CN112289435B - Gestational diabetes screening system based on machine learning and physical examination data - Google Patents
Gestational diabetes screening system based on machine learning and physical examination data Download PDFInfo
- Publication number
- CN112289435B CN112289435B CN202011100897.7A CN202011100897A CN112289435B CN 112289435 B CN112289435 B CN 112289435B CN 202011100897 A CN202011100897 A CN 202011100897A CN 112289435 B CN112289435 B CN 112289435B
- Authority
- CN
- China
- Prior art keywords
- module
- data
- historical
- measuring
- glycosylated hemoglobin
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
The invention belongs to the technical field of information processing, and discloses a gestational diabetes screening system and a method based on machine learning and physical examination data, wherein a parameter setting module is used for setting a pre-pregnancy weight index, glycosylated hemoglobin and a reference value of blood sugar; the historical data input module is used for inputting the height, the weight, the social and demographic data, the family history, the medical history, the allergy history and the living habits of the physical examiner; the data acquisition module is used for historical data, pre-pregnancy weight index, glycosylated hemoglobin and blood glucose data; the data processing module is used for comparing and analyzing the data; the display module is used for displaying the progestational precursor weight index, the glycosylated hemoglobin and the blood glucose data. The physical conditions of physical examination people are preliminarily known through life style, diseases and the like, and the gestational diabetes is judged through the measurement results of the weight index of pregnancies, the glycosylated hemoglobin and the blood sugar; the system has simple composition and convenient operation, and has important significance for screening the diabetes of the pregnant women.
Description
Technical Field
The invention belongs to the technical field of information processing, and particularly relates to a gestational diabetes screening system based on machine learning and physical examination data.
Background
Gestational diabetes is the disease with the highest incidence in gestational period, is a group of metabolic diseases characterized by hyperglycemia in the middle gestational period (24-28 weeks), not only causes huge fetus, hypoglycemia, dystocia and postpartum hemorrhage of newborn, but also increases cardiovascular diseases and metabolic disorders of two generations of mothers and children in the future. However, at present, a screening method for gestational diabetes mellitus is not available in the early gestational period, and a diagnosis standard is also unavailable, so that missed diagnosis and delayed treatment are easy to occur. At present, the national and international guidelines diagnose gestational diabetes with 24-28 weeks glucose tolerance (OGTT) blood sugar rise, and the adverse effect of hyperglycemia on the mother and the son generations is generated. Therefore, a screening system for gestational diabetes in the early stage of gestation must be found, an early warning guide is established, medical nutrition management is carried out in time, the purpose of blocking the forward shift of a checkpoint for gestational diabetes is achieved, and the occurrence of gestational diabetes is reduced.
In summary, the problems of the prior art are: at present, a method for screening the gestational diabetes mellitus in the early gestational period is not available, and medical nutrition intervention indication is not established in the early gestational period.
The difficulty of solving the technical problems is as follows: the gestational period is a special physiological period of the female life, and hormones secreted by the placenta have insulin resistance effect in order to adapt to the growth and development of the fetus. Easily causes disorder of energy metabolism and leads to increase of blood sugar. However, the screening and diagnosis standard of gestational diabetes is lacked before 24 weeks of gestation, and particularly under the condition that the blood sugar in early gestation is normal, the incidence rate of gestational diabetes in the middle gestation period (24 weeks to 28 weeks) cannot be predicted
The significance of solving the technical problems is as follows: the gestational diabetes screening early warning system is established in the early pregnancy so as to achieve the purpose of accurately intervening gestational diabetes, improve the quality of the birth population and reduce the poor perinatal fate of the mothers and the babies.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a gestational diabetes screening system based on machine learning and physical examination data.
