Gynecological and obstetric information system based on antenatal care data flow

Abstract

The invention relates to the technical field of computers, and discloses a gynaecology and obstetrics information system based on antenatal care data flow, which is used for solving the problem that the same part of information is input simultaneously during information input, and comprises the following steps: marking the time sequence of the antenatal care information each time, and inputting the antenatal care information needing to be input into the system through an interactive interface. Performing time sequence detection on the input antenatal care information, performing information data evaluation on the input antenatal care information if the condition that the same part of information is written at the same time is detected, selecting the antenatal care information to be input according to a data evaluation value, and inputting the antenatal care information into the system if the condition that the information is written at the same time is not detected. The problems of conflicts and redundancy caused by simultaneous input of multiple parties are effectively solved, the data quality is guaranteed, the input efficiency is optimized, and meanwhile, higher-quality data support is provided for clinical decision making.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and more specifically to an obstetrics and gynecology information system based on prenatal examination data stream. Background Art

[0002] In modern obstetrics and gynecology information systems, intelligent management technology based on prenatal examination data streams has become an important means to optimize medical services and improve the efficiency of maternal health management. Existing obstetrics and gynecology information systems usually adopt centralized data collection and processing methods, integrating maternal examination data at different stages (such as ultrasound, fetal heart rate monitoring, blood pressure, etc.) into the system through device upload or manual entry by medical staff. These systems focus on data integrity and consistency, and store, analyze and assess risks of data through preset rules and algorithms to provide support for clinical decision-making.

[0003] For example, Chinese patent CN106295109A discloses a mobile medical information system and an information entry method, including: an automated template engine configured to generate a template and a memory configured to store the template, the template including formatted content and optional content. The mobile medical information system further includes a mobile device and a mobile access device. The mobile device is connected to a server via a wireless network, and is configured to download a template from the server in response to a user's input, present the template to the user, receive the user's input for the optional content, generate merged content based on the formatted content and the user's input for the optional content, and send the merged content to the mobile access device via a wireless connection. The mobile access device communicates with the mobile device via a wireless connection, is connected to a host via a host interface component, and is configured to transmit the merged content received from the mobile device to the host via the host interface component.

[0004] For example, Chinese patent CN104965989A discloses a mobile medical information system, including: a sensor, a mobile intelligent terminal and a mobile medical service platform, wherein the mobile intelligent terminal and the sensor exchange data through short-range wireless communication technology, and the mobile intelligent terminal and the mobile medical service platform exchange data through long-range wireless communication technology. The mobile medical information system proposed in the present invention can provide some solutions to the problem of difficulty in seeking medical treatment. People can use mobile medical equipment to transmit medical information to remote medical centers, and medical centers can also use mobile medical equipment to carry out remote treatment for pregnant women.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0006] In actual clinical scenarios, doctors, midwives, nurses and even equipment may enter the same information at the same time. If the existing method is still used to enter the information, it may cause data conflicts, key anomalies being ignored or erroneous information being written, which may increase the risk of misjudgment and missed diagnosis. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, the present invention provides an obstetrics and gynecology information system based on prenatal examination data stream to solve the problems existing in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] An obstetrics and gynecology information system based on prenatal examination data stream includes a time series marking module, an interaction module, a user input module, a time series detection module, an information data evaluation module, an information selection module and an information entry module. The modules are connected by signals, and the steps of data processing between the modules are as follows: a time series marking module is used to mark the time series of each prenatal examination information, and transmit the prenatal examination information with the marked time series to an input user terminal, which includes equipment, doctors and pregnant women; an interaction module is used to provide an interactive interface for inputting information to the input user terminal; a user input module is used for the input user terminal to input the prenatal examination information to be entered into the system through the interactive interface; a time series detection module is used to time series the prenatal examination information input by the input user terminal Detection, if it is detected that the same part of the information is written at the same time, the prenatal examination information that can be entered into the user end is transmitted to the information data evaluation module; if it is detected that the information is not written at the same time, the prenatal examination information that can be entered into the user end is transmitted to the information entry module; the information data evaluation module is used to collect the information data of the prenatal examination information that can be entered into the user end, and obtain the data evaluation value according to the information data evaluation, the data evaluation value includes data integrity, data reliability and trend reliability, and transmit the data evaluation value to the information selection module; the information selection module is used to use the priority diagram method to select the prenatal examination information to be entered according to the data evaluation value, and transmit the prenatal examination information selected to be entered to the information entry module; the information entry module is used to enter the prenatal examination information into the system

[0010] Preferably, the data integrity acquisition step is as follows: the information data is composed of records, and the information of each record in the information data is collected, and the information of each record includes the data source and the number of filled fields; the total number of records in the information data is obtained, the number of records with source identifiers is counted, and the number of records with source identifiers is calculated by ratio to the total number of records to obtain the data source identification rate; the number of fillable fields in each record is collected, and the total number of fillable fields in the information data is obtained by summing them up, the number of filled fields in the information data is collected, and the number of filled fields is calculated by ratio to the total number of fillable fields to obtain the filled field coverage rate; the number of repeated fields in the information data is detected by the hash value method, the total number of fields in the information data is obtained, and the number of repeated fields is calculated by ratio to the total number of fields to obtain the field redundancy rate; the data integrity is obtained according to the data source identification rate, the filled field coverage rate and the field redundancy rate, and the specific acquisition method is as follows: In the formula, DC represents data integrity, si represents data source identification rate, fc represents filled field coverage rate, and fr represents field redundancy rate.

