total of n data points are collected within a period of time, and these data points are respectively

[0081] Calculate the mean fetal heart rate based on these data points, and calculate the fetal heart rate stability coefficient in combination with the fetal heart rate value of the data point and the number of data points. The specific calculation formula is as follows:

[0082]

[0083] In the formula, is the fetal heart rate stability coefficient, is the fetal heart rate value collected at time t i , n is the number of fetal heart rate data points collected, is the mean fetal heart rate.

[0084] The process for obtaining the growth and development index data is as follows:

[0085] Use an ultrasonic diagnostic instrument to measure the biparietal diameter value and femur length value of the fetus at different time points t i and t i+1 respectively;

[0086] According to the measured biparietal diameter value and femur length value of the fetus, calculate the ratio of the biparietal diameter to the femur length, and calculate the growth rate of the fetal biparietal diameter and the growth rate of the fetal femur length in combination with different time points t i and t i+1 ;

[0087] Calculate the growth and development index data based on the ratio of the biparietal diameter to the femoral length, the growth rate of the fetal biparietal diameter, and the growth rate of the fetal femoral length. The specific formula is as follows:

[0088]

[0089] In the formula, I grow is the growth and development index data, and are the biparietal diameter values of the fetus at time points t i and t i+1

[0090] respectively, and are the femoral length values of the fetus at time points t i and t i+1 respectively. u1 is the weight of the ratio of the biparietal diameter to the femoral length, u2 is the weight of the growth rate of the fetal biparietal diameter, and u3 is the weight of the growth rate of the fetal femoral length;

[0091] It should be noted that the above weights are obtained by analyzing and calculating historical data.

[0092] The server of the data processing center is configured with corresponding network interfaces and data receiving programs for receiving data from the acquisition devices. After receiving a data frame, the server first unpacks the data to extract the original data and metadata information. Then, it uses a verification algorithm (such as cyclic redundancy check CRC) to verify the data and check whether errors occurred during data transmission. If the verification passes, the data is stored in the database of the server; if the verification fails, an error message is sent to the acquisition device to request retransmission of the data frame.

[0093] An evaluation module is used to obtain maternal and infant health data, construct a maternal and infant health evaluation model by combining maternal and infant physiological data using machine learning algorithms, and calculate the deviation degree of maternal and infant physiological data;

[0094] The maternal and infant health data includes pregnant woman health data and fetal health data;

[0095] The maternal and infant health evaluation model includes a pregnant woman health evaluation model and a fetal health evaluation model;

[0096] The deviation degree of maternal and infant physiological data includes the deviation degree of pregnant woman physiological data and the deviation degree of fetal physiological data;

[0097] Construct the pregnant woman health evaluation model as follows:

[0098] Construct a pregnant woman health evaluation model based on the blood pressure fluctuation coefficient and hemoglobin concentration, combined with the pregnant woman health data. The specific formula is as follows:

[0099]

[0100] Wherein, P mom is the deviation degree of the physiological data of the pregnant woman; w1 and w2 are the weight coefficients of the blood pressure fluctuation coefficient and the hemoglobin concentration respectively, and both are greater than 0, which are obtained from the historical importance; is t i the hemoglobin concentration at the moment, X is the blood pressure fluctuation coefficient, and N is the health data of the pregnant woman;

[0101] The process of constructing the fetal health assessment model is as follows:

[0102] According to the fetal heart rate stability coefficient and the growth and development index data, combined with the fetal health data, a fetal health assessment model is constructed. The specific formula is:

[0103]

[0104] Wherein, P baby is the deviation degree of the fetal physiological data, a1 and a2 are the fetal heart rate stability coefficient respectively, is the fetal heart rate stability coefficient, I grow is the growth and development index data, I s is the upper limit of the growth and development index data, I x is the lower limit of the growth and development index data.

[0105] The warning module is used to compare and analyze the deviation degree of the maternal and fetal physiological data with the preset threshold, and automatically send out mild, moderate and severe warning information according to the comparison and analysis results;

[0106] Compare and analyze the deviation degree of the pregnant woman's physiological data and the deviation degree of the fetal physiological data with the physiological data deviation threshold respectively;

[0107] If the deviation degree of the pregnant woman's physiological data is less than the deviation threshold of the pregnant woman's physiological data and the deviation degree of the fetal physiological data is less than the deviation threshold of the fetal physiological data, no warning notice is sent, and the health of the mother and fetus is continuously monitored;

