• Decision-support systems or clinical prediction tools based on machine learning (including
the special case of deep learning) are similar to clinical support tools developed using …

Abstract Machine learning is a tool for analysing digitised data sets and formulating
predictions that can optimise clinical decision‐making. It aims to identify complex patterns in …

Abstract Machine learning methods, a subdomain of artificial intelligence, in healthcare have
been experiencing rapid growth and development but these methods have also been …

• Evaluation of machine-learned systems is a multifaceted process that encompasses
internal validation, clinical validation, clinical outcomes evaluation, implementation research …

The deployment of machine learning for tasks relevant to complementing standard of care
and advancing tools for precision health has gained much attention in the clinical …

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all
topics that fall under the heading of artificial intelligence (AI) and have gained popularity in …

Examples of fully integrated machine learning models that drive clinical care are rare.
Despite major advances in the development of methodologies that outperform clinical …

• Multidisciplinary partnership between technical experts and end-users, including clinicians,
administrators, and patients and their families, is essential to developing and implementing …

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 …

Machine learning promises to underpin personalised medicine. However, the expertise
required to develop and deploy state-of-the-art machine learning algo rithms has contributed …