Disclosure of Invention
In view of this, the embodiment of the invention provides a gestational diabetes risk intelligent prediction method based on multi-mode data fusion, which performs physiological risk analysis and biochemical risk analysis respectively in the gestational diabetes detection process, performs gestational risk index calculation, maximally guarantees the combination of data detection and risk prediction, and performs the construction of a gestational risk change curve after performing gestational risk index calculation, thereby determining the risk occurrence time in the gestational period, and effectively solving the problems raised in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
S1, setting a prediction window, wherein the prediction window comprises an early pregnancy window, a middle pregnancy window and a late pregnancy window, and the early pregnancy window, the middle pregnancy window and the late pregnancy window are respectively marked as T1, T2 and T3, and the time of the prediction window is set according to the medical diagnosis and treatment standard of diabetes;
s2, acquiring risk detection data, namely extracting current pregnancy detection time from a prediction window, wherein the pregnancy detection time is in units of gestational weeks and is marked as 1,2 according to the gestational weeks, wherein the j is m, and j represents the j-th pregnancy detection time, so that risk detection data corresponding to each pregnancy detection time are acquired, the risk detection data comprise physiological risk indexes and biochemical risk indexes, and further pregnancy detection time periods corresponding to the risk detection data are acquired;
S3, constructing a multi-mode time sequence matrix, namely generating a time sequence corresponding to the risk detection data according to a preset sequence based on a pregnancy detection time period corresponding to the risk detection data, and constructing the multi-mode time sequence matrix according to the time sequence;
S4, risk analysis, namely respectively carrying out physiological risk analysis and biochemical risk analysis on the multi-mode time sequence matrix corresponding to each pregnancy detection time period, and analyzing pregnancy risk indexes of each pregnancy detection time period according to the physiological risk analysis and the biochemical risk analysis;
S5, constructing a pregnancy risk variation curve, namely performing association analysis on pregnancy risk indexes corresponding to each pregnancy detection time period and the pregnancy detection time period, so as to construct the pregnancy risk variation curve, and further determining risk occurrence time;
And S6, visually displaying, namely sending the risk occurrence time to a user terminal according to a preset summarization mode, wherein the preset summarization mode comprises report summarization, picture summarization and chart summarization, and a manager carries out key detection of gestational diabetes on the risk occurrence time through summarization contents, and the summarization contents comprise a multi-mode time sequence matrix, a gestational risk condition and a gestational risk change curve.
The invention has the technical effects and advantages that:
1. According to the invention, the wearable equipment and the pregnancy test terminal are used for detecting physiological risk indexes and biochemical risk indexes of gestational diabetes mellitus, and the multi-mode time sequence matrix is constructed based on risk detection data, so that multi-dimensional data acquisition is performed, the two-way influence assessment of the gestation period on the risks is facilitated, comprehensive and reliable data support is provided for determining the occurrence time of the risks, the unilaterality of a single index is avoided, on the other hand, the gestation test time is marked according to the gestation Zhou Shunxu, a time sequence is formed, the data acquisition accuracy can be met to the greatest extent, and a structural basis is laid for the follow-up risk variation trend and fluctuation range;
2. According to the invention, the physiological risk index and the biochemical risk index are strictly aligned on the time axis through the pregnancy detection time period, the problem of inconsistent detection frequencies of different modes is solved, and a matrix structure of the time period and multiple indexes is formed, so that the pregnancy risk and the detection time period are subjected to correlation analysis, a pregnancy risk change curve is constructed, the dynamic evolution of the risk along with the pregnancy period is visually displayed, the risk occurrence time can be accurately positioned through inflection point detection, the key detection of the risk occurrence time is facilitated, the gestational diabetes risk is reduced to the greatest extent, and a powerful detection reference is provided for the diabetes judgment of doctors.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The intelligent prediction method for gestational diabetes risk based on multi-mode data fusion as shown in the accompanying figures 1-3 comprises a wearable device, a labor detection terminal, a user terminal and a control center, wherein the wearable device and the labor detection terminal are connected with the user terminal through Bluetooth.