[0108] If the deviation degree of the pregnant woman's physiological data is less than the deviation threshold of the pregnant woman's physiological data and the deviation degree of the fetal physiological data is greater than the deviation threshold of the fetal physiological data, a mild warning notice is sent;

[0109] If the deviation degree of the pregnant woman's physiological data is greater than the deviation threshold of the pregnant woman's physiological data and the deviation degree of the fetal physiological data is less than the deviation threshold of the fetal physiological data, a moderate warning notice is sent;

[0110] If the deviation degree of the pregnant woman's physiological data is greater than the deviation threshold of the pregnant woman's physiological data and the deviation degree of the fetal physiological data is greater than the deviation threshold of the fetal physiological data, a severe warning notice is sent, and the medical staff is immediately notified for diagnosis and treatment.

[0111] A feedback module for medical staff to log in to the system, view warning information and the deviation degree of maternal and infant physiological data, formulate personalized treatment plans based on clinical experience, and provide feedback through the system.

[0112] Medical staff open the login interface of the intelligent perinatal maternal and infant health monitoring and warning system, enter the pre-assigned username and password in the designated area, and the system will encrypt the entered username and password and compare them with the authorized information stored in the background database. To enhance security, some systems may also set up a secondary verification process, such as SMS verification codes, fingerprint recognition, or facial recognition. Only after successful verification can medical staff log in to the system and enter the main operation interface.

[0113] After successful login, the main interface of the system will display the latest warning information list in a prominent manner, clearly marking key information such as the occurrence time of each warning, the name of the pregnant woman, and the warning level (mild, moderate, severe). Medical staff can click on any warning record to enter the detailed information page.

[0114] On the detailed information page, the system not only presents the specific content of the warning but also shows the real-time values, historical change trends, and corresponding deviation degrees of various physiological data of the mother and infant in the form of a combination of charts and data tables. For example, a line chart is used to show the fluctuations of the fetal heart rate over a period of time and mark the deviation range from the normal range; a bar chart is used to compare the difference between the actual measured value of the pregnant woman's blood pressure and the normal threshold. At the same time, the system provides filtering and query functions to facilitate medical staff to quickly retrieve the required data according to conditions such as time and physiological parameter types.

[0115] Based on the deviation degrees of maternal and infant physiological data and warning information provided by the system, medical staff comprehensively evaluate the health status of the mother and infant in combination with their rich clinical experience. For example, when seeing that the deviation degree of fetal physiological data is greater than the threshold and a mild warning is prompted, medical staff will further check information such as the gestational age of the pregnant woman, previous medical history, and recent prenatal examination reports.

[0116] If the pregnant woman has no special medical history and is in the late pregnancy stage, medical staff will consider whether the fetus may have mild intrauterine hypoxia, umbilical cord around the neck, etc.; for a moderate warning situation where the deviation degree of the pregnant woman's physiological data is greater than the threshold, such as an increase in the pregnant woman's blood pressure, medical staff will combine the weight change of the pregnant woman, urine protein test results, etc. to determine whether she has gestational hypertension disease and evaluate the severity of the condition.

[0117] For mild warning situations, such as a slightly higher deviation in fetal physiological data but normal physiological data of the pregnant woman, medical staff will formulate targeted lifestyle adjustment suggestions. For example, it is recommended that the pregnant woman adjust her work and rest schedule to ensure sufficient sleep every day; change the diet structure, increase the intake of foods rich in protein and vitamins, and reduce the intake of high-salt and high-fat foods; appropriately increase the amount of exercise, such as doing 30 minutes of prenatal yoga or taking a walk every day, and elaborate on the precautions and intensity control of the exercise.

[0118] For moderate and severe warnings, medical staff will formulate corresponding medical intervention measures according to the specific condition. If it is diagnosed that the fetus has intrauterine hypoxia, it may be recommended that the pregnant woman undergo oxygen inhalation treatment, stipulate the time and frequency of oxygen inhalation every day, and arrange more frequent fetal heart rate monitoring; for pregnant women with gestational hypertension disorders, prescribe appropriate antihypertensive drugs, clarify the dosage, taking time and method of the drugs, and at the same time formulate a strict review plan, including the time interval of the review (such as once a week) and the items to be detected each time (such as blood pressure, blood routine, liver and kidney function, urine routine, etc.).

[0119] Considering the important impact of the psychological state of pregnant women during the perinatal period on the health of the mother and baby, psychological support content will be incorporated into the treatment plan. Medical staff will push psychological adjustment knowledge and relaxation techniques (such as deep breathing training, meditation guidance) to pregnant women through the system, and arrange professional psychological counselors for one-on-one psychological counseling according to the specific situation of the pregnant woman to help the pregnant woman relieve adverse emotions such as anxiety and tension.