The invention is realized in such a way that a gestational diabetes screening method based on machine learning and physical examination data comprises the following steps:
inputting a progestational precursor weight index, historical height, historical weight, historical waist circumference, historical blood pressure, historical glycosylated hemoglobin, historical blood glucose reference value, socio-demographic data, family history, medical history, allergy history and living habits of a physical examiner through a historical data input module;
acquiring standard body weight index, waist circumference, blood pressure, glycosylated hemoglobin and blood sugar reference data of the current height and age of the pregnant person through a parameter setting module;
thirdly, constructing a standard vector based on the historical data acquired in the first step and the related standard data acquired in the second step, and constructing a parameter setting model based on the related historical data and the constructed vector;
after setting the standard body mass index, waist circumference, blood pressure, glycosylated hemoglobin and blood sugar reference values of the height and age of the pregnant person, measuring the pre-pregnant body mass index, the glycosylated hemoglobin, the blood sugar, the waist circumference and the blood pressure of the physical examiner, which comprises the following steps:
the progestational precursor weight index determining module is used for determining the progestational precursor weight index of the physical examinee; the glycosylated hemoglobin measurement module measures glycosylated hemoglobin of a physical examination person through a mercurial glycosylated hemoglobin meter; the blood sugar measuring module measures the blood sugar of the physical examiner through the blood collecting device and the blood detecting device; the waistline measuring module measures the waistline of the pregnant person; the blood pressure measuring module measures the blood pressure of the pregnant person through a sphygmomanometer;
step four, the data acquisition module integrates a data acquisition tool set in the historical data input module, the body mass index measuring module, the glycosylated hemoglobin measuring module, the blood sugar measuring module, the waist circumference measuring module and the blood pressure measuring module;
implanting the data acquisition tool set into life cycle functions of a historical data input module, a body mass index measuring module, a glycosylated hemoglobin measuring module, a blood glucose measuring module, a waist circumference measuring module and a blood pressure measuring module by the data acquisition module;
step six, the central control module controls the data acquisition module to acquire channel data through context data in the historical data input module, the body mass index measuring module, the glycosylated hemoglobin measuring module, the blood glucose measuring module, the waistline measuring module and the blood pressure measuring module;
step seven, the central control module controls the data acquisition module to acquire user access data through a life cycle function of a frame in the rewriting historical data input module, the body mass index measuring module, the glycosylated hemoglobin measuring module, the blood glucose measuring module, the waist circumference measuring module and the blood pressure measuring module;
step eight, the central control module controls the data acquisition module to acquire bottom layer environment data of the intelligent terminal equipment through system interfaces of frames in the historical data input module, the weight measuring module, the glycosylated hemoglobin measuring module, the blood glucose measuring module, the waistline measuring module and the blood pressure measuring module;
step nine, the data acquisition module obtains the pre-pregnancy weight index, the historical height, the historical weight, the historical waist circumference, the historical blood pressure, the historical glycosylated hemoglobin, the historical blood glucose reference value, the social demographic data, the family history, the medical history, the allergy history, the living habits, the current glycosylated hemoglobin, the blood glucose, the waist circumference and the blood pressure data of the physical examinee based on the steps six to eight, and corresponding data summarization is carried out;
step ten, the data processing module compares and analyzes the information acquired by the data acquisition module; obtaining the conclusion of whether the gestational diabetes high risk group is existed or not; the display module displays the weight index of the progestational precursor, the height, the waist circumference, the blood pressure, the glycosylated hemoglobin and the blood sugar data through a display;
the data processing module compares and analyzes the information acquired by the data acquisition module and comprises the following steps:
acquiring a first matrix data set;
performing first dimension processing on the first matrix data set to obtain a second matrix data set;
performing second dimension processing on the second matrix data set to obtain a third matrix data set;
obtaining a fourth matrix data set from the first matrix data set and the third matrix data set;
quantizing the feature data of the fourth matrix data set to a specific area to obtain a feature data set;
comparing the data in the feature data set with corresponding target feature data;
and outputting a comparison result.
Further, the constructing a parameter setting model based on the relevant historical data and the constructed vector specifically includes: setting standard body weight index, waist circumference, blood pressure, glycosylated hemoglobin and blood sugar reference value of the height and age of the pregnant person.
Further, the ninth step is performed before: the central control module controls the data collection module to report collected data including at least a portion of the channel data, the user access data, and the underlying environmental data to the server.
Further, the method for determining the progestational precursor weight index of the physical examination person by the progestational precursor weight index determining module comprises the following steps:
the pre-pregnancy weight index determination module measures the weight and height data of the pregnant person by using a weight scale and a height measuring instrument, and calculates the weight index based on the following formula;
pre-pregnancy weight index = weight/height squared, in weight units: kg; the height unit is: and m is selected.
