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How artificial intelligence and machine learning can help healthcare systems respond to COVID-19
| Content Provider | Semantic Scholar |
|---|---|
| Author | Schaar, Mihaela Van Der Plummer, John Humphrey |
| Copyright Year | 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 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. Challenge 1: Managing limited healthcare resources Healthcare systems in the UK and elsewhere in the world will soon face a severe scarcity of resources—in particular testing kits, hospital beds, ICU beds, ventilators and personnel. The UK, for example, currently has capacity for only a limited amount of tests to be conducted per day [1] and many of the intensive care unit (ICU) beds are already occupied [2]. This scarcity of resources is aggravated by the fact that individuals with COVID19 are known to experience widely different patterns of disease progression and outcomes: while some patients are asymptomatic, others manifest flu-like symptoms of varying severity, and some experience complications such as pneumonia and fatal multi-organ failure [3]. Since resources are limited and risk and disease progression so heterogeneous, it is crucial to identify—as early as possible—which individuals are most likely to have been infected by the virus, which infected individuals may experience adverse events, which types of medical resources those individuals will require, and when these resources will be required. The scarcity of healthcare resources will be exacerbated by the need to employ those resources to deal with both COVID-19 cases and with other patients who require—or will require—medical care. There is thus a clear and urgent need to deploy systems that can provide early warnings for personalized risk and disease progression of individuals, and that can inform medical personnel and healthcare systems about which patients would benefit from what resources and when [5]. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.vanderschaar-lab.com/NewWebsite/covid-19/post1/paper.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
