Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes

AbstractPurpose of ReviewMachine 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 r esearch 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 FindingsMachine 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, particul arly 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.SummaryMachine learning approaches have the potential to improve risk stratification and outcome prediction for clinical epid...
Source: Current Diabetes Reports - Category: Endocrinology Source Type: research