Infection Control & Hospital Epidemiology (2018), 39, 1457–1462 doi:10.1017/ice.2018.265
Review
Introduction to Machine Learning in Digital Healthcare Epidemiology
Jan A. Roth MD1,2, Manuel Battegay MD1, Fabrice Juchler MD1, Julia E. Vogt PhD3,4,a and Andreas F. Widmer MD,
MS1,a 1Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Basel, Switzerland, 2Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Basel, Switzerland, 3Adaptive Systems and Medical Data Science, Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland and 4Swiss Institute of Bioinformatics, Basel, Switzerland
Abstract
To exploit the full potential of big routine data in healthcare and to efficiently communicate and collaborate with information technology specialists and data analysts, healthcare epidemiologists should have some knowledge of large-scale analysis techniques, particularly about machine learning. This review focuses on the broad area of machine learning and its first applications in the emerging field of digital healthcare epidemiology.
(Received 16 August 2018; accepted 28 September 2018) Background
Healthcare epidemiology has gained in importance in the United States and Europe due to growing financial pressure on hospitals, rising emergence of multidrug-resistant pathogens and greater complexity of healthcare delivery and systems,1,2 and it is likely to evolve further in the era of big data.3 In healthcare, the continuous adoption and integration of
electronic medical records, linkage of data sources, and the advent of new diagnostic and digital monitoring technologies have led to an unprecedented quantity and diversity of routine, electronic data.4 Big data in healthcare may be used to better exploit the potential for infection prevention and control, quality improve- ment, and optimal allocation of hospital resources.3,5 For healthcare epidemiologists to make use of big data, com-
putational systems and methods that can handle large datasets are required. Parallel with the rising amount of routine healthcare data and improvements in processing speed (computing power doubles every 2 years for the same cost),6 machine learning is increasingly being used for healthcare projects and is likely to become a key analytical tool in healthcare epidemiology.3,7 Thus, digital healthcare epidemiology, which focuses on
healthcare populations, may become an important field of epi- demiology, analogous to the rapidly growing field of digital epi- demiology that uses primarily social media data and other routine data sources within general populations.8–10 Similar to the general field of epidemiology, the primary goals of these interrelated
Author for correspondence: Andreas F. Widmer, MD, MS, Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Petersgraben 4, 4031
Basel, Switzerland. E-mail:
andreas.widmer@usb.ch aAuthors of equal contribution.
Cite this article: Roth JA, et al. (2018). Introduction to Machine Learning in Digital
Healthcare Epidemiology. Infection Control & Hospital Epidemiology 2018, 39, 1457–1462. doi: 10.1017/ice.2018.265
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved.
fields, digital epidemiology and digital healthcare epidemiology, are to understand the distribution and determinants of health- related states in specific populations and to use this knowledge to improve health and prevent disease. For simplicity, we char- acterize the spectrum between conventional healthcare epide- miology and digital healthcare epidemiology across 3 axes: (1) the analytical method, (2) the data source, and (3) the data type (Fig. 1). To exploit the full potential of big routine data in healthcare
and to efficiently communicate and collaborate with IT specialists and data analysts, healthcare epidemiologists require some knowledge of large-scale analysis techniques, particularly about machine learning. This review provides an overview on the broad area of machine learning and its recent applications in the emerging field of digital healthcare epidemiology for prediction, detection of trends and patterns (eg, for surveillance purposes), and the identification of risk factors. The main challenges and opportunities of studies relying on routine healthcare data and big data have been reviewed previously.11–15
Machine Learning: Introduction
Machine learning as a discipline originated in computer science with very close ties to statistics, but it is difficult to draw a straight line between the two. Machine learning is a young field compared to statistics that arose from the field of mathematics, having developed long before computers became available.16 Machine learning and statistics share a common aim to learn from data. Logistic regression for example, which is a standard technique in statistics,17 is called a machine-learning algorithm within the machine-learning community.18 The same holds true for more recent algorithms, such as random forests, which are well known machine-learning algorithms, developed by the statistician Leo
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