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 COVID-19 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.
Highlights
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
This paper goes into detail about specific practical challenges faced by healthcare systems, and how artificial intelligence (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
Summary
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 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.). Unproven hypotheses about the disease are likely to propagate online, impacting individual behaviour and causing systemic risks This encourages more progress in an area of machine learning called transfer learning to account for differences between populations, substantially eliminating bias while still extracting usable data that can be applied from one population to another. We need methods to make us aware of the degree of uncertainty of any given conclusion or recommendation generated from machine learning This means that decision-makers can be provided with confidence estimates that tell them how confident they can be about a recommended course of action.
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