Further, the glycated hemoglobin measurement module measures glycated hemoglobin of a subject by a mercurial glycated hemoglobin meter twice.
Further, when the blood glucose measurement module collects blood glucose through the blood collection device, it is determined that the blood sample is obtained in an empty stomach.
Further, the method of waist circumference measurement module measuring waist circumference of a pregnant person comprises:
and measuring the circumference of the midpoint of the connecting line of the upper edge of the axillary midline ilium and the lower edge of the twelfth rib by using a measuring tape, and obtaining the waist circumference of the pregnant person.
Another object of the present invention is to provide a method for implementing the machine learning and physical examination data-based gestational diabetes screening, which includes:
the progestational precursor weight index measuring module is connected with the central control module and is used for measuring the progestational precursor weight index of the physical examiner;
the glycosylated hemoglobin measurement module is connected with the central control module and is used for measuring glycosylated hemoglobin of a physical examination person through a mercurial glycosylated hemoglobin meter;
the blood sugar measuring module is connected with the central control module and is used for measuring the blood sugar of the physical examinee through the blood collecting device and the blood detecting device;
the parameter setting module is connected with the central control module and is used for setting reference values of weight index of pregnancies, height, waistline, blood pressure, glycosylated hemoglobin and blood sugar;
the historical data input module is connected with the central control module and is used for inputting the progestational precursor weight index, the historical height, the historical weight, the historical waist circumference, the historical blood pressure, the historical glycosylated hemoglobin, the historical blood glucose reference value, the social demographic data, the family history, the medical history, the allergic history and the living habits of the physical examiner;
the data acquisition module is connected with the central control module and is used for acquiring historical data acquired by the historical data input module and summarizing the progestational body weight index, the glycated hemoglobin and the glycated hemoglobin measured by the progestational body weight index measuring module, the blood glucose measuring module, the waist circumference measuring module and the blood pressure measuring module;
the data processing module is connected with the central control module and is used for comparing and analyzing the information acquired by the data acquisition module;
the display module is connected with the central control module and is used for displaying the progestational precursor weight index, the height, the waist circumference, the blood pressure, the glycosylated hemoglobin and the blood sugar data through the display;
and the central control module is connected with the progestational precursor weight index measuring module, the glycosylated hemoglobin measuring module, the blood glucose measuring module, the parameter setting module, the historical data input module, the data acquisition module, the data processing module and the display module and is used for controlling the modules to normally work through the main control computer.
Further, the gestational diabetes screening system based on machine learning and physical examination data comprises:
the waist measuring module is connected with the central control module and is used for measuring the waist of the pregnant person;
and the blood pressure measuring module is connected with the central control module and is used for measuring the blood pressure of the pregnant person through a sphygmomanometer.
Another object of the present invention is to provide an information data processing terminal for implementing the method for screening gestational diabetes based on machine learning and physical examination data.
It is another object of the present invention to provide a computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method for gestational diabetes screening based on machine learning and physical examination data.
In summary, the advantages and positive effects of the invention are as follows: setting a progestational precursor weight index, glycosylated hemoglobin and a blood sugar reference value through a parameter setting module; the height, the weight, social and demographic data, family history, medical history, allergy history and living habits of the physical examiner are input through the historical data input module; the weight index of the pregnancies, the glycated hemoglobin and the blood sugar of the physical examinee are respectively measured by a pregnancies weight index measuring module, a glycated hemoglobin measuring module and a blood sugar measuring module; the physical condition of a physical examination person is preliminarily known through life style, diseases and the like, and the gestational diabetes is judged through the measurement results of the weight index of pregnancies, the glycosylated hemoglobin and the blood sugar; the screening system has simple composition and convenient operation, and has important significance for screening the diabetes of the pregnant women.
Drawings
Fig. 1 is a flowchart of a method for screening gestational diabetes based on machine learning and physical examination data according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a system for screening gestational diabetes based on machine learning and physical examination data according to an embodiment of the present invention;
in the figure: 1. a progestational precursor weight index determination module; 2. a glycated hemoglobin measurement module; 3. a blood glucose measurement module; 4. a parameter setting module; 5. a historical data input module; 6. a data acquisition module; 7. a data processing module; 8. a display module; 9. a central control module; 10. a waist circumference measuring module; 11. a blood pressure measuring module.
