Data Science in Healthcare- Current Challenges and Opportunities

Pankaj Khurana; Rajeev Varshney

doi:10.20944/preprints202103.0425.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 16 March 2021 / Approved: 16 March 2021 / Online: 16 March 2021 (13:03:15 CET)

The rise in the volume, variety and complexity of data in healthcare has made it as a fertile-bed for Artificial intelligence (AI) and Machine Learning (ML). Several types of AI are already being employed by healthcare providers and life sciences companies. The review summarises a classical machine learning cycle, different machine learning algorithms; different data analytical approaches and successful implementation in haematology. Although there are many instances where AI has been found to be great tool that can augment the clinician’s ability to provide better health outcomes, implementation factors need to be put in place to ascertain large-scale acceptance and popularity.

Artificial Intelligence; Machine Learning; Healthcare; Haematology

Biology and Life Sciences, Anatomy and Physiology

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Data Science in Healthcare- Current Challenges and Opportunities

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