Authors: Mihaela van der Schaar (University of Cambridge, UK)
Both the UK and the international community are still in the early stages of a crisis that will see an unbelievable amount of pressure put on social and healthcare infrastructure. Life-and-death choices will be made and often, artificial intelligence (AI) and machine learning can be used to make objective and informed recommendations.
Ventilators and ICU beds will be in short supply, and the time of clinical professionals will be stretched across too many patients to cover. The need for difficult evidence-based decisions to be made has never been more crucial, and the speed in which front-line carers will need to make these decisions is likely to have a negative effect.
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.
Our team recently published a perspective review, which explores the specific challenges faced by healthcare systems and how AI and machine learning can improve decision-making to ensure the best outcomes possible. Our research describes specialized machine-learning data. In this editorial, I summarize the key points of how AI should be employed by our governments to ensure the most effective responses and actions are taken.
Managing limited resources
AI and machine learning can help us identify people who are at highest risk of being infected by the novel SARS-CoV-2 coronavirus. This can be done by integrating electronic health record data with a multitude of “big data” sources pertaining to human-to-human interactions (from mobile cellular operators, road traffic, airlines, social media, etc.). This will make allocation of valuable resources, such as testing kits, more efficient, as well as informing how we, as a society, respond to this crisis over time.
AI and machine learning can also help us work out which infected patients are more likely to suffer severely from COVID-19. We can provide more accurate patient risk scores that will help clinical professionals decide who needs urgent treatment (and resources), and when.
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 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 putting this patient on a ventilator by a few hours?” Or “would the outcome for this patient be better if we switched them from supportive care to an experimental treatment sooner?”
Informing policies and improving collaboration
We have seen a huge variety of approaches taken by decision-makers when deciding on policies to respond to COVID-19. This is true from the individual level (i.e. practitioners) all the way up to the government level. For example, differences in triaging protocols used by medical institutions and practitioners could mean that two patients with similar profiles will end up receiving different types of treatment depending on where they happen to live.
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, hampering their ability to learn from one-another.
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.
This information can be shared between decision-makers at all levels, improving consistency and efficiency across the board. The result is that routine decisions can be made in a more coordinated and timely way, freeing up valuable medical attention to the cases that demand real-time expertise.
We still know very little about the COVID-19 pandemic, and the virus itself may continue to change over time. We may not be able to rely on the data from decisions and outcomes taken in other countries (China, Iran, South Korea, Italy, etc.), as those may generalize poorly to other countries like the UK or the US. In the meantime, unproven hypotheses about the disease are likely to propagate online, impacting individual behavior and causing systemic risks.
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.
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 in 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 of these patient groups.
Rather than recruiting and assigning subjects at random, machine learning methods can recruit subjects from identifiable subgroups, and assign them to treatment or control groups in a way that speeds up learning. These methods have been shown to significantly reduce error and achieve a prescribed level of confidence in findings, whilst also requiring fewer subjects. We can also use machine learning to target particular treatments to specific subgroups and to understand which treatments are suitable for the population as a whole.
These techniques are proven, and should be implemented without delay
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.
The data to support these techniques already exists in the UK and many other countries. There is a wealth of information we can get from electronic health records and emergency call databases, as well as “big data” for human-to-human interactions. We simply need to be able to integrate this information on a national, hospital and individual level.
My fellow authors and I call upon the governments of the UK and other nations to implement the above techniques as soon as possible. We also extend our support in the form of technologies, resources and knowledge to assist with their implementation. If we act now, we may be able to have these systems in place before our healthcare infrastructure is overwhelmed. Doing so will save lives.
You can read the full paper here. If you would like to get in contact with my team to discuss any of our research and how this can be applied to the COVID-19 pandemic, please feel free to do so.
About the author
Mihaela van der Schaar is the University of Cambridge’s John Humphrey Plummer Professor of AI and machine learning in Medicine. She is also a Turing Fellow, and Chancellor’s Professor at UCLA (CA, USA).
Her recent perspective paper on COVID-19 is co-authored with members of the Cambridge Centre for AI in Medicine, and calls on governments and healthcare authorities to use proven AI and machine learning techniques and existing data to coordinate a response to the disease.