[0120] After medical staff complete the formulation of the personalized treatment plan in the system, they click the send button. The system will send the treatment plan to the pregnant woman through multiple channels such as text messages, mobile application push, and emails according to the preferences pre-set by the pregnant woman. The text message content briefly summarizes the core points of the treatment plan, such as "There is a mild abnormality in your fetal heart rate. It is recommended to rest in the left lateral position every day and increase the frequency of fetal heart rate monitoring. For specific details, please check the mobile application notification".

[0121] The mobile application push will display the complete treatment plan in detail, including text descriptions, picture examples (such as pictures of the correct resting position), video tutorials (such as breathing training videos), etc.; relevant medical popular science materials will also be attached to the email to help the pregnant woman better understand the condition and treatment measures.

[0122] After the pregnant woman receives the treatment plan, she can confirm the operation in the system and communicate with medical staff in real time using the system's message, online consultation and other functions. The pregnant woman can raise questions about the treatment plan, such as the side effects of the drugs, the specific way of exercise, etc.; after receiving the message, medical staff will give professional and detailed answers and further guidance in a timely manner to ensure that the pregnant woman can correctly understand and effectively implement the treatment plan. At the same time, the system will completely record the communication records between medical staff and pregnant women, which is convenient for subsequent review and tracking of the treatment effect at any time.

[0123] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0124] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0125] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in this document can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0126] In addition, the functional modules in each embodiment of this application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0127] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

[0128] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent perinatal maternal and infant health monitoring and early warning system, characterized in that: It includes data collection module, evaluation module, early warning module and feedback module. There are connections between the modules: The data collection module is used to collect maternal and infant physiological data and transmit the collected maternal and infant physiological data to the data processing center in real time; The evaluation module is used to obtain maternal and infant health data, build a maternal and infant health evaluation model by combining maternal and infant physiological data with machine learning algorithms, and calculate the deviation of maternal and infant physiological data; The early warning module is used to compare and analyze the deviation of maternal and infant physiological data with the preset threshold value, and automatically issue mild, moderate and severe early warning information according to the comparison and analysis results; The feedback module is used for medical staff to log into the system, view early warning information and the deviation of maternal and infant physiological data, formulate personalized treatment plans based on clinical experience, and provide feedback through the system.

2. According to claim 1, the intelligent perinatal maternal and infant health monitoring and early warning system is characterized in that: The maternal and infant physiological data include maternal physiological parameters and fetal physiological parameters; The physiological parameters of pregnant women include blood pressure fluctuation coefficient and hemoglobin concentration; The fetal physiological parameters include fetal heart rate stability coefficient and growth and development index data.

3. The intelligent perinatal maternal and infant health monitoring and early warning system according to claim 2 is characterized in that: The specific process of obtaining the blood pressure fluctuation coefficient is as follows: Measure pregnant women's systolic and diastolic blood pressure; The systolic and diastolic blood pressures measured each time were compared with the normal reference range, and the number of times the systolic and diastolic blood pressures exceeded the normal range was recorded respectively; And record each time the systolic and diastolic blood pressure exceeds the normal range, and calculate the blood pressure fluctuation coefficient based on the number of times the systolic and diastolic blood pressure exceeds the normal range. The specific calculation formula is as follows: Where X is the blood pressure fluctuation coefficient, S and D are the times when systolic and diastolic blood pressure exceed the normal range, respectively, and ΔM SBP and ΔM DBP The systolic and diastolic blood pressures are outside the normal range. Yes i Systolic blood pressure measured at all times, Yes i Diastolic blood pressure measured at all times.

4. The intelligent perinatal maternal and infant health monitoring and early warning system according to claim 3, characterized in that: The process of obtaining the hemoglobin concentration is as follows: The quality of hemoglobin is obtained through routine blood tests; Obtain the pregnant woman's weight and estimate the blood volume based on the weight; The hemoglobin concentration is obtained by dividing the hemoglobin mass by the blood volume. The specific calculation formula is as follows: In the formula, Yes i The hemoglobin concentration at the time, Yes i The quality of hemoglobin at each moment, It is the pregnant woman's weight at the moment.