Fig. 3 is a flowchart of a data acquisition method according to an embodiment of the present invention.
Fig. 4 is a flowchart of a parameter setting method according to an embodiment of the present invention.
Fig. 5 is a flowchart of a data processing method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to solve the problems in the prior art, embodiments of the present invention provide a gestational diabetes screening system based on machine learning and physical examination data, which is described in detail below with reference to the accompanying drawings.
S101, inputting a pregnancies weight index, historical height, historical weight, historical waist circumference, historical blood pressure, historical glycosylated hemoglobin, historical blood glucose reference value, social demographic data, family history, medical history, allergy history and living habits of a physical examiner through a historical data input module;
s102, setting standard body weight indexes of the height and the age of the pregnant person, waist circumference, blood pressure, glycosylated hemoglobin and blood sugar reference values through a parameter setting module;
s103, the progestational precursor weight index determination module determines the progestational precursor weight index of the physical examinee; the glycosylated hemoglobin measurement module measures glycosylated hemoglobin of a physical examiner through a mercury glycosylated hemoglobin meter;
s104, the blood sugar measuring module measures the blood sugar of the physical examiner through the blood collecting device and the blood detecting device; the waist circumference measuring module measures the waist circumference of the pregnant person; the blood pressure measuring module measures the blood pressure of the pregnant person through a sphygmomanometer;
s105, acquiring a pregnancies body mass index, historical height, historical weight, historical waist circumference, historical blood pressure, historical glycosylated hemoglobin, a historical blood glucose reference value, social and demographic data, family history, medical history, allergy history, living habits, current glycosylated hemoglobin, blood glucose, waist circumference and blood pressure data of the physical examinee by a data acquisition module, and summarizing corresponding data;
s106, the data processing module compares and analyzes the information acquired by the data acquisition module to obtain the conclusion whether the gestational diabetes high risk group is present or not; the display module displays the weight index of the progestational precursor, the height, the waist circumference, the blood pressure, the glycosylated hemoglobin and the blood sugar data through the display.
As shown in fig. 2, the gestational diabetes screening system based on machine learning and physical examination data provided by the embodiment of the present invention includes:
the system comprises a pregnancies weight index measuring module 1, a glycosylated hemoglobin measuring module 2, a blood sugar measuring module 3, a parameter setting module 4, a historical data input module 5, a data acquisition module 6, a data processing module 7, a display module 8, a central control module 9, a waistline measuring module 10 and a blood pressure measuring module 11.
The progestational precursor weight index measuring module 1 is connected with the central control module and is used for measuring the progestational precursor weight index of the physical examinee;
a glycosylated hemoglobin measurement module 2 connected to the central control module for measuring glycosylated hemoglobin of the subject by a mercury glycosylated hemoglobin meter;
the blood sugar measuring module 3 is connected with the central control module and is used for measuring the blood sugar of the physical examiner through the blood collecting device and the blood detecting device;
the parameter setting module 4 is connected with the central control module and is used for setting reference values of weight index of progestational precursor, height, waist circumference, blood pressure, glycosylated hemoglobin and blood sugar;
the historical data input module 5 is connected with the central control module and is used for inputting the progestational precursor weight index, the historical height, the historical weight, the historical waist circumference, the historical blood pressure, the historical glycosylated hemoglobin, the historical blood glucose reference value, the social demographic data, the family history, the medical history, the allergic history and the living habits of the physical examiner;
the data acquisition module 6 is connected with the central control module and is used for acquiring historical data acquired by the historical data input module and summarizing the pre-pregnancy weight index measured by the pre-pregnancy weight index measurement module, the glycosylated hemoglobin measurement module, the blood sugar measurement module, the waist circumference measurement module and the blood pressure measurement module, the glycosylated hemoglobin, the blood sugar, the waist circumference and the blood pressure data;
the data processing module 7 is connected with the central control module and is used for comparing and analyzing the information acquired by the data acquisition module;
the display module 8 is connected with the central control module and is used for displaying the weight index of the progestational precursor, the height, the waistline, the blood pressure, the glycosylated hemoglobin and the blood sugar data through a display;
the central control module 9 is connected with the progestational precursor weight index measuring module, the glycosylated hemoglobin measuring module, the blood sugar measuring module, the parameter setting module, the historical data input module, the data acquisition module, the data processing module and the display module and is used for controlling the modules to normally work through the main control computer;
a waist measuring module 10 connected with the central control module 9 for measuring the waist of the pregnant person;
and a blood pressure measurement module 11 connected to the central control module 9 for measuring the blood pressure of the pregnant person by a blood pressure meter.