In a more specific application of the invention, the wearable device is used for monitoring physical sign states, in particular weight, height, resting heart rate and electrocardiosignals, wherein the wearable device can be an intelligent bracelet and is used for monitoring in combination with a heart rate monitoring device, and the heart rate monitoring device can be an electrocardio monitor for continuously monitoring the resting heart rate and the electrocardiosignals, for example, the weight and the height of a pregnant woman are continuously recorded through the intelligent bracelet.
The obstetric examination terminal is used for collecting insulin conditions of the pregnant women, and specifically can be an RFID reader, an insulin detector and an obstetric examination database, wherein the insulin conditions are that fasting blood glucose and insulin concentration of the pregnant women are monitored, monitoring results are stored in the obstetric examination database, and the insulin conditions of the pregnant women are automatically identified through the RFID reader.
The control center is used for analyzing and controlling related parameters based on the data of the equipment in the gestational diabetes detection process.
The specific embodiment of the invention comprises the following steps:
S1, setting a prediction window, wherein the prediction window comprises an early pregnancy window, a middle pregnancy window and a late pregnancy window, and is respectively marked as T1, T2 and T3, the time of the prediction window is set according to the medical diagnosis and treatment standard of diabetes, and the time of the prediction window is exemplified by 6-12 weeks of pregnancy, 24-28 weeks of pregnancy, and 36-40 weeks of pregnancy as T2.
It should be explained that the purpose of the setting of the prediction window is to scientifically define the key time range of data acquisition based on large physiological change of pregnancy, ensure timeliness, pertinence and standardization of multi-mode data, ensure that the early pregnancy window is the acquisition time of first time of pregnancy detection and is used for evaluating the risk factors of pre-pregnancy diabetes, the middle pregnancy window is the middle pregnancy, at the moment, insulin resistance obviously increases due to the fact that insulin resistance secreted by placenta reaches a peak value at the stage, therefore, the stage is the high-stage of gestational diabetes, the late pregnancy window is increased in risk of gestational diabetes due to fetal development, the pregnancy physiological index is changed along with pregnancy Zhou Dongtai, the detection of the non-key window is possibly limited in value due to gentle change, and the focus key sensitive window can highlight the index shock related to gestational diabetes.
And S2, acquiring risk detection data, namely extracting current pregnancy detection time from a prediction window, wherein the pregnancy detection time is in units of gestational weeks and is marked as 1,2 according to the gestational weeks, wherein the j is m, and j represents the j-th pregnancy detection time, so that risk detection data corresponding to each pregnancy detection time are acquired, the risk detection data comprise physiological risk indexes and biochemical risk indexes, and further a pregnancy detection time period corresponding to the risk detection data is acquired.
In this embodiment, it should be specifically described that the specific process of obtaining the physiological risk indicator includes:
Extracting electrocardiosignals through wearable equipment, and identifying R wave peak values according to the electrocardiosignals, so as to respectively calculate adjacent R wave intervals;
Calculating R wave root mean square based on adjacent R wave interval difference values, and further calculating all R wave interval standard deviations through the R wave root mean square;
Extracting weight and height, and obtaining a body weight index through a body weight index calculation formula, and further obtaining a weight increase rate through comparing the body weight index of the adjacent pregnancy detection time with the time interval of the adjacent pregnancy detection time;
The standard deviation of the R wave interval and the weight gain rate are integrated as physiological risk indexes.
It should be explained that the standard deviation of R wave interval and the weight gain rate are the core physiological indexes reflecting the functions and metabolic loads of the maternal autonomic nerves, the standard deviation of R wave interval is the standard deviation of heartbeat period, the greater the standard deviation of R wave interval is, the more balanced the activity of the autonomic nervous system is, the heart regulating ability is strong, the insulin secretion and the glucose metabolism are more stable, the smaller the standard deviation of R wave interval is, the autonomic nerves are disregulated, the insulin resistance is aggravated, the fasting blood sugar is increased, the risk of gestational diabetes is increased, the weight gain rate is the change of body mass index of the adjacent pregnancy detection time divided by the time interval, and when the rate is too fast, the fat cells are excessively proliferated, and the insulin signal path is directly inhibited, thereby increasing the incidence risk.