[0011] Preferably, the steps for detecting the number of repeated fields in the information data by the hash value method are: taking the fillable fields in each record in the information data as input; generating a unique hash value for each fillable field; using a hash table to store the hash values ​​and counting the number of occurrences; traversing the hash table, counting the number of fields whose number of occurrences is greater than 1, recording them as repeated fields, and obtaining the number of repeated fields by counting.

[0012] Preferably, the data reliability acquisition step is: setting credibility scores for different information data sources; detecting the number of repeated fields in the information data by hash value method, acquiring the total number of fields in the information data, and obtaining the field redundancy rate based on the number of repeated fields and the total number of fields; obtaining data reliability based on the credibility score and the field redundancy rate.

[0013] Preferably, the trend reliability acquisition step is: calculating the trend consistency and trend abnormality of each information data; acquiring the physical information of the pregnant woman in real time, and obtaining the health coefficient of the pregnant woman according to the physical information evaluation; setting the initial consistency weight of the trend consistency and the initial abnormality weight of the trend abnormality, and dynamically adjusting the initial consistency weight and the initial abnormality weight according to the health coefficient of the pregnant woman to obtain the actual consistency weight and the actual abnormality weight; evaluating the trend reliability according to the trend consistency, trend abnormality of the information data and the actual consistency weight and the actual abnormality weight after dynamic adjustment, and the specific acquisition method is as follows: TR=W T ×TQ+W A ×AQ; where TR represents trend reliability, TQ represents trend consistency, AQ represents trend abnormality, and W T , W AExpressed as actual consistency weight and actual abnormality weight.

[0014] Preferably, the steps of calculating the trend consistency and trend abnormality of each information data are: obtaining the time series of the information data, using the model to predict the normal prediction value and the maximum allowable deviation range when obtaining the time series; obtaining the actual input value of the information data, and obtaining the trend consistency according to the actual input value, the normal prediction value and the maximum allowable deviation range; obtaining the center value of the maximum allowable deviation range when obtaining the time series, recorded as the range center value; obtaining the trend abnormality according to the actual input value, the range center value and the maximum allowable deviation range.

[0015] Preferably, the steps for obtaining the health coefficient of the pregnant woman are: obtaining the actual age of the pregnant woman, obtaining diseases that may increase the risk of pregnancy, recording them as total diseases, counting the total number of diseases, recording them as the total number of diseases, collecting the medical history of the pregnant woman in real time, screening out the number of diseases included in the total number of diseases in the medical history, recording them as the number of diseases; and evaluating the health coefficient of the pregnant woman according to the actual age of the pregnant woman, the total number of diseases, and the number of diseases. The specific acquisition method is as follows: In the formula, PH represents the health coefficient of pregnant women, ag 实际 It is the actual age of pregnant women, ag 理想 represents the ideal age, hb represents the number of diseases, and jz represents the total number of diseases.

[0016] Preferably, the steps of dynamically adjusting the initial consistency weight and the initial abnormality weight according to the health coefficient of the pregnant woman to obtain the actual consistency weight and the actual abnormality weight are: obtaining the health coefficient of the pregnant woman, and obtaining the actual consistency weight according to the health coefficient of the pregnant woman and the initial consistency weight; and calculating the actual abnormality weight according to the actual consistency weight.

[0017] Preferably, the steps of using the priority diagram method to select the prenatal examination information to be entered according to the data evaluation value are: setting a linear preference function; comparing the data reliability of every two information data in the prenatal examination information to obtain a data reliability difference, and bringing the data reliability difference into the linear preference function to obtain a data reliability preference value for each data; traversing the data reliability of all information data to construct a data reliability preference matrix; comparing the data integrity of every two information data in the prenatal examination information to obtain a data integrity difference, bringing the data integrity difference into the linear preference function to obtain a data integrity preference value, traversing the data integrity of all information data to construct a data integrity preference matrix; comparing the trend reliability of every two information data in the prenatal examination information to obtain a trend reliability difference, bringing the trend reliability difference into the linear preference function to obtain a trend reliability preference value, traversing the trend reliability of all information data to construct a trend reliability preference matrix; performing weighted calculations on the data reliability preference matrix, the data integrity preference matrix and the trend reliability preference matrix to obtain a comprehensive preference matrix; calculating the net flow of each element according to the comprehensive preference matrix, sorting the net flow of each element from large to small, and selecting the information data with the largest net flow to enter the prenatal examination information.