As shown in fig. 3, a method for data acquisition by the data acquisition module 6 provided by the embodiment of the present invention includes:
s201, integrating a data acquisition tool set in a historical data input module 5, a progestational precursor weight index measuring module 1, a glycosylated hemoglobin measuring module 2, a blood glucose measuring module 3, a waist circumference measuring module 10 and a blood pressure measuring module 11;
s202, implanting the initialization method of the data acquisition tool set into the life cycle functions of the historical data input module 5, the progestational mass index measuring module 1, the glycated hemoglobin measuring module 2, the blood glucose measuring module 3, the waist circumference measuring module 10, and the blood pressure measuring module 11;
s203, acquiring channel data through context data in the historical data input module 5, the progestational precursor weight index measuring module 1, the glycosylated hemoglobin measuring module 2, the blood sugar measuring module 3, the waist circumference measuring module 10 and the blood pressure measuring module 11;
s204, acquiring user access data by rewriting the life cycle functions of the frames in the historical data input module 5, the progestational precursor vital index measuring module 1, the glycated hemoglobin measuring module 2, the blood glucose measuring module 3, the waist circumference measuring module 10 and the blood pressure measuring module 11;
s205, acquiring bottom layer environment data of the intelligent terminal equipment through system interfaces of frames in the historical data input module 5, the pre-pregnancy weight index measuring module 1, the glycosylated hemoglobin measuring module 2, the blood sugar measuring module 3, the waistline measuring module 10 and the blood pressure measuring module 11;
s206, reporting the collected data including at least one part of the channel data, the user access data and the bottom layer environment data to a server.
As shown in fig. 4, the method for setting a parameter setting module according to the embodiment of the present invention includes:
s301, acquiring a pregnancies weight index, historical height, historical weight, historical waist circumference, historical blood pressure, historical glycosylated hemoglobin, historical blood glucose reference value, social and demographic data, family history, medical history, allergy history and living habits of the physical examinee;
s302, acquiring standard body weight index, waist circumference, blood pressure, glycosylated hemoglobin and blood sugar reference data of the current height and age of the pregnant person;
and S303, constructing a standard vector based on the historical data obtained in the step S301 and the related standard data obtained in the step S302, constructing a parameter setting model based on the related historical data and the constructed vector, and setting standard body weight indexes of the height and the age of the pregnant person, waist circumference, blood pressure, glycosylated hemoglobin and blood glucose reference values.
