December 11, 2018
Machine learning is a branch of computer science that has the potential to transform epidemiological 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, followed by a brief introduction to five common machine learning algorithms and four ensemble-based approaches. We then summarize epidemiological applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiological research and discuss opportunities and challenges for integrating machine learning and existing epidemiological research methods.
Qifang Bi is a fourth year PhD student in the department of Epidemiology and is a concurrent MHS student in the department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. During her PhD, she's worked with the World Health Organization to develop Cholera Elimination Plan for the Ministry of Health in Zanzibar, and has authored a systematic review on the effectiveness of oral cholera vaccine, as part of a project for the WHO Global Task Force on Cholera Control. Her research focuses on understanding the spatio-temporal dynamics of cholera and dengue, and her dissertation explores the interactions between vaccination policy and the epidemiology of vaccine preventable diseases. Qifang received her B.S.E in biomedical engineering from Duke University in 2012.