5. The intelligent perinatal maternal and infant health monitoring and early warning system according to claim 4, characterized in that: The specific process of obtaining the fetal heart rate stability coefficient is as follows: Using an ultrasonic Doppler fetal heart rate monitor, the fetal heart rate data is continuously collected at certain time intervals, which are data points. The mean fetal heart rate is calculated based on the data points, and the fetal heart rate stability coefficient is calculated by combining the fetal heart rate value of the data point and the number of data points. The specific calculation formula is as follows: In the formula, is the fetal heart rate stability coefficient, Yes i The fetal heart rate value collected at the moment, n is the number of fetal heart rate data points collected, is the mean fetal heart rate.

6. The intelligent perinatal maternal and infant health monitoring and early warning system according to claim 5, characterized in that: The process of obtaining growth and development indicator data is as follows: At different time points t i and t i+1 The biparietal diameter and femur length of the fetus were measured respectively; According to the measured biparietal diameter and femoral length of the fetus, the ratio of biparietal diameter to femoral length was calculated, and the ratio of biparietal diameter to femoral length was calculated based on the different time points t i and t i+1 Calculate the fetal biparietal diameter growth rate and fetal femoral length growth rate; The growth and development index data are calculated based on the ratio of biparietal diameter to femoral length, the fetal biparietal diameter growth rate and the fetal femoral length growth rate. The specific formula is as follows: In the formula, I grow It is the growth and development indicator data. and are time points t i and t i+1 The biparietal diameter of the fetus, and are time points t i and t i+1 The bone length value of the fetus, u1 is the weight of the ratio of biparietal diameter to femur length, u2 is the weight of the fetal biparietal diameter growth rate, and u3 is the weight of the fetal femur length growth rate.

7. The intelligent perinatal maternal and infant health monitoring and early warning system according to claim 6, characterized in that: The maternal and child health data includes maternal health data and fetal health data; The maternal and infant health assessment model includes a maternal health assessment model and a fetal health assessment model; The maternal and infant physiological data deviation includes the pregnant woman's physiological data deviation and the fetal physiological data deviation.

8. The intelligent perinatal maternal and infant health monitoring and early warning system according to claim 7, characterized in that: The described model for constructing the health assessment model for pregnant women is as follows: According to the blood pressure fluctuation coefficient and hemoglobin concentration, a maternal health assessment model is constructed in combination with maternal health data. The specific formula is as follows: Where P mom is the deviation of the pregnant woman's physiological data; w1 and w2 are the weight coefficients of blood pressure fluctuation coefficient and hemoglobin concentration, respectively, and both are greater than 0, obtained by historical importance; Yes i The hemoglobin concentration at the moment, X is the blood pressure fluctuation coefficient, and N is the health data of the pregnant woman.

9. The intelligent perinatal maternal and infant health monitoring and early warning system according to claim 8, characterized in that: The process of constructing the fetal health assessment model is as follows: According to the fetal heart rate stability coefficient, growth and development index data, and combined with fetal health data, a fetal health assessment model is constructed. The specific formula is: Where P baby is the deviation of fetal physiological data, a1 and a2 are the fetal heart rate stability coefficients, is the fetal heart rate stability coefficient, I grow is the growth and development index data, I s is the upper limit of growth and development index data, I x It is the lower limit of growth and development indicator data.

10. The intelligent perinatal maternal and infant health monitoring and early warning system according to claim 9, characterized in that: The process of comparing and analyzing the deviation of maternal and infant physiological data with a preset threshold and automatically issuing mild, moderate and severe warning information according to the comparison and analysis results is as follows: Compare and analyze the deviation of the pregnant woman's physiological data and the deviation of the fetus' physiological data with the physiological data deviation threshold respectively; If the deviation of the pregnant woman's physiological data is less than the deviation threshold of the pregnant woman's physiological data, and the deviation of the fetal physiological data is less than the deviation threshold of the fetal physiological data, no warning notification will be issued, and the health of the mother and child will be continuously monitored; If the deviation of the pregnant woman's physiological data is less than the deviation threshold of the pregnant woman's physiological data, and the deviation of the fetal physiological data is greater than the deviation threshold of the fetal physiological data, a mild warning notification is issued; If the deviation of the pregnant woman's physiological data is greater than the deviation threshold of the pregnant woman's physiological data, and the deviation of the fetal physiological data is less than the deviation threshold of the fetal physiological data, a moderate warning notification is issued; If the deviation of the pregnant woman's physiological data is greater than the deviation threshold of the pregnant woman's physiological data, and the deviation of the fetal physiological data is greater than the deviation threshold of the fetal physiological data, a severe warning notification will be issued and medical staff will be notified immediately to conduct diagnosis and treatment.

Images