A method, system, device and medium for prenatal screening data processing

Abstract

This application relates to the field of data processing and discloses a method, system, device, and medium for prenatal screening data processing, including: a risk assessment unit, a data update unit, and a database. The risk assessment unit is used to acquire and parse testing instructions to obtain maternal screening data and a maternal risk assessment plan, and to acquire standard parameter information corresponding to the maternal risk assessment plan from the database to process the maternal screening data according to the standard parameter information, thereby obtaining a risk assessment result. The data update unit is used to update the standard parameter information in the database when the standard parameter information meets a first preset condition. This application achieves real-time parameter updates and improves the accuracy of prenatal screening data processing by storing the standard parameter information in the database, facilitating updates by the data update unit and acquisition of the standard parameter information by the risk assessment unit from the database for processing the testing data.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method, system, apparatus and medium for processing prenatal screening data. Background Technology

[0002] Prenatal screening refers to the use of economical, simple, and non-invasive testing methods to identify high-risk pregnant women carrying fetuses with potential abnormalities, thereby preventing birth defects. Detection of trisomy 21 (Down syndrome) is an important component of prenatal screening.

[0003] Current prenatal screening technologies typically require analyzing the pregnant woman's serum to obtain serum biomarker data and calculating the MOM value (the ratio of the detected value to a standard reference value). The probability of trisomy syndrome in the fetus is then calculated using a likelihood ratio based on the MOM value. However, current prenatal screening systems use fixed test samples and fixed screening combinations, with all test parameters being fixed, leading to inaccurate probability values. Adjusting these parameters requires a complete software upgrade, a complex and time-consuming process that inconveniences users.

[0004] Therefore, it is evident that providing a new method for processing prenatal screening data to facilitate user adjustment of testing parameters and improve testing accuracy is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, medium, and system for processing prenatal screening data, so that users can adjust the detection parameters and thereby improve the accuracy of prenatal screening data processing.

[0006] To address the aforementioned technical problems, this application provides a method for processing prenatal screening data, including:

[0007] Obtain and parse the testing instructions to obtain maternal screening data and maternal risk assessment plans;

[0008] The maternal screening data is processed according to standard parameter information to obtain risk assessment results; wherein, the standard parameter information is information in the database corresponding to the maternal risk assessment scheme and is updated by the data update unit when the standard parameter information meets the first preset condition.

[0009] Preferably, determining whether the standard parameter information satisfies the first preset condition includes:

[0010] Historical testing data is acquired and analyzed to obtain statistical information for each test item; wherein, the statistical information includes the median information for each test item at each gestational week;

[0011] Determine whether the error between the statistical information and the standard value is greater than the error threshold;

[0012] If the error is greater than the error threshold, then the standard parameter information is determined to meet the first preset condition.

[0013] Preferably, the data update unit updates the standard parameter information when the standard parameter information meets the first preset condition, including:

[0014] The historical detection data were fitted using a curve function with least squares curve fitting.

[0015] The curve function with the highest fitting degree is obtained as the fitting function, wherein the fitting function is used to characterize the gestational age-concentration relationship of the target analyte;

[0016] The standard parameter information is determined based on the fitting function, and the standard parameter information in the database is updated.

[0017] Preferably, after the step of determining the standard parameter information based on the fitting function, the method further includes:

[0018] Determine whether the current MOM value meets the second preset condition using correlation analysis;

[0019] If the second preset condition is met, the MOM value is corrected according to the standard parameter information.

[0020] Preferably, before the step of updating the standard parameter information in the database, the method further includes:

[0021] The corrected MOM values ​​were subjected to a normality test.

[0022] If the MOM value meets the third preset condition, then the step of updating the standard parameter information in the database is executed, and the database log is updated to record the update operation.

[0023] Preferably, the processing of the maternal screening data based on standard parameter information includes:

[0024] The standard parameter information is analyzed to obtain the gestational age calculation method;

[0025] The maternal screening data is processed according to the gestational age calculation method to obtain the MOM values ​​of biochemical markers and ultrasound scans.

[0026] Risk items are calculated based on the maternal risk assessment protocol, the MOM values ​​of the biochemical markers, and the MOM values ​​of the ultrasound scans to obtain the risk assessment results.

