Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology

Abstract

The increasing availability of electronic health data presents a major opportunity in healthcare for both discovery and practical applications to improve healthcare. However, for healthcare epidemiologists to best use these data, computational techniques that can handle large complex datasets are required. Machine learning (ML), the study of tools and methods for identifying patterns in data, can help. The appropriate application of ML to these data promises to transform patient risk stratification broadly in the field of medicine and especially in infectious diseases. This, in turn, could lead to targeted interventions that reduce the spread of healthcare-associated pathogens. In this review, we begin with an introduction to the basics of ML. We then move on to discuss how ML can transform healthcare epidemiology, providing examples of successful applications. Finally, we present special considerations for those healthcare epidemiologists who want to use and apply ML.

Keywords: computation; data-driven; healthcare epidemiologist; machine learning; patient risk stratification.

Figure 1.

Traditional vs. machine learning (ML) approach. In a traditional approach to data analysis, one starts with the model as input to the machine. In an ML (or data-driven) approach, one starts with the data and outputs a model that can then be applied to new data.