Machine Learning for Clinical Predictive Analytics

Abstract

In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine learning, and outline some of the most common machine learning algorithms. Next, we demonstrate how to apply the algorithms with appropriate toolkits to conduct machine learning experiments for clinical prediction tasks. This chapter is composed of five sections. First, we will explain why machine learning techniques are helpful for researchers in solving clinical prediction problems (Sect. 12.1). Understanding the motivations behind machine learning approaches in healthcare are essential, since precision and accuracy are often critical in healthcare problems, and everything from diagnostic decisions to predictive clinical analytics could dramatically benefit from data-based processes with improved efficiency and reliability. In the second section, we will introduce several important concepts in machine learning in a colloquial manner, such as learning scenarios, objective/target function, error and loss function and metrics, optimization and model validation, and finally a summary of model selection methods (Sect. 12.2). These topics will help us utilize machine learning algorithms in an appropriate way. Following that, we will introduce some popular machine learning algorithms for prediction problems (Sect. 12.3), for example, logistic regression, decision tree and support vector machine. Then, we will discuss some limitations and pitfalls of using the machine learning approach (Sect. 12.4). Lastly, we will provide case studies using real intensive care unit (ICU) data from a publicly available dataset, PhysioNet Challenge 2012, as well as the breast tumor data from Breast Cancer Wisconsin (Diagnostic) Database, and summarize what we have presented in this chapter (Sect. 12.5).