[0027] To address the aforementioned technical issues, this application also provides a prenatal screening data processing system, comprising: a risk assessment unit, a data update unit, and a database;

[0028] The risk assessment unit is used to acquire and parse the testing instructions to obtain a risk assessment plan and testing data, and to acquire standard parameter information corresponding to the risk assessment plan in the database to process the testing data according to the standard parameter information, thereby obtaining the assessment result.

[0029] The data update unit is used to update the standard parameter information in the database when the standard parameter information meets the first preset condition.

[0030] Preferably, the prenatal screening data processing system is also used for:

[0031] The detection data is saved to the database so that the data update unit can use the detection data to update the standard parameter information.

[0032] To address the aforementioned technical problems, this application also provides a prenatal screening data processing device, including a memory for storing computer programs;

[0033] A processor is used to implement the steps of the prenatal screening data processing method when executing the computer program.

[0034] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the prenatal screening data processing method.

[0035] This application provides a prenatal screening data processing system, including: a risk assessment unit, a data update unit, and a database. The risk assessment unit is used to acquire and parse testing instructions to obtain maternal screening data and a maternal risk assessment plan, and to acquire standard parameter information corresponding to the maternal risk assessment plan from the database to process the maternal screening data according to the standard parameter information, thereby obtaining a risk assessment result. The data update unit is used to update the standard parameter information in the database when the standard parameter information meets a first preset condition. Therefore, the technical solution provided by this application, by storing the standard parameter information in the database, allows the data update unit to update the standard parameter information and the risk assessment unit to acquire the standard parameter information from the database to process the testing data, thereby achieving real-time parameter updates and improving the accuracy of prenatal screening data processing.

[0036] In addition, this application also provides a method, apparatus, and medium for processing prenatal screening data, which corresponds to the above-mentioned prenatal screening data processing system and has the same effect. Attached Figure Description

[0037] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 A flowchart illustrating a prenatal screening data processing method provided in this application embodiment;

[0039] Figure 2 This is an architecture diagram of a prenatal screening data processing system provided in an embodiment of this application;

[0040] Figure 3 An architectural diagram of a risk assessment unit provided in an embodiment of this application;

[0041] Figure 4 A flowchart illustrating the workflow of a prenatal screening data processing system provided in this application embodiment;

[0042] Figure 5 This is a structural diagram of a prenatal screening data processing device provided in an embodiment of this application;

[0043] The attached diagram is labeled as follows: 1 represents the risk assessment unit, 2 represents the data update unit, and 3 represents the database. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0045] The core of this application is to provide a prenatal screening data processing system, method, device, and medium, so that users can adjust the detection parameters, thereby improving the accuracy of prenatal screening data processing.

[0046] The prenatal screening data processing system provided in this application is applied in a computer or server to conduct prenatal screening for pregnant women, thereby reducing the incidence of trisomy syndrome and achieving the effect of eugenics. In the technical solution provided in this application, standard parameter information is stored in a database, allowing the data update unit to update the standard parameter information and the risk assessment unit to obtain the standard parameter information from the database to process the test data, thus achieving real-time parameter updates and improving the accuracy of prenatal screening data processing.

[0047] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Figure 1 This application provides a flowchart of a prenatal screening data processing method, which is applied to a prenatal screening data processing system including a risk assessment unit, a data update unit, and a database. Figure 1 As shown, the risk assessment methods include:

[0049] S10: Obtain and parse the testing instructions to obtain maternal screening data and maternal risk assessment plans;

[0050] S11: Process the maternal screening data according to the standard parameter information to obtain the risk assessment results; wherein, the standard parameter information is the information in the database that corresponds to the maternal risk assessment plan and is updated by the data update unit when the standard parameter information meets the first preset condition.

[0051] In practice, the risk assessment unit receives and parses the testing instructions sent by the user to obtain the risk assessment plan and testing data contained within the instructions. This allows for the acquisition of the assessment results for the tested individual based on the testing data. It is important to note that the "user" mentioned in this application is not the person receiving the testing data, but rather the equipment operator performing the risk assessment. After obtaining the tested individual's data via the network or other testing equipment, the equipment operator inputs the data into the prenatal screening data processing system in the form of testing instructions to determine whether the fetus has any corresponding conditions.