[0018] Preferably, the steps of calculating the net flow of each element according to the comprehensive preference matrix are: calculating the average value of each row of data in the comprehensive preference matrix to obtain positive flow; calculating the average value of each column of data in the comprehensive preference matrix to obtain negative flow; subtracting the negative flow from the positive flow to obtain the net flow.

[0019] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. Marking the time series of each prenatal examination information can effectively identify whether the same part of the information is written at the same time, avoid data conflicts or duplications caused by multiple entries, and improve the consistency and accuracy of system data.

[0021] 2. Detecting time series can effectively avoid data conflicts and redundancy, and ensure the quality and accuracy of input information. This mechanism can not only screen and optimize duplicate information input by multiple parties, but also simplify the input process of normal information, improve system operation efficiency, and provide reliable basic support for subsequent data analysis and clinical decision-making.

[0022] 3. Evaluating prenatal examination information helps to scientifically screen data entered by multiple parties and ensure the high quality of the entered information. This mechanism can comprehensively evaluate the accuracy and credibility of the data, give priority to entering the most reliable information, and effectively reduce the impact of erroneous or abnormal data, providing stronger support for system data management and clinical decision-making.

[0023] 4. Using the priority diagram method to select information for entry can achieve a comprehensive multi-dimensional evaluation, thereby giving priority to the most reliable and accurate information entry system. This method can not only effectively solve the problem of multi-party data conflicts, but also improve the quality and consistency of input information, provide a reliable data basis for subsequent medical analysis and clinical decision-making, while reducing the risk of human intervention and data errors, and optimizing system operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A structural diagram of an obstetrics and gynecology information system based on prenatal examination data stream provided in an embodiment of the present application.

[0025] Figure 2 Schematic diagram of data evaluation values ​​in the embodiments of the present application. DETAILED DESCRIPTION

[0026] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative. The obstetrics and gynecology information system based on prenatal examination data stream involved in the present invention is not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0027] The present invention provides an obstetrics and gynecology information system based on prenatal examination data stream, such as Figure 1 As shown, it includes an automatic feeding module, a fixture fixing module, a quality detection module, a performance data acquisition module, a fixture adjustment module and an early warning module, and the modules are connected through signals;

[0028] The time series marking module is used to mark the time series of each prenatal examination information and transmit the marked time series prenatal examination information to the input user terminal, which includes the device, the doctor and the pregnant woman;

[0029] Marking each prenatal checkup information in time series can clarify the time sequence of the data, facilitate analysis of maternal health trends, identify abnormal information at key nodes, and ensure the correct association and traceability of multiple input data. This kind of marking can support dynamic risk assessment and personalized health management, avoid misjudgment or omissions caused by data time confusion, and provide more accurate decision support, especially in high-risk pregnancy monitoring.

[0030] Marking the time series of each prenatal checkup information can ensure that the data is correctly associated and managed in chronological order, avoiding misjudgments or inaccurate judgments caused by different data entry orders or time confusion. It can clearly show the dynamic changes in the health status of mothers, ensure the accuracy of risk assessment and trend analysis, and provide reliable data support for clinical diagnosis and treatment, which is particularly important in the management of high-risk pregnancies.

[0031] An interactive module, used to provide an interactive interface for a user terminal to input information;

[0032] The user input module allows the user to input the prenatal examination information that needs to be entered into the system through the interactive interface;

[0033] The user can input the prenatal examination information that needs to be written into the system through the interactive panel, which can realize multi-party collaboration. Through the unified entrance of the interactive panel, different roles can enter relevant information according to their authority and responsibilities, thus forming a comprehensive and dynamic maternal health record. The benefits of this method are: first, it improves the flexibility and efficiency of data entry to meet the needs of different scenarios; second, it improves the reliability and integrity of information through cross-validation of multi-source data; third, it enables pregnant women to participate in their own health management and enhance their health awareness; fourth, it facilitates real-time updates and monitoring of the system to ensure that the information is accurate and timely, and ultimately optimizes the quality of clinical decision support and maternal health management.

[0034] A time series detection module is used to perform time series detection on the prenatal examination information that can be input by the user end. If it is detected that the same part of the information is written at the same time, the prenatal examination information that can be input by the user end is transmitted to the information data evaluation module; if it is detected that the information is not written at the same time, the prenatal examination information that can be input by the user end is transmitted to the information input module;

[0035] Dividing the prenatal examination information input by the user into information that is written to the same part at the same time and information that is not written at the same time can achieve targeted data processing. In the case of simultaneous writing, data conflicts can be effectively detected and resolved to ensure the consistency and reliability of data entered by multiple parties; in the case of no simultaneous writing, single-source data processing can be performed directly to improve system efficiency. The role of this module is to improve the accuracy and flexibility of data management, provide support for the dynamic update and efficient processing of prenatal examination information, thereby ensuring the accuracy of data and the scientific nature of clinical decision-making.

