Machine learning for healthcare technologies - an introduction

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Abstract

Much has been written concerning the manner in which healthcare is changing, with a particular emphasis on how very large quantities of data are now being routinely collected during the routine care of patients. The use of machine learning methods to turn these ever-growing quantities of data into interventions that can improve patient outcomes seems as if it should be an obvious path to take. However, the field of machine learning in healthcare is still in its infancy. This book, kindly supported by the Institution of Engineering and Technology, aims to provide a “snapshot” of the state of current research at the interface between machine learning and healthcare. Necessarily, this is a partial and biased sampling of the state of current research, and yet we have aimed to provide a wide-ranging introduction to the depth and scale of work that is being undertaken worldwide. In selecting material for this edited volume, we have placed special emphasis on machine learning projects that are (or are close to) achieving improvement in patient outcomes. For many reasons, uncovered variously in some of the chapters that follow, it is a truism that “healthcare is hard”; there are unique constraints that exist, and considerations that must be taken, when working with healthcare data. However, for all its difficulties, working with healthcare data is exceptionally rewarding, both in terms of the computational challenges that exist and in terms of the outputs of research being able to affect the way in which healthcare is delivered. There are few application areas of machine learning that have such promise to benefit society as does that of healthcare.