Medical Decision Support Using Increasingly Large Multimodal Data Sets

Abstract

Medical decision support has traditionally been using model-based approaches and small data sets for evaluation. This chapter analyses recent trends and techniques that make use of increasingly large data sets and thus more data-driven approaches to medical decision support that have in some areas replaced the more traditional rule-based approaches. It explains the challenge infrastructures and approaches as they are often essential to access data, and application scenarios give examples of existing applications and objectives. The chapter focuses on how to overcome current limitations and how to tackle the upcoming challenges of image-based decision support for digital medicine. It reviews the literature of mainly the past five years in the field of medical visual decision support and highlights the use of multimodal data and data-driven approaches. Machine learning is an indispensable part of medical decision support, especially in diagnostic and categorization applications. © 2019 John Wiley & Sons Ltd.