[0036] An information data evaluation module is used to collect information data of prenatal examination information that can be input by the user end, obtain a data evaluation value based on the information data evaluation, the data evaluation value includes data integrity, data reliability and trend reliability, and transmit the data evaluation value to the information selection module;

[0037] In this embodiment, it should be specifically explained that the steps of obtaining data integrity are:

[0038] The information data may include one or more records, each record may include one or more fillable fields, and the information of each record in the information data is collected, and the information of each record includes the data source and the number of filled fields;

[0039] Get the total number of records in the information data, count the number of records with source identifiers, and get the data source identification rate based on the total number of records and the number of records with source identifiers. The specific acquisition method is as follows:

[0040]

[0041] In the formula, si represents the data source identification rate, num s Represented as the number of records with source identifier, num s总 Expressed as the total number of records;

[0042] Collect the number of fillable fields in each record, sum them up to get the total number of fillable fields in the information data, collect the number of filled fields in the information data, and get the filled field coverage rate based on the number of filled fields and the total number of fillable fields. The specific acquisition method is as follows:

[0043]

[0044] In the formula, fc represents the coverage rate of the filled field, num f Indicates the number of filled fields, num f总 Expressed as the total number of fillable fields;

[0045] The number of repeated fields in the information data is detected by the hash value method, and the total number of fields in the information data is obtained. The field redundancy rate is obtained according to the number of repeated fields and the total number of fields. The specific acquisition method is as follows:

[0046]

[0047] In the formula, fr represents the field redundancy rate, num r Represented as the number of repeated fields and num r总 Expressed as the total number of fields;

[0048] The data integrity is obtained based on the data source identification rate, field coverage rate, and field redundancy rate. The specific acquisition method is as follows:

[0049]

[0050] In the formula, DC represents data integrity, si represents data source identification rate, fc represents filled field coverage rate, and fr represents field redundancy rate.

[0051] In this embodiment, it should be specifically explained that the steps of detecting the number of repeated fields in the information data by using the hash value method are:

[0052] Take the fillable fields in each record in the information data as input, and ensure that the data is in a clear and standardized format;

[0053] Generate a unique hash value for each fillable field. The hash value is the result of mapping the field to a fixed-length value through a hash function;

[0054] Use a hash table to store hash values ​​and count the number of occurrences. If the current hash value already exists, it means the field is repeated. If it does not exist, add it to the hash table.

[0055] Traverse the hash table, count the number of fields that appear more than 1 times, record them as repeated fields, and count the number of repeated fields.

[0056] A hash value is the result of mapping input data (such as text, numbers, files, etc.) to a fixed-length string or value through a hash function. It is unique and efficient. The same input will always get the same hash value, and different inputs will most likely generate different hash values. Hash values ​​are often used for fast indexing, duplicate detection, and data verification. When detecting duplicate fields in information data, hash values ​​can efficiently identify field contents and quickly determine whether a field is duplicated by comparing whether the hash values ​​are the same.

[0057] In this embodiment, it should be specifically explained that the steps of obtaining data reliability are:

[0058] Set credibility scores for different information data sources, and the credibility score of doctor input is the highest. Doctors can make data corrections based on the patient's clinical background and comprehensive judgment to avoid interference from abnormal measurement values ​​of the equipment. Doctors' input usually contains contextual explanations, such as explaining the reasons for a certain abnormal measurement. Such information cannot be provided by the equipment. For example, the credibility score of doctor input data is 1.0, the credibility score of data automatically collected by the equipment is 0.9, and the credibility score of data filled in by pregnant women is 0.7;

[0059] The number of repeated fields in the information data is detected by the hash value method, the total number of fields in the information data is obtained, and the field redundancy rate is obtained according to the number of repeated fields and the total number of fields;

[0060] The data reliability is obtained based on the credibility score and field redundancy rate. The specific acquisition method is as follows:

[0061]

[0062] In the formula, DR represents data reliability, cy represents the credibility score, and fr represents the field redundancy rate. It should be specifically noted that when calculating data reliability, the credibility score and the field redundancy rate are both normalized.

[0063] In this embodiment, it should be specifically explained that the steps of obtaining trend reliability are:

[0064] Calculate the trend consistency and trend abnormality of each information data;

[0065] Obtain the pregnant woman's physical information in real time, and evaluate the pregnant woman's health index based on the physical information;

[0066] The initial consistency weight of the trend consistency and the initial abnormality weight of the trend abnormality are set, and the sum of the initial consistency weight and the initial abnormality weight is 1. The initial consistency weight and the initial abnormality weight are dynamically adjusted according to the health coefficient of the pregnant woman to obtain the actual consistency weight and the actual abnormality weight;

[0067] The trend reliability is obtained by evaluating the trend consistency, trend anomaly, and actual consistency weight and actual anomaly weight of the information data after dynamic adjustment. The specific acquisition method is as follows:

[0068] TR=W T ×TQ+W A ×AQ;

[0069] In the formula, TR represents the reliability of the trend, TQ represents the consistency of the trend, AQ represents the abnormality of the trend, and W T , W A Expressed as actual consistency weight and actual abnormality weight.

