Responding to COVID-19 with AI and machine learning

Paper by Mihaela van der Schaar et al: “…AI and machine learning can use data to make objective and informed recommendations, and can help 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….

1. Managing limited resources

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 more efficient, as well as informing how we, as a society, respond to this crisis over time….

2. Developing a personalized treatment course for each patient 

As mentioned above, COVID-19 symptoms and disease evolution vary widely from patient to patient in terms of severity and characteristics. A one-size-fits-all approach for treatment doesn’t work. We also are a long way off from mass-producing a vaccine. 

Machine learning techniques can help determine the most efficient course of treatment for each individual patient on the basis of observational data about previous patients, including their characteristics and treatments administered. We can use machine learning to answer key “what-if” questions about each patient, such as “What if we postpone a couple hours before putting them on a ventilator?” or “Would the outcome for this patient be better if we switched them from supportive care to an experimental treatment earlier?”

3. Informing policies and improving collaboration

…It’s hard to get a clear sense of which decisions result in the best outcomes. In such a stressful situation, it’s also hard for decision-makers to be aware of the outcomes of decisions being made by their counterparts elsewhere. 

Once again, data-driven AI and machine learning can provide objective and usable insights that far exceed the capabilities of existing methods. We can gain valuable insight into what the differences between policies are, why policies are different, which policies work better, and how to design and adopt improved policies….

4. Managing uncertainty

….We can use 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 can also use 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.

5. Expediting clinical trials

Randomized clinical trials (RCTs) are generally used to judge the relative effectiveness of a new treatment. However, these trials can be slow and costly, and may fail to uncover specific subgroups for which a treatment may be most effective. A specific problem posed by COVID-19 is that subjects selected for RCTs tend not to be elderly, or to have other conditions; as we know, COVID-19 has a particularly severe impact on both those patient groups….

The AI and machine learning techniques I’ve mentioned above do not require further peer review or further testing. Many have already been implemented on a smaller scale in real-world settings. They are essentially ready to go, with only slight adaptations required….(More) (Full Paper)”.