As shown in fig. 5, a method for data processing by the data processing module 7 according to the embodiment of the present invention includes:
s401, acquiring a first matrix data set;
s402, performing first dimension processing on the first matrix data set to obtain a second matrix data set;
s403, performing second dimension processing on the second matrix data set to obtain a third matrix data set;
s404, obtaining a fourth matrix data set according to the first matrix data set and the third matrix data set;
s405, quantizing the feature data of the fourth matrix data set to a specific area to obtain a feature data set;
s406, comparing the data in the characteristic data set with corresponding target characteristic data;
and S407, outputting the comparison result.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A method for screening gestational diabetes based on machine learning and physical examination data is characterized by comprising the following steps:
inputting a progestational precursor weight index, historical height, historical weight, historical waist circumference, historical blood pressure, historical glycosylated hemoglobin, historical blood glucose reference value, socio-demographic data, family history, medical history, allergy history and living habits of a physical examiner through a historical data input module;
acquiring standard body weight index, waist circumference, blood pressure, glycosylated hemoglobin and blood sugar reference data of the current height and age of the pregnant person through a parameter setting module;
thirdly, constructing a standard vector based on the historical data acquired in the first step and the related standard data acquired in the second step, and constructing a parameter setting model based on the related historical data and the constructed vector;
after setting standard body mass index, waist circumference, blood pressure, glycosylated hemoglobin and blood sugar reference data of the height and age of the pregnant person, measuring the pre-pregnant body mass index, the glycosylated hemoglobin, the blood sugar, the waist circumference and the blood pressure of the physical examiner, which comprises the following steps:
the progestational precursor weight index determining module is used for determining the progestational precursor weight index of the physical examinee; the glycosylated hemoglobin measurement module measures glycosylated hemoglobin of a physical examination person through a mercurial glycosylated hemoglobin meter; the blood sugar measuring module measures the blood sugar of the physical examiner through the blood collecting device and the blood detecting device; the waistline measuring module measures the waistline of the pregnant person; the blood pressure measuring module measures the blood pressure of the pregnant person through a sphygmomanometer;
step four, the data acquisition module integrates a data acquisition tool set in the historical data input module, the body mass index measuring module, the glycosylated hemoglobin measuring module, the blood sugar measuring module, the waist circumference measuring module and the blood pressure measuring module;
implanting the data acquisition tool set into life cycle functions of a historical data input module, a body mass index measuring module, a glycosylated hemoglobin measuring module, a blood glucose measuring module, a waist circumference measuring module and a blood pressure measuring module by the data acquisition module;
step six, the central control module controls the data acquisition module to acquire channel data through context data in the historical data input module, the body mass index measuring module, the glycosylated hemoglobin measuring module, the blood glucose measuring module, the waistline measuring module and the blood pressure measuring module;
step seven, the central control module controls the data acquisition module to acquire user access data through a life cycle function of a frame in the rewriting historical data input module, the body mass index measuring module, the glycosylated hemoglobin measuring module, the blood glucose measuring module, the waist circumference measuring module and the blood pressure measuring module;
step eight, the central control module controls the data acquisition module to acquire bottom layer environment data of the intelligent terminal equipment through system interfaces of frames in the historical data input module, the weight measurement module, the glycosylated hemoglobin measurement module, the blood glucose measurement module, the waist measurement module and the blood pressure measurement module;
step nine, the data acquisition module obtains the pregnancies weight index, the historical height, the historical weight, the historical waist circumference, the historical blood pressure, the historical glycosylated hemoglobin, the historical blood glucose reference value, the social demographic data, the family history, the medical history, the allergy history, the living habits, the current glycosylated hemoglobin, the blood glucose, the waist circumference and the blood pressure data of the physical examiner based on the steps six to eight, and performs corresponding data summarization;
step ten, the data processing module compares and analyzes the information acquired by the data acquisition module; obtaining the conclusion of whether the gestational diabetes high risk group is existed or not; the display module displays the weight index of the progestational precursor, the height, the waist circumference, the blood pressure, the glycosylated hemoglobin and the blood sugar data through a display;
the data processing module compares and analyzes the information acquired by the data acquisition module and comprises the following steps:
acquiring a first matrix data set;
performing first dimension processing on the first matrix data set to obtain a second matrix data set;
performing second dimension processing on the second matrix data set to obtain a third matrix data set;
obtaining a fourth matrix data set from the first matrix data set and the third matrix data set;
quantizing the feature data of the fourth matrix data set to a specific area to obtain a feature data set;
comparing the data in the feature data set with corresponding target feature data;
and outputting a comparison result.
2. The method for screening gestational diabetes mellitus based on machine learning and physical examination data as claimed in claim 1, wherein the parameter setting model is constructed based on the relevant historical data and the constructed vector, specifically: setting standard body weight index, waist circumference, blood pressure, glycosylated hemoglobin and blood sugar reference value of the height and age of the pregnant person.
3. The method for screening gestational diabetes mellitus based on machine learning and physical examination data as claimed in claim 2, wherein the pre-pregnancy weight index determination module determines the pre-pregnancy weight index of the physical examinee by:
the pre-pregnancy weight index determination module measures the weight and height data of the pregnant person by using a weight scale and a height measuring instrument, and calculates the weight index based on the following formula;
pre-pregnancy weight index = weight/height squared, in weight units: kg; the height unit is: and m is selected.