[0052] Understandably, since the types of diseases the system can screen are limited and fixed, and the types of other testing equipment that operators can access are also fixed, the types of testing instructions input by operators and the sets of testing data included in those instructions can be obtained in advance. Therefore, to improve testing efficiency and accuracy, standard parameter information in the database is stored in the form of parameter documents. When the risk assessment unit receives and parses the testing instructions, it obtains a risk assessment plan. The risk assessment unit then retrieves the parameter document corresponding to the risk assessment plan from the database to evaluate the testing data. The initial standard parameter information is determined based on standard data.

[0053] In practice, we determine the corresponding risk assessment plan by collecting client needs and establishing standard parameter information documents corresponding to the risk assessment plan. For example, we determine the client's choices regarding screening risk items (T21 / T18 / T13 / TS / NTD) and the selection of biochemical markers. The mainstream biochemical markers include PAPPA / FBHCG / PIGH / AFP / HCG / UE3 / INHIBNA, and ultrasound scan markers mainly include NT and NB, and the population as a whole. Specifically, the biochemical markers (ultrasound scan markers) for early pregnancy are PAPPA / FBHCG / PIGF / NT / NB, while those for mid-pregnancy mainly include AFP / HCG / FBHCG / UE3 / INHIBNA. NB needs to be included in the calculation based on NT, and generally, HCG and FBHCG are not included in the calculation simultaneously in mid-pregnancy. In addition, screening items can be freely and randomly combined into single-item screenings to multi-item screenings with varying parameters, based on client habits and screening efficiency. Furthermore, we support combined screening of early and mid-pregnancy items with varying parameters. Based on the hospital's comprehensive emphasis on false positive and false negative rates, a risk cutoff value is set, and a personalized optimal screening plan is provided to each client.

[0054] In this application, to ensure the accuracy of the risk assessment results, the standard parameter information is continuously updated through a parameter update unit. In specific implementation, to further improve the accuracy of the assessment results, newly generated test data can also be used to update the standard parameter information. When equipment operators process the test data by accessing the latest parameter document, they can further update the standard parameter information document based on the processing results.

[0055] Figure 4 A flowchart of a prenatal screening data processing system provided in this application embodiment is shown below. Figure 4 As shown, the main architecture of the algorithm interpreter in the prenatal screening data processing system is implemented using the Interpreter pattern. The Interpreter pattern is a behavioral design pattern that defines a language and an interpreter for that language, through which specific language statements are interpreted and executed.

[0056] In prenatal screening algorithm libraries, the interpreter pattern can be used to parse text information in parameter files. Specifically, the parser can extract the text information from the parameter file according to the function calculation order, function model, parameter coefficients, etc. For example, for "a+b", the parser can retrieve the corresponding values ​​of "a" and "b" from the input information, call the corresponding calculator, and calculate the final result.

[0057] The main process controller receives and saves input information, then uses this information to parse the main process controller. The parsing process specifies the order of gestational age calculation, MOM value calculation, pre-natal risk calculation, and risk calculation. Sub-process controllers, in conjunction with the input information and the current stage of the main process, parse specific sub-processes from the sub-process controllers, such as the last menstrual period calculation process within gestational age calculation. Based on the obtained sub-process controllers, the specific function execution paths within them are parsed. Following the order of the function calculation paths, the interpreter parses each function sequentially and transmits the parsed information to the calculator. The calculator then performs calculations based on the input information and returns the results.

[0058] It should be noted that the standard parameter information mentioned in this application includes the standard parameter information of all serum markers involved in the detection process during the pregnant woman's test, which will not be repeated here.

[0059] This application provides a method for processing prenatal screening data, comprising: a risk assessment unit for acquiring and parsing testing instructions to obtain maternal screening data and a maternal risk assessment plan, and acquiring standard parameter information corresponding to the maternal risk assessment plan from a database to process the maternal screening data based on the standard parameter information, thereby obtaining a risk assessment result; and a data update unit for updating the standard parameter information in the database when the standard parameter information meets a first preset condition. Therefore, the technical solution provided in this application, by storing the standard parameter information in a database, facilitates the data update unit's updating of the standard parameter information and the risk assessment unit's acquisition of the standard parameter information from the database to process the testing data, thereby achieving real-time parameter updates and improving the accuracy of prenatal screening data processing.

