Purpose of Review

Machine learning approaches—which seek to predict outcomes or classify patient features by recognizing patterns in large datasets—are increasingly applied to clinical epidemiology research on diabetes. Given its novelty and emergence in fields outside of biomedical research, machine learning terminology, techniques, and research findings may be unfamiliar to diabetes researchers. Our aim was to present the use of machine learning approaches in an approachable way, drawing from clinical epidemiological research in diabetes published from 1 Jan 2017 to 1 June 2020.

Recent Findings

Machine learning approaches using tree-based learners—which produce decision trees to help guide clinical interventions—frequently have higher sensitivity and specificity than traditional regression models for risk prediction. Machine learning approaches using neural networking and “deep learning” can be applied to medical image data, particularly for the identification and staging of diabetic retinopathy and skin ulcers. Among the machine learning approaches reviewed, researchers identified new strategies to develop standard datasets for rigorous comparisons across older and newer approaches, methods to illustrate how a machine learner was treating underlying data, and approaches to improve the transparency of the machine learning process.

Summary

Machine learning approaches have the potential to improve risk stratification and outcome prediction for clinical epidemiology applications. Achieving this potential would be facilitated by use of universal open-source datasets for fair comparisons. More work remains in the application of strategies to communicate how the machine learners are generating their predictions.

中文翻译：

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机器学习方法在糖尿病临床流行病学研究中的应用

审查目的

机器学习方法试图通过识别大型数据集中的模式来预测结果或对患者特征进行分类，因此越来越多地应用于糖尿病的临床流行病学研究。鉴于其新颖性和在生物医学研究领域以外的新兴应用，机器学习术语，技术和研究结果可能对糖尿病研究者而言是陌生的。我们的目标是以2017年1月1日至2020年6月1日发表的糖尿病临床流行病学研究为基础，以一种通俗易懂的方式介绍机器学习方法的使用。

最近的发现

使用基于树的学习器（可生成决策树以帮助指导临床干预）的机器学习方法通​​常比用于风险预测的传统回归模型具有更高的敏感性和特异性。使用神经网络和“深度学习”的机器学习方法可以应用于医学图像数据，特别是用于糖尿病性视网膜病变和皮肤溃疡的识别和分期。在所审查的机器学习方法中，研究人员确定了开发标准数据集以对旧方法和新方法进行严格比较的新策略，说明机器学习者如何处理基础数据的方法以及提高机器学习过程透明度的方法。

概要

机器学习方法具有改善临床流行病学应用的风险分层和结果预测的潜力。通过使用通用开源数据集进行公平比较，将有助于实现这一潜力。在交流机器学习者如何生成预测的策略应用方面，还有更多工作要做。