Machine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: A review

Mennickent, Daniela; Rodriguez, Antonio B.; Farías-Jofré, Marcelo; Araya, Juan; Guzmán-Gutiérrez, Enrique

doi:10.1016/j.artmed.2022.102378

Cited by 21 publications

(18 citation statements)

References 130 publications

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“…maternal age, body mass index (BMI), family history of diabetes, previous GDM, among others. Nevertheless, approaches that consider only in this type of parameters usually have a poor predictive performance for GDM [6][7][8]. Thus, other biomarkers should be investigated and tested in order to complement and boost them.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia

et al. 2023

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Maternal thyroid alterations have been widely associated with the risk of gestational diabetes mellitus (GDM). This study aims to 1) test the first and the second trimester full maternal thyroid profile on the prediction of GDM, both alone and combined with non-thyroid data; and 2) make that prediction independent of the diagnostic criteria, by evaluating the effectiveness of the different maternal variables on the prediction of oral glucose tolerance test (OGTT) post load glycemia. Pregnant women were recruited in Concepción, Chile. GDM diagnosis was performed at 24–28 weeks of pregnancy by an OGTT (n = 54 for normal glucose tolerance, n = 12 for GDM). 75 maternal thyroid and non-thyroid parameters were recorded in the first and the second trimester of pregnancy. Various combinations of variables were assessed for GDM and post load glycemia prediction through different classification and regression machine learning techniques. The best predictive models were simplified by variable selection. Every model was subjected to leave-one-out cross-validation. Our results indicate that thyroid markers are useful for the prediction of GDM and post load glycemia, especially at the second trimester of pregnancy. Thus, they could be used as an alternative screening tool for GDM, independently of the diagnostic criteria used. The final classification models predict GDM with cross-validation areas under the receiver operating characteristic curve of 0.867 (p<0.001) and 0.920 (p<0.001) in the first and the second trimester of pregnancy, respectively. The final regression models predict post load glycemia with cross-validation Spearman r correlation coefficients of 0.259 (p = 0.036) and 0.457 (p<0.001) in the first and the second trimester of pregnancy, respectively. This investigation constitutes the first attempt to test the performance of the whole maternal thyroid profile on GDM and OGTT post load glycemia prediction. Future external validation studies are needed to confirm these findings in larger cohorts and different populations.

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Section: Introductionmentioning

confidence: 99%

Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia

et al. 2023

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“…These diagnostic technologies predict GDM with high accuracy [12][13][14]. However, the related studies used medical records as their data source [15]. Medical records provide accurate medical histories and conditions along with several blood test results obtained during pregnancy [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Medical records provide accurate medical histories and conditions along with several blood test results obtained during pregnancy [12,13]. In contrast, birth cohort data, including information on lifestyle and living environment that is not typically included in medical records, are used for only a few AI studies examining the accuracy and e cacy of GDM prediction [14,15]. Birth cohort data include lifestyle and social factors, depending on the purpose of the survey [16,17].…”

Section: Introductionmentioning

confidence: 99%

Prediction of gestational diabetes mellitus using machine learning from birth cohort study data: The Japan Environment and Children's Study

Watanabe

Eguchi

Sakurai

et al. 2023

Preprint

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Recently, prediction of gestational diabetes mellitus (GDM) using artificial intelligence (AI) from medical records has been reported. We aimed to evaluate GDM-predictive AI-based models using birth cohort data with a wide range of information and to explore factors contributing to GDM development. This investigation was conducted as a part of the Japan Environment and Children's Study. In total, 82,698 pregnant mothers who provided data on lifestyle, anthropometry, and socioeconomic status before pregnancy and the first trimester were included in the study. We employed machine learning methods as AI algorithms, such as random forest (RF), gradient boosting decision tree (GBDT), and support vector machine (SVM), along with logistic regression (LR) as a reference. GBDT displayed the highest accuracy, followed by LR, RF, and SVM. In the GBDT model, the area under the receiver operating characteristic curve for GDM was 0.67 (95% CI, 0.59–0.75) for mothers with GDM history and 0.76 (95% CI, 0.74–0.78) for mothers without GDM history. The results of decision tree-based algorithms, such as GBDT, have shown high accuracy, interpretability, and superiority for predicting GDM using birth cohort data.

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“…There are several reviews that overview various machine learning and deep learning models for the classification of data-driven blood glucose patterns as well as the prediction of diabetes and hypoglycemia [30][31][32]. In this review, we survey the most recently developed machine learning and deep learning algorithms for various aspects of prediction, diagnosis, and management of all diabetes types in more detail.…”

Section: Introductionmentioning

confidence: 99%

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Afsaneh¹,

Sharifdini²,

Ghazzaghi³

et al. 2022

Diabetol Metab Syndr

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Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.

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Machine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: A review

Cited by 21 publications

References 130 publications

Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia

Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia

Prediction of gestational diabetes mellitus using machine learning from birth cohort study data: The Japan Environment and Children's Study

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

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