[0070] In this embodiment, it should be specifically explained that the steps of calculating the trend consistency and trend abnormality of each information data are:

[0071] Get the time series of information data, use the model to predict according to historical data, and predict the normal prediction value and the maximum allowable deviation range of the time series. For example, the normal range of fetal heart rate is 110-160bpm, and the maximum allowable deviation range is 50.

[0072] The actual input value of the information data is obtained, and the trend consistency is obtained according to the actual input value, the normal predicted value and the maximum allowable deviation range. The specific acquisition method is as follows:

[0073]

[0074] In the formula, TQ represents trend consistency, which takes values ​​between 0 and 1. The closer the value is to 1, the more consistent the data is with the trend. 输入Represents the actual input value, x 预测 It represents the normal prediction value, and fw represents the maximum allowable deviation range;

[0075] The center value of the maximum deviation range allowed when obtaining the time series is recorded as the range center value. For example, the normal range of fetal heart rate is 110-160bpm, and the center value is 135;

[0076] The trend abnormality is obtained based on the actual input value, the range center value and the maximum allowable deviation range. The specific acquisition method is as follows:

[0077]

[0078] In the formula, TQ represents the trend abnormality, x 输入 Represents the actual input value, x 中心值 It is expressed as the center value of the range, and fw is expressed as the maximum allowed deviation range.

[0079] In this embodiment, it should be specifically explained that the steps for obtaining the health coefficient of pregnant women are:

[0080] Obtain the actual age of the pregnant woman, obtain the diseases that increase the risk of pregnancy, record them as the total diseases, count the total number of diseases, record them as the total number of diseases, collect the medical history of the pregnant woman in real time, the time of the medical history is the time from the birth of the pregnant woman to the present, filter out the number of diseases included in the total diseases in the medical history, record them as the number of diseases;

[0081] The health coefficient of pregnant women is obtained based on the actual age of the pregnant woman, the total number of diseases and the number of diseases. The specific method of obtaining it is as follows:

[0082]

[0083] In the formula, PH represents the health coefficient of pregnant women, ag 实际 It is the actual age of pregnant women, ag 理想 It represents the ideal age. In this embodiment, the ideal age can be 35, and can be adjusted by professionals according to actual conditions. hb represents the number of diseases, and jz represents the total number of diseases.

[0084] Obtaining a pregnant woman's complete medical history, including all time periods from birth to the present, is because diseases during and before pregnancy may have an important impact on pregnancy safety and maternal and child health. A comprehensive medical history can help the medical team better assess the health status and potential risks of pregnant women. For example, chronic diseases (such as hypertension, diabetes, and thyroid dysfunction) may worsen during pregnancy and increase the risk of pregnancy complications, while new diseases during pregnancy (such as gestational diabetes and pregnancy-induced hypertension syndrome) may have a direct impact on fetal development and the delivery process. By tracing a complete medical history, it can provide a comprehensive basis for formulating personalized pregnancy management plans, effectively reduce pregnancy risks, and improve maternal and child safety.

[0085] In this embodiment, it should be specifically explained that the steps of dynamically adjusting the initial consistency weight and the initial abnormality weight according to the health coefficient of the pregnant woman to obtain the actual consistency weight and the actual abnormality weight are:

[0086] Obtain the maternal health coefficient, and obtain the actual consistency weight based on the maternal health coefficient and the initial consistency weight. The specific acquisition method is as follows:

[0087]

[0088] Where W T Expressed as the actual consistency weight, It is expressed as the initial consistency weight, and PH is expressed as the maternal health factor;

[0089] The actual abnormality weight is calculated based on the actual consistency weight. The specific method of obtaining it is as follows:

[0090] W A =1-W T ;

[0091] Where W A Expressed as the actual abnormality weight, W T Expressed as the actual consistency weight.

[0092] The consistency weight and abnormality weight are adjusted dynamically in real time according to the health coefficient of the pregnant woman, which can flexibly respond to the current physical condition and disease risk of the pregnant woman and realize personalized data evaluation. This method can more accurately identify potential abnormalities by increasing the sensitivity to abnormal data when the health risk is high and reducing the reliance on trend consistency; when the health risk is low, it relies more on the historical trend of the data to reduce misjudgment. This dynamic adjustment mechanism helps to optimize the data processing strategy in real time according to the health status of the pregnant woman, thereby improving the accuracy of diagnosis and the pertinence of management plans, and ensuring the safety of mothers and babies.