In this embodiment, it should be specifically described that the specific biochemical risk indicator acquiring process includes:
Identifying insulin condition of pregnant women through the labor detection terminal, and extracting fasting blood glucose and insulin concentration of the pregnant women;
Abnormal state marking is carried out on the fasting blood glucose of the pregnant women, and the abnormal state marking is specifically classified by a diagnosis standard:
,
Wherein, theMarking the result of the abnormal state of the pregnant woman at each pregnancy detection time,Indicating the fasting blood glucose of the pregnant woman at each pregnancy test time,AndRespectively obtaining fasting blood glucose standard values of various abnormal states according to the medical diagnosis and treatment standard of diabetes;
adding the abnormal state marking results of the pregnancy detection times, if the added results are not equal to 0, judging that the abnormal condition exists in the fasting blood glucose corresponding to the pregnancy detection times, otherwise, judging that the normal fasting blood glucose corresponding to the pregnancy detection times exists;
extracting abnormal fasting blood sugar corresponding to each pregnancy detection time, subtracting the fasting blood sugar corresponding to the adjacent pregnancy detection time in sequence, and obtaining an absolute value, and comparing the absolute value with the maximum value of the fasting blood sugar and the absolute value, thereby obtaining a blood sugar fluctuation coefficient;
Comparing the extracted insulin concentrations, subtracting insulin concentrations corresponding to adjacent pregnancy detection times respectively, and comparing the extracted insulin concentrations with adjacent pregnancy detection time intervals, thereby obtaining insulin secretion rates;
The blood glucose fluctuation coefficient and the insulin secretion rate are integrated as biochemical risk indexes.
It should be explained that, the blood glucose fluctuation coefficient and the insulin secretion rate are used as biochemical risk indexes, because the blood glucose fluctuation coefficient is the fluctuation range of blood glucose along with the gestation week, the insulin secretion rate is the dynamic compensation of insulin secretion, the higher the blood glucose fluctuation coefficient is, the higher the insulin secretion rate is, the short-term regulation capacity of insulin on blood glucose is reduced, which indicates that the higher the secretion compensation insulin resistance is increased by cells, the higher the biochemical risk is, and meanwhile, the change of the biochemical risk index in gestation period can be reflected by carrying out adjacent gestation detection time treatment on fasting blood glucose and insulin concentration corresponding to each gestation detection time, rather than carrying out single-time-point state detection to cause the condition that the blood glucose fluctuation is large but not overdetermined.
It should be further noted that, because the detection frequencies of the physiological risk indicator and the biochemical risk indicator are different, by extracting the current pregnancy detection time from the prediction window, different mode data can be forcedly aligned to the same time range, so as to avoid fusion errors caused by time dislocation, and meanwhile, after the physiological risk indicator and the biochemical risk indicator are acquired, different pregnancy detection times are compared to generate new detection times, which are respectively marked as 1,2, the first, the second, the third, the fourth, the fifth, the seventh, the eighth, the ninth, and the eighth, wherein j-1 represents a j-1 th pregnancy detection time period.
And S3, constructing a multi-mode time sequence matrix, namely generating a time sequence corresponding to the risk detection data according to a preset sequence based on a pregnancy detection time period corresponding to the risk detection data, and constructing the multi-mode time sequence matrix according to the time sequence.
