Machine Learning for Clinical Predictive Analytics
It offers an introductory guide for practitioners in healthcare or data science, but it is incremental as it reviews existing methods without new results.
This chapter provides an overview of applying machine learning techniques to clinical prediction tasks, demonstrating how to use common algorithms like regression, decision trees, and support vector machines on publicly available datasets through case studies.
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. The objectives of this chapter are to (1) understand the basics of machine learning techniques and the reasons behind why they are useful for solving clinical prediction problems, (2) understand the intuition behind some machine learning models, including regression, decision trees, and support vector machines, and (3) understand how to apply these models to clinical prediction problems using publicly available datasets via case studies.