[0093] An information selection module, used to select the prenatal examination information to be entered according to the data evaluation value using the priority diagram method, and transmit the prenatal examination information selected to be entered to the information entry module;

[0094] The superiority graph method is a scientific decision-making method based on multi-criteria analysis, which is widely used in scenarios that require a comprehensive evaluation of the pros and cons of multidimensional data. This method calculates the preference function of the indicators of each data point in different dimensions, constructs a preference matrix, and then calculates the positive flow (measures the superiority of the data relative to other data), negative flow (measures the inferiority of the data) and net flow (the difference in the comprehensive superiority). Finally, the net flow value is used as the basis for data ranking. In the entry of prenatal examination information, the superiority graph method can effectively handle conflicting data entered by multiple parties at the same time, and scientifically select the optimal information for entry by weighing the pros and cons of multi-dimensional indicators. This method not only improves the accuracy and reliability of the entered data, but also ensures the reasonable identification of abnormal data, providing more accurate data support for the dynamic health management and evaluation of parturients.

[0095] In this embodiment, it should be specifically explained that the steps of selecting the prenatal examination information to be entered according to the data evaluation value using the priority diagram method are as follows:

[0096] like Figure 2 As shown, the data evaluation values ​​are sorted. In this embodiment, the data shown in Table 1 is taken as an example;

[0097] Table 1 Data evaluation values

[0098] Information Data Data reliability Data Integrity Trend reliability 1 0.9 0.8 0.7 2 0.8 0.9 0.6 3 0.7 0.85 0.8

[0099] As shown in Table 1, in a specific embodiment, the numerical changes of data reliability, data integrity, and trend reliability are displayed through different dimensions. By analyzing the differences in these indicators, possible abnormal data points can be identified, such as differences in the reliability of data entry sources or abnormalities that deviate from historical trends. The degree of fluctuation of these indicators can also be used to evaluate the credibility and consistency of data in multi-source data entry scenarios. In the obstetrics and gynecology information system based on prenatal examination data streams, analyzing the changes in these indicators can effectively capture information conflicts caused by simultaneous entry by multiple parties or data anomalies, thereby intelligently screening high-quality data to be written into the system to ensure the accuracy of information and the reliability of clinical judgment.

[0100] A linear preference function is used. For example, in this embodiment, if the difference is less than or equal to 0, there is no preference, that is, P(a, b) = 0; if the difference is less than or equal to 0.3 and greater than 0, the preference value increases linearly, and Where Δ is the difference; if the difference is greater than 0.3, it is completely preferred, that is, P(a, b) = 1;

[0101] According to the data reliability data and linear preference function in Table 1, the data reliability of every two pieces of information in the prenatal examination information is compared to obtain the data reliability difference. The specific method of obtaining the difference is as follows:

[0102] Δ R (a,b)=R a -R b ;

[0103] In the formula, Δ R (a, b) represents the data reliability difference between information data a and information data b. For example, the data reliability difference between information data 1 and information data 2 is Δ R (1,2)=0.9-0.8=0.1, then the data reliability difference between information data 1 and information data 2 is 0.1. Substituting the data reliability difference into the linear preference function, we can get the data reliability preference value between information data 1 and information data 2. The specific acquisition method is as follows:

[0104]

[0105] Where P(1,2) represents the data reliability preference value of information data 1 and information data 2;

[0106] Traverse the data reliability of all information data in Table 1 and construct a data reliability preference matrix:

[0107]

[0108] Where P R It is represented as a data reliability preference matrix;

[0109] Compare the data integrity of every two pieces of information in the prenatal examination information to obtain the data integrity difference, bring the data integrity difference into the linear preference function to obtain the data integrity preference value, traverse the data integrity of all information data in Table 1, and construct the data integrity preference matrix:

[0110]

[0111] Where P C Represented as a data integrity preference matrix;

[0112] Compare the trend reliability of every two information data in the prenatal examination information to obtain the trend reliability difference, bring the trend reliability difference into the linear preference function to obtain the trend reliability preference value, traverse the trend reliability of all information data in Table 1, and construct the trend reliability preference matrix:

[0113]

[0114] Where P T It is represented as a trend reliability preference matrix;

[0115] The comprehensive preference matrix is ​​obtained by comprehensively calculating the data reliability preference matrix, the data integrity preference matrix and the trend reliability preference matrix. The specific method of obtaining it is as follows:

[0116] P total =W1×P R +W2×P C +W3×P T ;

[0117] Where P total Expressed as a comprehensive preference matrix, P R Expressed as data reliability preference matrix, P C Expressed as a data integrity preference matrix, P T It is expressed as a trend reliability preference matrix, W1, W2, and W3 are expressed as weight coefficients of the data reliability preference matrix, the data integrity preference matrix, and the trend reliability preference matrix, and W1+W2+W3=1. The specific values ​​of W1, W2, and W3 are determined by professionals according to the actual situation. For example, W1, W2, and W3 can be 0.3, 0.4, and 0.3;

[0118] The net flow of each element is calculated based on the comprehensive preference matrix, and the net flow of each element is sorted from large to small, and the information data with the largest net flow is selected for entering prenatal examination information.