In this embodiment, it is to be specifically described that the time sequence corresponding to the risk detection data includes a physiological risk time sequence and a biochemical risk time sequence, where the physiological risk time sequence arranges physiological risk indexes according to a preset sequence, and specifically indicates that: Wherein each time period corresponds to a physiological risk feature comprising an R-wave interval standard deviation and a weight gain rate, and the biochemical risk time sequence is generated by the same method, and is specifically expressed as follows: And further, aligning different modal data through pregnancy detection time periods, filling the missing detection time periods by adopting time sequence interpolation, and constructing a multi-modal time sequence matrix based on the physiological risk time sequence and the biochemical risk time sequence, wherein the multi-modal time sequence matrix is specifically expressed as follows:
,
Wherein, theRepresenting a multi-modal time series matrix, each row corresponding to a pregnancy detection period, each column corresponding to a modal feature,Representing the physiological risk characteristics corresponding to the m-1 th pregnancy detection period in the physiological risk time sequence,Representing the biochemical risk characteristics corresponding to the m-1 pregnancy detection time period in the biochemical risk time sequence.
It should be explained that, the time-series interpolation is a data processing method for solving the problem of data missing, and the accuracy of data analysis can be improved by the time-series interpolation, so as to improve the prediction result.
It should be further noted that, a time sequence corresponding to the risk detection data is generated according to a preset sequence, where the preset sequence is a sequence of pregnancy detection time periods corresponding to the risk detection data.
And S4, risk analysis, namely respectively carrying out physiological risk analysis and biochemical risk analysis on the multi-mode time sequence matrix corresponding to each pregnancy detection time period, and analyzing the pregnancy risk index of each pregnancy detection time period according to the physiological risk analysis and the biochemical risk analysis.
In the embodiment, the physiological risk analysis is specifically described as follows, physiological risk characteristics are extracted based on a multi-mode time sequence matrix corresponding to each pregnancy detection time period, wherein the physiological risk characteristics comprise an R wave interval standard deviation and a weight growth rate;
Comparing and calculating the standard deviation of the R wave interval and the weight increase rate to obtain the physiological abnormality risk coefficient corresponding to each pregnancy detection time period, wherein the physiological abnormality risk coefficient is specifically expressed as:
,
Wherein, theRepresents the physiological abnormality risk coefficient corresponding to each pregnancy detection time,Represents the standard deviation of the R-wave interval,Represents the standard deviation of the ideal R-wave interval,Indicating the rate of weight gain.
It should be explained that the physiological abnormality risk condition is greatly influenced by the heart rate variability, and is also influenced by the obesity degree of pregnant women, the heart rate variability is described by using the R wave interval standard deviation, if the R wave interval standard deviation is close to the ideal R wave interval standard deviation, the heart rate variability is large in the pregnancy detection time period, the obesity degree is described by using the weight increase rate, and if the weight increase rate is large, the physiological abnormality risk coefficient corresponding to the pregnancy detection time period is large.
The biochemical risk analysis is specifically described as extracting biochemical risk characteristics including blood sugar fluctuation coefficient and insulin secretion rate based on a multi-mode time sequence matrix corresponding to each pregnancy detection time period;
Comparing and calculating the blood sugar fluctuation coefficient and the insulin secretion rate to obtain a biochemical abnormality risk coefficient corresponding to each pregnancy detection time period, wherein the biochemical abnormality risk coefficient is specifically expressed as:
,
Wherein, theRepresenting the corresponding biochemical abnormality risk coefficient of each pregnancy detection time period,、Respectively represent the blood sugar fluctuation coefficient and the insulin secretion rate in biochemical risk characteristics,、The corresponding standard blood sugar fluctuation coefficient and insulin secretion rate are respectively expressed, and are known from the medical diagnosis and treatment standard of diabetes.
It should be explained that, the biochemical abnormality risk condition is analyzed through the blood sugar fluctuation coefficient and the insulin secretion rate, when the insulin secretion rate is slower, the blood sugar regulation capability is poorer in the pregnancy detection time period, at the moment, the blood sugar fluctuation is larger, and the larger the blood sugar fluctuation coefficient is, the larger the biochemical abnormality risk coefficient corresponding to the pregnancy detection time period is.
