How artificial intelligence and machine learning can help healthcare systems respond to COVID-19
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How artificial intelligence and machine learning can help healthcare systems respond to COVID‑19 Mihaela van der Schaar1,2 · Ahmed M. Alaa2 · Andres Floto1 · Alexander Gimson3 · Stefan Scholtes1 · Angela Wood1 · Eoin McKinney1 · Daniel Jarrett1 · Pietro Lio1 · Ari Ercole1,3 Received: 19 July 2020 / Revised: 18 October 2020 / Accepted: 21 October 2020 © The Author(s) 2020
Abstract The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide personalized patient management plans, inform policy, and expedite clinical trials. In this paper, we introduce five of the most important challenges in responding to COVID19 and show how each of them can be addressed by recent developments in machine learning (ML) and artificial intelligence (AI). We argue that the integration of these techniques into local, national, and international healthcare systems will save lives, and propose specific methods by which implementation can happen swiftly and efficiently. We offer to extend these resources and knowledge to assist policymakers seeking to implement these techniques. Keywords Clinical decision support · Healthcare · COVID-19
1 Introduction Both the UK and the international community have seen an unbelievable amount of pressure put on their social and healthcare infrastructure over the past months. AI and machine learning can use data to make objective and informed recommendations, and can help
Editor: Hendrik Blockeel. * Mihaela van der Schaar [email protected] 1
University of Cambridge, Cambridge, UK
2
University of California, Los Angeles, USA
3
Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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Machine Learning
ensure that scarce resources are allocated as efficiently as possible. Doing so will save lives and can help reduce the burden on healthcare systems and professionals. This paper goes into detail about specific practical challenges faced by healthcare systems, and how AI and machine learning can improve decision-making to ensure the best outcomes possible. While the paper is primarily focused on the UK national healthcare system, the challenges and methods highlighted in the paper apply to other countries. First, AI and machine learning can help us identify people who are at highest risk of being infected by the novel coronavirus. This can be done by integrating electronic health record data with a multitude of “big data” pertaining to human-to-human interactions (from cellular operators, traffic, airlines, social media, etc.). This will make allocation of resources like testing kits mor
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