[0119] In this embodiment, it should be specifically explained that the steps for calculating the net flow of each element according to the comprehensive preference matrix are:

[0120] Calculate the average value of each row of data in the comprehensive preference matrix to get the positive flow. The positive flow is used to measure the superiority of a certain data relative to other data in the superiority chart method. By calculating the average value of each row of data in the comprehensive preference matrix, we can get the average preference value of the data that has an advantage in all comparisons. The higher the positive flow value, the more overall advantage the data has in the comparison, which can serve as an important basis for the final selection;

[0121] Calculate the average value of each column of data in the comprehensive preference matrix to get the negative flow. Negative flow is a value used in the priority diagram method to measure the degree of disadvantage of a certain data relative to other data in the comprehensive index. By calculating the average value of each column of the comprehensive preference matrix, we can get the average preference value of the data surpassed by other data in all comparisons. The higher the negative flow value, the greater the degree of disadvantage of the data in the comparison, which is an important indicator for judging the overall disadvantage of the data.

[0122] Net flow is obtained by subtracting negative flow from positive flow. In the priority diagram method, net flow is the value obtained by subtracting negative flow from positive flow, which is used to comprehensively measure the overall superiority of a certain data. The larger the net flow value, the more obvious the advantage of the data in all comparisons; the smaller the value, the greater its relative disadvantage. Net flow is an important basis for the final sorting and selection of the best data, and is used to screen out the data with the best performance under multi-dimensional indicators.

[0123] The information entry module is used to enter prenatal examination information into the system.

[0124] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0125] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An obstetrics and gynecology information system based on prenatal examination data stream, characterized in that: It includes time series marking module, interaction module, user input module, time series detection module, information data evaluation module, information selection module and information entry module. The modules are connected by signals. The data processing steps between the modules are as follows: The time series marking module is used to mark the time series of each prenatal examination information and transmit the marked time series prenatal examination information to the input user terminal, which includes the device, the doctor and the pregnant woman; An interactive module, used to provide an interactive interface for a user terminal to input information; The user input module allows the user to input the prenatal examination information that needs to be entered into the system through the interactive interface; A time series detection module is used to perform time series detection on the prenatal examination information that can be input by the user end. If it is detected that the same part of the information is written at the same time, the prenatal examination information that can be input by the user end is transmitted to the information data evaluation module; If it is detected that there is no simultaneous information writing, the prenatal examination information that can be input by the user end is transmitted to the information input module; An information data evaluation module is used to collect information data of prenatal examination information that can be input by the user end, obtain a data evaluation value based on the information data evaluation, the data evaluation value includes data integrity, data reliability and trend reliability, and transmit the data evaluation value to the information selection module; An information selection module, used to select the prenatal examination information to be entered according to the data evaluation value using the priority diagram method, and transmit the prenatal examination information selected to be entered to the information entry module; The information entry module is used to enter prenatal examination information into the system.

2. The obstetrics and gynecology information system based on prenatal examination data stream according to claim 1, characterized in that: The steps for obtaining data integrity are: Information data consists of records, and the information of each record in the information data is collected. The information of each record includes the data source and the number of filled fields; Obtain the total number of records in the information data, count the number of records with source identifiers, and calculate the ratio of the number of records with source identifiers to the total number of records to obtain the data source identification rate; The number of fillable fields in each record is collected, and the total number of fillable fields in the information data is obtained by summing them up. The number of filled fields in the information data is collected, and the number of filled fields is calculated by the ratio of the number of filled fields to the total number of fillable fields to obtain the filled field coverage rate. The number of repeated fields in the information data is detected by the hash value method, the total number of fields in the information data is obtained, and the field redundancy rate is calculated by calculating the ratio of the number of repeated fields to the total number of fields; The data integrity is obtained based on the data source identification rate, field coverage rate, and field redundancy rate. The specific acquisition method is as follows: In the formula, DC represents data integrity, si represents data source identification rate, fc represents filled field coverage rate, and fr represents field redundancy rate.

3. The obstetrics and gynecology information system based on prenatal examination data stream according to claim 2, characterized in that: The step of detecting the number of repeated fields in the information data by the hash value method is: Take as input the fillable fields in each record in the information data; Generate a unique hash value for each fillable field; Use a hash table to store hash values ​​and count the number of occurrences; Traverse the hash table, count the number of fields that appear more than 1 times, record them as repeated fields, and count the number of repeated fields.

