What is Machine Learning? A Primer for the Epidemiologist

Abstract

Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database

“…Either deep learning algorithms were not used [ 19 , 21 , 29 , 30 , 33 , 36 ], the datasets were small [ 12 , 20 , 22 , 23 , 31 ], or the used computational software could not simultaneously employ a higher number of models [ 12 , 37 ]. Moreover, the global demand for machine learning solutions often exceeds the expertise of healthcare providers to effectively utilize ML [ 38 ].…”

Systematic review of methods used to study the intersecting impact of sex and social locations on health outcomes

“…In addition to quantifying the contribution of each characteristic, this approach differentiates between mediation effects due to disparate levels versus disparate effects of a mediator, providing a model that is applicable to a wide range of health-related inquiries. Machine learning or decision tree approaches constitute another proposed analytic framework ( Bi et al, 2019 ; Cairney et al, 2014 ). This one involves successive subgroupings of a sample based on ‘within group’ similarities in terms of the health outcome variable.…”

Early Stage Machine Learning–Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach

“…In supervised techniques, the outcome or dependent variable is available for a given set of observations. Supervised techniques are further divided into regression or classification techniques depending upon the data type of the outcome variable: continuous or categorical [ 40 ]. In the literature, artificial neural network–based deep learning and tree-based gradient tree–boosting techniques have demonstrated better prediction capabilities in exploring nonlinear relationships among correlated predictors [ 41 - 49 ].…”