4. The method for screening gestational diabetes mellitus based on machine learning and physical examination data of claim 2, wherein the glycated hemoglobin measurement module measures glycated hemoglobin of the examinee with a mercurial glycated hemoglobin meter twice.
5. The method for screening gestational diabetes mellitus based on machine learning and physical examination data as claimed in claim 2, wherein the blood glucose determination module determines that the blood sample is taken on an empty stomach when collecting blood glucose through the blood collection device.
6. The method for gestational diabetes screening based on machine learning and physical examination data of claim 2, wherein the method for waist circumference measurement module to measure waist circumference of the pregnant person comprises:
and measuring the circumference of the midpoint of the connecting line of the upper edge of the axillary midline ilium and the lower edge of the twelfth rib by using a measuring tape, and obtaining the waist circumference of the pregnant person.
7. The method for screening gestational diabetes mellitus based on machine learning and physical examination data as claimed in claim 1, wherein the step nine is preceded by: the central control module controls the data collection module to report collected data including at least a portion of the channel data, the user access data, and the underlying environment data to the server.
8. A method of conducting the machine learning and physical examination data-based gestational diabetes screening of claims 1-7, wherein the machine learning and physical examination data-based gestational diabetes screening system comprises:
the progestational precursor weight index measuring module is connected with the central control module and is used for measuring the progestational precursor weight index of the physical examiner;
the glycosylated hemoglobin measurement module is connected with the central control module and is used for measuring glycosylated hemoglobin of a physical examination person through a mercurial glycosylated hemoglobin meter;
the blood sugar measuring module is connected with the central control module and is used for measuring the blood sugar of the physical examination person through the blood collecting device and the blood detecting device;
the parameter setting module is connected with the central control module and is used for setting reference values of weight index of progestational precursor, height, waist circumference, blood pressure, glycosylated hemoglobin and blood sugar;
the historical data input module is connected with the central control module and is used for inputting the progestational precursor weight index, the historical height, the historical weight, the historical waist circumference, the historical blood pressure, the historical glycosylated hemoglobin, the historical blood glucose reference value, the social demographic data, the family history, the medical history, the allergic history and the living habits of the physical examiner;
the data acquisition module is connected with the central control module and is used for acquiring historical data acquired by the historical data input module and summarizing the progestational body weight index, the glycated hemoglobin and the glycated hemoglobin measured by the progestational body weight index measuring module, the blood glucose measuring module, the waist circumference measuring module and the blood pressure measuring module;
the data processing module is connected with the central control module and is used for comparing and analyzing the information acquired by the data acquisition module;
the display module is connected with the central control module and is used for displaying the weight index of the progestational precursor, the height, the waistline, the blood pressure, the glycosylated hemoglobin and the blood sugar data through the display;
the central control module is connected with the progestational precursor weight index measuring module, the glycosylated hemoglobin measuring module, the blood sugar measuring module, the parameter setting module, the historical data input module, the data acquisition module, the data processing module and the display module and is used for controlling the modules to normally work through the main control computer;
further, the gestational diabetes screening system based on machine learning and physical examination data comprises:
the waist measuring module is connected with the central control module and is used for measuring the waist of the pregnant person;
and the blood pressure measuring module is connected with the central control module and is used for measuring the blood pressure of the pregnant person through a sphygmomanometer.
9. An information data processing terminal for implementing the method for screening gestational diabetes based on machine learning and physical examination data according to any one of claims 1 to 7.