4. The obstetrics and gynecology information system based on prenatal examination data stream according to claim 1, characterized in that: The steps for obtaining data reliability are as follows: Set credibility scores for different information data sources; The number of repeated fields in the information data is detected by the hash value method, the total number of fields in the information data is obtained, and the field redundancy rate is obtained according to the number of repeated fields and the total number of fields; The data reliability is obtained based on the credibility score and field redundancy rate.

5. The obstetrics and gynecology information system based on prenatal examination data stream according to claim 1, characterized in that: The trend reliability acquisition steps are: Calculate the trend consistency and trend abnormality of each information data; Obtain the pregnant woman's physical information in real time, and evaluate the pregnant woman's health index based on the physical information; The initial consistency weight of the trend consistency and the initial abnormality weight of the trend abnormality are set, and the initial consistency weight and the initial abnormality weight are dynamically adjusted according to the health coefficient of the pregnant woman to obtain the actual consistency weight and the actual abnormality weight; The trend reliability is obtained by evaluating the trend consistency, trend anomaly, and actual consistency weight and actual anomaly weight of the information data after dynamic adjustment. The specific acquisition method is as follows: TR=W T ×TQ+W A ×AQ; In the formula, TR represents the reliability of the trend, TQ represents the consistency of the trend, AQ represents the abnormality of the trend, and W T , W A Expressed as actual consistency weight and actual abnormality weight.

6. The obstetrics and gynecology information system based on prenatal examination data stream according to claim 5, characterized in that: The steps of calculating the trend consistency and trend abnormality of each information data are as follows: Obtain the time series of information data, and use the model to predict the normal prediction value and the maximum allowable deviation range of the time series; Obtain the actual input value of the information data, and obtain the trend consistency based on the actual input value, the normal predicted value, and the maximum allowable deviation range; The center value of the maximum deviation range allowed when obtaining the time series is recorded as the range center value; The trend abnormality is obtained based on the actual input value, the range center value, and the maximum allowed deviation range.

7. The obstetrics and gynecology information system based on prenatal examination data stream according to claim 5, characterized in that: The steps for obtaining the health coefficient of pregnant women are: Obtain the actual age of the pregnant woman, obtain the diseases that increase the risk of pregnancy, record them as the total diseases, count the total number of diseases, record them as the total number of diseases, collect the medical history of the pregnant woman in real time, filter out the number of diseases included in the total diseases in the medical history, and record them as the number of diseases; The health coefficient of pregnant women is obtained based on the actual age of the pregnant woman, the total number of diseases and the number of diseases. The specific method of obtaining it is as follows: In the formula, PH represents the health coefficient of pregnant women, ag 实际 It is the actual age of pregnant women, ag 理想 represents the ideal age, hb represents the number of diseases, and jz represents the total number of diseases.

8. The obstetrics and gynecology information system based on prenatal examination data stream according to claim 5, characterized in that: The steps of dynamically adjusting the initial consistency weight and the initial abnormality weight according to the health coefficient of the pregnant woman to obtain the actual consistency weight and the actual abnormality weight are as follows: Obtain the health coefficient of pregnant women, and obtain the actual consistency weight according to the health coefficient of pregnant women and the initial consistency weight; The actual abnormality weight is calculated based on the actual consistency weight.

9. The obstetrics and gynecology information system based on prenatal examination data stream according to claim 1, characterized in that: The steps of selecting the antenatal examination information to be entered according to the data evaluation value using the priority diagram method are as follows: Set the linear preference function; Compare the data reliability of every two pieces of information in the prenatal examination information to obtain the data reliability difference, and bring the data reliability difference into the linear preference function to obtain the data reliability preference value of each piece of data; Traverse the data reliability of all information data and build a data reliability preference matrix; Compare the data integrity of every two pieces of information in the prenatal examination information to obtain the data integrity difference, bring the data integrity difference into the linear preference function to obtain the data integrity preference value, traverse the data integrity of all information data, and construct a data integrity preference matrix; Compare the trend reliability of every two information data in the prenatal examination information to obtain the trend reliability difference, bring the trend reliability difference into the linear preference function to obtain the trend reliability preference value, traverse the trend reliability of all information data, and construct a trend reliability preference matrix; The data reliability preference matrix, data integrity preference matrix and trend reliability preference matrix are weighted and calculated to obtain a comprehensive preference matrix; The net flow of each element is calculated based on the comprehensive preference matrix, and the net flow of each element is sorted from large to small, and the information data with the largest net flow is selected for entering prenatal examination information.

10. The obstetrics and gynecology information system based on prenatal examination data stream according to claim 9, characterized in that: The steps for calculating the net flow of each element according to the comprehensive preference matrix are: Calculate the average value of each row of data in the comprehensive preference matrix to obtain the positive flow; Calculate the average value of each column of data in the comprehensive preference matrix to obtain negative flow; Subtract the negative flow from the positive flow to get the net flow.

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