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Analytics and AI for Healthcare

About the Series

Artificial Intelligence (AI) and analytics are increasingly being applied to various healthcare settings. AI and analytics are salient to facilitate better understanding and identifying key insights from healthcare data in many areas of practice and enquiry in healthcare including at the genomic, individual, hospital, community and/or population levels.

The Chapman&Hall/CRC Press Analytics and AI in Healthcare Series aims to help professionals upskill and leverage the techniques, tools, technologies and tactics of analytics and AI to achieve better healthcare delivery, access and outcomes.

The series covers all areas of analytics and AI as applied to healthcare. It will look at critical areas including: prevention, prediction, diagnosis, treatment, monitoring, rehabilitation and survivorship.

If you are interested in contributing your work, please send us your book proposals:

Series Editor
Nilmini Wickramasinghe
[email protected]

Chapman&Hall/CRC Editor
George Knott
[email protected]

2 Series Titles

Per Page

Data Analysis in Medicine and Health using R

Data Analysis in Medicine and Health using R

1st Edition


By Kamarul Imran Musa, Wan Nor Arifin Wan Mansor, Tengku Muhammad Hanis Tengk
September 18, 2023

In medicine and health, data are analysed to guide treatment plan, patients care as well as the control and prevention policies. However, in doing so, researchers in medicine and health often lack the understanding of data and statistical concepts and the skills in programming. In addition, there ...

Explainable AI in Healthcare Unboxing Machine Learning for Biomedicine

Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine

1st Edition


Edited By Mehul S Raval, Mohendra Roy, Tolga Kaya, Rupal Kapdi
July 17, 2023

This title offers a reference for readers hoping to combine computer vision and machine learning. Contributors offer diverse perspectives drawn from an international pool. The book shows the development of algorithms and architectures for healthcare. Explainable AI opens ML up to show the reasons ...

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