Data Science and Rheumatic Disease: application and potential of machine learning using big data

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

The expansion of big data within rheumatology and recent progress in computational approaches have enhanced opportunities to study complex conditions to improve patient care. Current and emerging big data includes clinical data from linked electronic health records, imaging, multi-omics and patient-generated data. In parallel, advances in data science especially in artificial intelligence (AI) and machine learning (ML) provide huge potential for early detection, disease classification and stratification of treatment in patients with rheumatological conditions towards the ambition of precision medicine. In this chapter, we review the methodological advances in data science, AI and ML as well as techniques that demonstrate future potential. Applications to rheumatological conditions are discussed to date, where such techniques have been performed successfully. Despite recent progress, in rheumatology, data science applications and AI to inform precision medicine and transform healthcare are still nascent but herald an exciting era for research opportunities to advance clinical care.

Key words
Data science, artificial intelligence, machine learning, NLP, deep learning, neural networks
Original languageEnglish
Title of host publicationOxford Textbook of Rheumatology
PublisherOxford University Press
Publication statusAccepted/In press - 22 Apr 2022

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