Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis.

IF 3.3 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making Pub Date : 2025-01-13 DOI:10.1186/s12911-024-02848-x

Meng Zhao, Zhixin Yao, Yan Zhang, Lidan Ma, Wenquan Pang, Shuyin Ma, Yijun Xu, Lili Wei

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引用次数: 0

Abstract

Background: This systematic review aims to explore the early predictive value of machine learning (ML) models for the progression of gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM).

Methods: A comprehensive and systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science up to July 02, 2024. The quality of the studies included was assessed. The risk of bias was assessed through the prediction model risk of bias assessment tool and a graph was drawn accordingly. The meta-analysis was performed using Stata15.0.

Results: A total of 13 studies were included in the present review, involving 11,320 GDM patients and 22 ML models. The meta-analysis for ML models showed a pooled C-statistic of 0.82 (95% CI: 0.79 ~ 0.86), a pooled sensitivity of 0.76 (0.72 ~ 0.80), and a pooled specificity of 0.57 (0.50 ~ 0.65).

Conclusion: ML has favorable diagnostic accuracy for the progression of GDM to T2DM. This provides evidence for the development of predictive tools with broader applicability.

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机器学习对妊娠糖尿病发展为2型糖尿病的预测价值：一项系统综述和荟萃分析。

背景：本系统综述旨在探讨机器学习（ML）模型对妊娠期糖尿病（GDM）发展为2型糖尿病（T2DM）的早期预测价值。方法：全面系统检索Pubmed、Cochrane、Embase、Web of Science，检索时间截止到2024年7月2日。对纳入研究的质量进行了评估。通过预测模型偏倚风险评估工具对偏倚风险进行评估，并绘制相应的图表。meta分析采用Stata15.0进行。结果：本综述共纳入13项研究，涉及11320例GDM患者和22个ML模型。ML模型的meta分析显示，合并c统计量为0.82 (95% CI: 0.79 ~ 0.86)，合并敏感性为0.76(0.72 ~ 0.80)，合并特异性为0.57（0.50 ~ 0.65）。结论：ML对GDM向T2DM发展有较好的诊断准确性。这为开发具有更广泛适用性的预测工具提供了证据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Medical Informatics and Decision Making 医学-医学：信息

CiteScore

7.20

自引率

5.70%

发文量

297

审稿时长

1 months

期刊介绍： BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.