10. A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method of gestational diabetes screening based on machine learning and physical examination data of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011100897.7A CN112289435B (en) | 2020-10-15 | 2020-10-15 | Gestational diabetes screening system based on machine learning and physical examination data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011100897.7A CN112289435B (en) | 2020-10-15 | 2020-10-15 | Gestational diabetes screening system based on machine learning and physical examination data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112289435A CN112289435A (en) | 2021-01-29 |
| CN112289435B true CN112289435B (en) | 2022-10-14 |
Family
ID=74497122
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011100897.7A Active CN112289435B (en) | 2020-10-15 | 2020-10-15 | Gestational diabetes screening system based on machine learning and physical examination data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112289435B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114496209B (en) * | 2022-02-18 | 2022-09-27 | 青岛市中心血站 | Intelligent decision-making method and system for blood donation |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102299556A (en) * | 2010-06-25 | 2011-12-28 | 苏州太谷电力有限公司 | Real-time monitoring system of electric equipment |
| CN104063583B (en) * | 2014-06-06 | 2017-06-20 | 北京工业大学 | The gestational diabetes risk monitoring system of of science and chemical factors based on goer |
| CN204705861U (en) * | 2015-06-26 | 2015-10-14 | 北京九州恒盛电力科技有限公司 | A kind of switchgear building intelligent data acquisition management system |
| CN107194138B (en) * | 2016-01-31 | 2023-05-16 | 北京万灵盘古科技有限公司 | Fasting blood glucose prediction method based on physical examination data modeling |
| CN109308545B (en) * | 2018-08-21 | 2023-07-07 | 中国平安人寿保险股份有限公司 | Method, device, computer equipment and storage medium for predicting diabetes probability |
| CN109524118A (en) * | 2018-11-01 | 2019-03-26 | 上海海事大学 | A kind of screen method for gestational diabetes based on machine learning and physical examination data |
-
2020
- 2020-10-15 CN CN202011100897.7A patent/CN112289435B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN112289435A (en) | 2021-01-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20210007675A1 (en) | Method of detecting sepsis using vital signs, including systolic blood pressure, hematology parameters, and combinations thereof | |
| CN104257353A (en) | Sleep apnea syndrome detecting system | |
| CN100453035C (en) | Assessment of fetal reactivity by fetal heart rate analysis | |
| CN115083607A (en) | Human health index condition monitoring and analyzing method, system and storage medium | |
| CN105877708A (en) | Smart bra for measuring and analyzing female basal body temperature and control method thereof | |
| CN112289435B (en) | Gestational diabetes screening system based on machine learning and physical examination data | |
| CN113077900A (en) | Diabetes early risk assessment method, device, computer equipment and medium | |
| CN113611419A (en) | Postpartum hemorrhage risk prediction method and early warning system based on fetal monitoring uterine contraction diagram and high-risk factors | |
| Huhn et al. | Quality predictors of abdominal fetal electrocardiography recording in antenatal ambulatory and bedside settings | |
| Sun et al. | Reference ranges of fetal heart function using a Modified Myocardial Performance Index: a prospective multicentre, cross-sectional study | |
| CN114758780A (en) | Postpartum hemorrhage prediction method, device, terminal and storage medium | |
| Turner et al. | Decreased fetal movements—the utility of ultrasound to identify infants at risk and prevent stillbirth is poor | |
| CN103519791A (en) | System and method for collecting human body temperature | |
| Zhang et al. | The influence of HbA1c and gestational weight gain on pregnancy outcomes in pregnant women with gestational diabetes mellitus | |
| CN115223706A (en) | Sepsis early stage screening model suitable for mobile monitoring equipment | |
| CN115239273A (en) | Intelligent informatization health management and analysis system for women and children | |
| Li et al. | Correlation between ductus venosus spectrum and right ventricular diastolic function in isolated single-umbilical-artery foetus and normal foetus in third trimester | |
| CN206892869U (en) | A kind of pregnant Nutrition Management system | |
| CN111803129A (en) | Pregnancy monitoring system and method | |
| CN117442212B (en) | Intelligent monitoring method for obstetrical nursing | |
| Yoles et al. | Pregnant women become sweeter: A population-based study of trends in glucose challenge test results over a decade | |
| CN116138749A (en) | Blood pressure instrument based on AI algorithm and used for real-time blood pressure prediction method | |
| CZ2009127A3 (en) | Method of timely recognition of premature labor risk consisting in monitoring uterine activity based on evaluating uterine contractions and circuit arrangement for making the method | |
| CN114391821B (en) | Monitor for automatically converting heart rate and blood pressure of fetus in intrauterine hypoxia detection | |
| US11202577B2 (en) | System for monitoring cardiovascular and brain function in combination with physiological detection device and method thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |