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Early prediction method for gestational diabetes mellitus

A prediction method and technology for diabetes, which can be applied to instruments, health index calculation, calculation models, etc., and can solve the problem of low sensitivity of identification.

Pending Publication Date: 2021-06-22
SECOND AFFILIATED HOSPITAL OF COLLEGE OF MEDICINEOF XIAN JIAOTONG UNIV
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Problems solved by technology

Results show that the OGTT test is discriminative for early GDM in high-risk pregnancies but less sensitive for this distinction in all pregnancies

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  • Early prediction method for gestational diabetes mellitus
  • Early prediction method for gestational diabetes mellitus
  • Early prediction method for gestational diabetes mellitus

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Embodiment Construction

[0032] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0033] The present invention specifically provides an early prediction method for gestational diabetes mellitus, the prediction method comprising the following steps:

[0034] Step S1: Establish a structured database, and retrospectively obtain the clinical index test results of the research subjects at 11-18 weeks of pregnancy through the hospital medical record system and the obstetrical medical record system, including 109 indicators;

[0035] Step S2: Preprocessing the indicators obtained from the detection in step S1;

[0036] (1) Referring to the indicators of the healthy group, delete the indicators with the same value; if the antibody results are all negative;

[0037] (2) Remove the indicators with a missi...

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Abstract

The invention discloses an early prediction method for gestational diabetes mellitus. The prediction method comprises the following steps: S1, obtaining clinical index test results of a research object in 11-18 weeks of pregnancy; s2, the indexes obtained through detection in the step S1 are preprocessed; s3, indexes related to gestational diabetes mellitus are selected, Fisher values are used for evaluating all the indexes, and sorting is carried out according to the Fisher values of all the indexes from large to small; s4, the first 11 indexes are selected to be input into the machine learning model, the machine learning model is trained according to the selected input indexes, and the predictive ability of the indexes for gestational diabetes mellitus is verified; s5, obtaining a prediction model of the gestational diabetes mellitus according to a result of the step S4; s6, substituting another group of new clinical index test results into the prediction model for verification so as to evaluate the accuracy of the prediction model; and S7, predicting whether the pregnant woman is sick or not through the prediction model.

Description

technical field [0001] The invention relates to the technical field of early diabetes prediction, in particular to an early prediction method for gestational diabetes. Background technique [0002] Gestational diabetes mellitus (GDM) refers to the development of hyperglycemia during pregnancy in women who did not have diabetes before pregnancy. Worldwide, the incidence of GDM is 1.8%-25.1%. GDM may increase the risk of preeclampsia, depression and cesarean delivery. Infants of mothers with poorly treated GDM are at increased risk of overweight, neonatal hypoglycemia, and jaundice. If left untreated, GDM may lead to premature labor, polyhydramnios, intrauterine infection, fetal malformation, or stillbirth. Studies have shown that the recurrence rate of GDM is as high as 48%, and 30-50% of women with GDM may develop diabetes in the future. [0003] China currently uses 75g oral glucose tolerance test (OGTT) to diagnose GDM. According to the recommendations of the World He...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20G16H50/30G06N20/00
CPCG16H50/20G16H50/30G06N20/00
Inventor 毛占热塔安娜嘎斯卡张若姚超
Owner SECOND AFFILIATED HOSPITAL OF COLLEGE OF MEDICINEOF XIAN JIAOTONG UNIV
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