[0060] In specific implementation, determining whether the standard parameter information meets the first preset condition includes: acquiring historical test data and analyzing the historical test data to obtain statistical information of each test item; wherein, the statistical information includes the median information of each test item at each gestational week; determining whether the error between the statistical information and the standard value is greater than the error threshold; if it is greater than the error threshold, then it is determined that the standard parameter information meets the first preset condition.

[0061] Historical detection data for each serum marker was analyzed to obtain gestational age and median concentration information for each marker. In practice, after reviewing numerous literatures on median correction, a median error threshold of 10% was set. The mean of the medians corresponding to multiple gestational ages was calculated. If the deviation of the mean from the standard value 1 was greater than 10%, the fitting equation of the gestational age versus median function needed adjustment; otherwise, no adjustment of the relevant parameters for that serum marker was required.

[0062] In a preferred embodiment, the data update unit updates the standard parameter information when the standard parameter information meets the first preset condition, including: performing least squares curve fitting on historical detection data using a curve function; obtaining the curve function with the highest fitting degree as the fitting function, wherein the fitting function is used to characterize the gestational age-concentration relationship of the target analyte; determining the standard parameter information based on the fitting function, and updating the standard parameter information in the database.

[0063] In practice, various curve functions, including simple linear functions, quadratic curve functions, cubic curve functions, growth curve functions, logarithmic curve functions, exponential curve functions, and S-curve functions, are used to perform least-squares curve fitting on the median gestational age of the same test sample. The goodness of fit of different curves is compared to obtain the optimal curve function, and the standard parameter information of the serum marker is determined based on the optimal fitting curve.

[0064] It is understandable that even if the standard parameters currently used meet the first preset condition, this may be due to the specificity of the data and does not necessarily indicate the existence of a function with a higher fit. Therefore, the newly obtained fit may be worse than the original function. Thus, adjustment is only necessary if the optimal curve fit is worse than the original function's fit; otherwise, the fitting function for the serum marker does not need to be adjusted.

[0065] It should be noted that although the influence of gestational age on serum marker concentration is eliminated by function fitting of the relationship between gestational age and median concentration, other factors may still affect the detection process. Therefore, it is necessary to verify the obtained standard parameter information to determine whether the standard parameter information needs to be updated.

[0066] In a preferred embodiment, after determining the standard parameter information based on the fitting function, the method further includes: determining whether the current MOM value meets the second preset condition through a correlation analysis method; if the second preset condition is met, then correcting the MOM value based on the standard parameter information.

[0067] Furthermore, before updating the standard parameter information in the database, the process includes: performing a normal distribution test on the corrected MOM value; if the MOM value meets the third preset condition, then the step of updating the standard parameter information in the database is executed, and the database log is updated to record the update operation.

[0068] After eliminating the influence of gestational age, and drawing on authoritative research, correlation analysis methods such as ANOVA were used to analyze whether other influencing factors of the MOM value were biased. These influencing factors mainly included weight, race, smoking, and method of conception. When the correlation deviated significantly from the original correction parameters, new correlation parameters were used to correct the MOM value.

[0069] The original serum markers were used to calculate a new MOM value using the new gestational age median and calibration parameters.

[0070] The optimization effect of the median function should consider not only the fit effect but also whether the MOM value calculated by the new gestational age median curve function completely eliminates the influence of gestational age and whether the transformation optimizes the performance indicators of the normal distribution. The logarithmic values ​​of the original MOM value and the newly generated MOM value are calculated, and a Kolmogorov-Smirnov test for normality is performed. The results of the probability values ​​P are compared. If the P value of the newly generated MOM value is larger than that of the original MOM value, it indicates that the MOM value after processing with the new function parameters better satisfies or is closer to a normal distribution. In this case, the parameters related to the marker are updated; otherwise, the parameters are not adjusted.

[0071] If the parameters do not need to be adjusted, provide the reasons for not adjusting them. If adjustments are needed, compare the two versions of the parameters, calculate the mean, median, and mode of the gestational age of the MOM values, and compare the changes in the final prediction results for high and low risks under different parameters, for reference in subsequent parameter optimization.

[0072] Figure 2 This application provides an architecture diagram of a prenatal screening data processing system, as shown in the embodiments below. Figure 2 As shown, the prenatal screening data processing system includes: a risk assessment unit, a data update unit, and a database;

[0073] The risk assessment unit is used to obtain and parse the testing instructions to obtain the risk assessment plan and testing data, and to obtain the standard parameter information corresponding to the risk assessment plan from the database so as to process the testing data according to the standard parameter information, thereby obtaining the assessment result.

[0074] The data update unit is used to update the standard parameter information in the database when the standard parameter information meets the first preset condition.

[0075] like Figure 2 As shown, the software's overall structure consists of three parts: a risk assessment unit, a data update unit, and a database serving as an intermediary. The data update unit updates relevant parameter documents based on customer needs and historical data, and stores these documents in the database. The risk assessment unit then retrieves the parameter documents from the database, performs calculations, and stores the results as historical data.

[0076] Figure 3 An architectural diagram of a risk assessment unit provided in an embodiment of this application is shown below. Figure 3 As shown, the risk assessment unit system architecture consists of three layers: algorithm interface, algorithm manager, and algorithm interpreter. The algorithm interface is the entry point for receiving data from the outside and the starting point for internal data input. The algorithm manager is used to manage algorithm operation monitoring, algorithm version management, and the algorithm interpreter. The algorithm interpreter uses a method of one interpreter calling, parsing, and translating multiple algorithms to invoke various algorithms.

[0077] The algorithm operation monitoring section mainly includes six parts: input error verification, flow control error parsing, parser call error, calculator call error, timeout judgment, and calculation anomaly monitoring.

[0078] Output error checking mainly verifies whether missing fields are entered, whether the input of specific range values ​​meets the requirements, whether the input of serum markers meets the gestational age requirements, whether the ultrasound markers match a certain ultrasound indicator, and whether the screening combination exists.

[0079] From the perspective of the process path, parsing errors can be categorized into main process parsing errors, sub-process parsing errors, and function path parsing errors. From the perspective of specific error types, parsing errors can be divided into five categories: file existence, file reading, keyword errors, and parsing category errors.

[0080] An interpreter call error mainly refers to a situation where the content parsed by the interpreter does not meet the calculator's input format requirements.

[0081] Calculator call errors mainly refer to errors such as incorrect data type conversion, non-existent data types, or incorrect calculation symbols.

[0082] Timeout detection means that starting from the input, if the total calculation time does not exceed the preset time length, the time monitoring will not be triggered. If the time spent in the intermediate calculation stage exceeds the preset time length, the current calculation node and related information will be displayed.

[0083] Calculation anomaly monitoring mainly includes two aspects: mathematical operation anomaly monitoring and calculation value range anomaly monitoring. Mathematical anomaly monitoring is used to monitor illegal numbers (such as NAN); calculation range anomaly monitoring mainly involves whether the calculation exceeds the calculation range, such as an S-shaped curve exceeding the minimum or maximum calculation range.

[0084] The algorithm version management section comprises three parts: historical version storage, version information recording, and update information recording. The historical version storage section saves all historical parameter data used by the customer since its inception, allowing users to view the data optimization process and temporarily restore the device using historical standard parameter information when current parameter data becomes abnormal. Version information recording primarily documents the reasons for each version update and related reports. This includes the initial historical version's base data version information, whether it is the original version, the version's generation date, the optimization reason, a report on the customer data on which the optimization was based (data time span, data volume, and some basic statistical information), and an optimization effect report (optimization effect and related statistical reports). Update information recording primarily documents the order of version migrations, the reasons for migrations, and the related migration effects.

[0085] In practical implementation, after the risk assessment unit processes the test data based on standard parameter information, it also includes:

[0086] The test data is saved to the database so that the data update unit can use the test data to update the standard parameter information.

[0087] This application provides a prenatal screening data processing system, including: a risk assessment unit, a data update unit, and a database. The risk assessment unit is used to acquire and parse testing instructions to obtain maternal screening data and a maternal risk assessment plan, and to acquire standard parameter information corresponding to the maternal risk assessment plan from the database to process the maternal screening data according to the standard parameter information, thereby obtaining a risk assessment result. The data update unit is used to update the standard parameter information in the database when the standard parameter information meets a first preset condition. Therefore, the technical solution provided by this application, by storing the standard parameter information in the database, allows the data update unit to update the standard parameter information and the risk assessment unit to acquire the standard parameter information from the database to process the testing data, thereby achieving real-time parameter updates and improving the accuracy of prenatal screening data processing.

[0088] The study targeted the risk of Down syndrome (T21). Based on the training parameters of the original data, data from two hospitals over two years were collected and analyzed. Annual parameter updates were applied to both hospitals, and the mean detection rate and false positive rate of the model under these updated parameters were compiled, as shown in the table below. Specifically, for the early pregnancy triple test + NB (HCGB + PAPPA + NT + NB) screening combination, Hospital A achieved an average detection rate of 94.70% and a false positive rate of 4.31%; Hospital B achieved a detection rate of 93.89% and a false positive rate of 4.16%. For the early pregnancy quadruple test (HCGB + AFP + UE3 + INHIBINA) screening combination, Hospital A achieved a mean detection rate of 82.76% and a false positive rate of 4.53%; Hospital B achieved a mean detection rate of 81.46% and a false positive rate of 4.84%. These data demonstrate that the parameter update method can relatively stably maintain a high detection rate and a low false positive rate.

[0089] Table 1 Experimental Results

[0090]

[0091] In the above embodiments, the prenatal screening data processing method has been described in detail. This application also provides embodiments corresponding to the prenatal screening data processing device. It should be noted that this application describes the embodiments of the device from two perspectives: one is based on functional modules, and the other is based on hardware.

[0092] The risk assessment device provided in this application also includes: a MOM value correction unit, a normal distribution test unit, and a test data storage unit.

[0093] The MOM value correction unit is used to determine whether the current MOM value meets the second preset condition through correlation analysis; if the second preset condition is met, the MOM value is corrected according to the standard parameter information.

[0094] The normality test unit is used to perform a normality test on the corrected MOM value; if the MOM value meets the third preset condition, the step of updating the standard parameter information in the database is executed, and the database log is updated to record the update operation.

[0095] The test data storage unit is used to save the test data to the database so that the data update unit can use the test data to update the standard parameter information.

[0096] Figure 5 This is a structural diagram of a prenatal screening data processing device provided in an embodiment of this application, as shown below. Figure 5 As shown, the prenatal screening data processing device includes: a memory 20 for storing computer programs;

[0097] The processor 21 is used to execute a computer program to implement the steps of the prenatal screening data processing method as described in the above embodiments.

[0098] The prenatal screening data processing device provided in this embodiment may include, but is not limited to, smartphones, tablets, laptops, or desktop computers.

[0099] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0100] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 20 is used to store at least the following computer program 201, which, after being loaded and executed by the processor 21, is capable of implementing the relevant steps of the prenatal screening data processing method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, and the storage method may be temporary or permanent storage. The operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, test data, standard parameter information, etc.

[0101] In some embodiments, the prenatal screening data processing device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0102] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on the prenatal screening data processing device and may include more or fewer components than shown.

[0103] The prenatal screening data processing device provided in this application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the following method:

[0104] Obtain and parse the testing instructions to obtain maternal screening data and maternal risk assessment plans;

[0105] The maternal screening data is processed according to the standard parameter information to obtain the risk assessment results; wherein, the standard parameter information is the information in the database that corresponds to the maternal risk assessment plan and is updated by the data update unit when the standard parameter information meets the first preset condition.

[0106] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiments.

[0107] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0108] The prenatal screening data processing method, apparatus, medium, and system provided in this application have been described in detail above. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0109] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for processing prenatal screening data, characterized in that, include: Obtain and parse the testing instructions to obtain maternal screening data and maternal risk assessment plans; The maternal screening data is processed according to standard parameter information to obtain risk assessment results; wherein, the standard parameter information is information in the database corresponding to the maternal risk assessment scheme and is updated by the data update unit when the standard parameter information meets the first preset condition; The process of parsing the detection instructions to obtain a maternal risk assessment plan also includes: The detection instructions are parsed and the needs of the mother are collected to obtain the maternal risk assessment plan; Determining whether the standard parameter information meets the first preset condition includes: Historical testing data is acquired and analyzed to obtain statistical information for each test item; wherein, the statistical information includes the median information for each test item at each gestational week; Determine whether the error between the statistical information and the standard value is greater than the error threshold; If the error is greater than the error threshold, then the standard parameter information is determined to meet the first preset condition; The data update unit updates the standard parameter information when the standard parameter information meets the first preset condition, including: The historical detection data were fitted using a curve function with least squares curve fitting. The curve function with the highest fitting degree is obtained as the fitting function, wherein the fitting function is used to characterize the gestational age-concentration relationship of the target analyte; When the curve fitting effect of the fitting function is higher than that of the original median gestational age curve function, and the MOM value calculated by the fitting function optimizes the performance index of the normal distribution, the standard parameter information is updated.

2. The prenatal screening data processing method according to claim 1, characterized in that, The data update unit updates the standard parameter information when the standard parameter information meets the first preset condition, including: The standard parameter information is determined based on the fitting function, and the standard parameter information in the database is updated.

3. The prenatal screening data processing method according to claim 2, characterized in that, After the step of determining the standard parameter information based on the fitting function, the method further includes: Determine whether the current MOM value meets the second preset condition using correlation analysis; If the second preset condition is met, the MOM value is corrected according to the standard parameter information.

4. The prenatal screening data processing method according to claim 3, characterized in that, Before the step of updating the standard parameter information in the database, the method further includes: The corrected MOM values ​​were subjected to a normality test. If the MOM value meets the third preset condition, then the step of updating the standard parameter information in the database is executed, and the database log is updated to record the update operation.

5. The prenatal screening data processing method according to claim 1, characterized in that, The process of processing the maternal screening data based on standard parameter information includes: The standard parameter information is analyzed to obtain the gestational age calculation method; The maternal screening data is processed according to the gestational age calculation method to obtain the MOM values ​​of biochemical markers and ultrasound scans. Risk items are calculated based on the maternal risk assessment protocol, the MOM values ​​of the biochemical markers, and the MOM values ​​of the ultrasound scans to obtain the risk assessment results.

6. A prenatal screening data processing system, characterized in that, include: Risk assessment unit, data update unit, and database; The risk assessment unit is used to acquire and parse the testing instructions to obtain a risk assessment plan and testing data, and to acquire standard parameter information corresponding to the risk assessment plan in the database to process the testing data according to the standard parameter information, thereby obtaining an assessment result. The data update unit is used to update the standard parameter information in the database when the standard parameter information meets the first preset condition; The method of parsing the detection command to obtain a risk assessment plan also includes: The detection instructions are parsed and the needs of the mother are collected to obtain the risk assessment plan; Determining whether the standard parameter information meets the first preset condition includes: Historical testing data is acquired and analyzed to obtain statistical information for each test item; wherein, the statistical information includes the median information for each test item at each gestational week; Determine whether the error between the statistical information and the standard value is greater than the error threshold; If the error is greater than the error threshold, then the standard parameter information is determined to meet the first preset condition; The data update unit updates the standard parameter information when the standard parameter information meets the first preset condition, including: The historical detection data were fitted using a curve function with least squares curve fitting. The curve function with the highest fitting degree is obtained as the fitting function, wherein the fitting function is used to characterize the gestational age-concentration relationship of the target analyte; When the curve fitting effect of the fitting function is higher than that of the original median gestational age curve function, and the MOM value calculated by the fitting function optimizes the performance index of the normal distribution, the standard parameter information is updated.

7. The prenatal screening data processing system according to claim 6, characterized in that, The prenatal screening data processing system is also used for: The detection data is saved to the database so that the data update unit can use the detection data to update the standard parameter information.

8. A prenatal screening data processing device, characterized in that, Includes memory used to store computer programs; A processor, configured to execute the computer program to implement the steps of the prenatal screening data processing method as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the prenatal screening data processing method as described in any one of claims 1 to 5.

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