It should be further noted that, the pregnancy risk index is added with weights based on the physiological abnormal risk coefficient and the biochemical abnormal risk coefficient corresponding to each pregnancy detection period, specifically expressed as:
,
Wherein, theRepresents the pregnancy risk index corresponding to each pregnancy test period,AndRespectively represent the physiological abnormality risk coefficient and the biochemical abnormality risk coefficient of the m-1 pregnancy detection time period,AndThe weight coefficients corresponding to the physiological abnormal risk coefficient and the biochemical abnormal risk coefficient are respectively represented, and are set through the prediction windows corresponding to the pregnancy detection time periods, and if the prediction window corresponding to the pregnancy detection time period is T1, the weight coefficients corresponding to the physiological abnormal risk coefficient and the biochemical abnormal risk coefficient in the pregnancy detection time period are respectively 0.7 and 0.3.
And S5, constructing a pregnancy risk variation curve, namely performing association analysis on pregnancy risk indexes corresponding to each pregnancy detection time period and the pregnancy detection time period, thereby constructing the pregnancy risk variation curve and further determining risk occurrence time.
In the embodiment, a specific construction method of a pregnancy risk variation curve needs to be specifically explained, namely, a two-dimensional coordinate system is constructed by taking pregnancy detection time as a horizontal axis and taking a pregnancy risk index as a vertical axis, a plurality of points are marked in the constructed two-dimensional coordinate system aiming at the pregnancy detection time and the pregnancy risk index which are detected each time in each pregnancy detection time period to form the pregnancy risk variation curve in the pregnancy detection time period, and then the risk occurrence time is determined, wherein the specific determination is that inflection points are marked on the pregnancy risk variation curve in the pregnancy detection time period;
Dividing the pregnancy risk change curve into an ascending section, a descending section and a flat section based on marked inflection points, respectively counting the duty ratio of the ascending section and the amplitude of the ascending section, and sequentially numbering the ascending sections corresponding to the pregnancy risk change curve from front to back according to the positions of the ascending sections;
calculating the risk occurrence probability of each rising segment based on the rising segment duty ratio and the rising segment amplitude, and specifically associating the rising segment duty ratio and the rising segment amplitude through a probability calculation formula, wherein the probability calculation formula specifically comprises the following steps:
,
Wherein, theRepresenting the probability of risk occurrence for each rising segment,AndThe rising segment duty cycle and the rising segment amplitude are represented respectively,、AndRespectively representing risk fitting parameters, and estimating the fitting parameters by a maximum likelihood method;
and comparing the risk occurrence probabilities of the pregnancy risk change curves corresponding to the ascending segments in the pregnancy detection time periods, and screening the pregnancy detection time period corresponding to the maximum risk occurrence probability from the risk occurrence probabilities as risk occurrence time.
It should be further noted that the rising segment duty ratio and the rising segment amplitude are counted as follows:
Extracting pregnancy detection time of the rising section through a pregnancy risk change curve, and calculating to obtain the rising section duty ratio compared with the total pregnancy detection time;
And acquiring the slope of each ascending segment in the pregnancy risk change curve, and carrying out mean value calculation to obtain the amplitude of the ascending segment.
It should be understood that the basis for taking the rising period duty ratio and the rising period amplitude of the pregnancy risk variation curve in the pregnancy detection time period as the average risk variation probability is that the indexes comprehensively consider the variation trend of different stages in the pregnancy detection time, but not just pay attention to a certain time point, and the overall dynamic variation of the pregnancy risk can be more comprehensively estimated by considering the rising period duty ratio and the rising period amplitude, so that the gestational diabetes risk can be more accurately predicted.
And S6, visually displaying, namely sending the risk occurrence time to a user terminal according to a preset summarization mode, wherein the preset summarization mode comprises report summarization, picture summarization and chart summarization, and a manager carries out key detection of gestational diabetes on the risk occurrence time through summarization contents, and the summarization contents comprise a multi-mode time sequence matrix, a gestational risk condition and a gestational risk change curve.
In the drawings of the disclosed embodiments, only the structures related to the embodiments of the present disclosure are referred to, and other structures can refer to the common design, so that the same embodiment and different embodiments of the present disclosure can be combined with each other without conflict;
finally, the foregoing description of the preferred embodiment of the invention is provided for the purpose of illustration only, and is not intended to limit the invention to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.