The Covid-19 Open AI Consortium (COAI) intends to bring breakthrough medical discoveries and actionable findings to the fight against the Covid-19 pandemic.
COAI aims to increase collaborative research, to accelerate clinical development of effective treatments for Covid-19, and to share all of its findings with the global medical and scientific community. COAI will unite collaborators: academic institutions, researchers, data scientists and industrial partners, to fight the Covid-19 pandemic.
This will be the first of three interviews with principal leaders behind COAI.
Sanjay Budhdeo is a practicing physician. He holds Medical Sciences and Medical degrees from Oxford University and a Masters Degree from Cambridge University, as well as Membership of the Royal College of Physicians. Sanjay has research experience in neuroimaging, epidemiology and digital health. Prior to joining Owkin as a Partnership Manager, he was a Senior Associate at Boston Consulting Group, where he focused on data and digital in healthcare. He sits on the Patient Safety Committee at the Royal Society of Medicine and was previously a Specialist Advisor at the Care Quality Commission.
What was it that inspired you to join OWKIN?
When practicing as a doctor, I saw many patients who had conditions we couldn’t treat with medications, where there was only so much we could do. As a researcher, I was frustrated by the traditional approaches to analysis, at a time when there was access to ever more data. Trying to make the connection between fields that had evolved separately — such as epidemiology and imaging – proved really challenging. Machine learning was for me a way to connect the dots from my work as a researcher and a physician, being able to derive individual-level insights that could impact the diagnosis and treatment for the entire patient population.
You have research experience in both epidemiology and digital health. Could you share with us some of the previous projects that you have worked on?
In epidemiology, I worked on the UK’s 1946 birth cohort — a fascinating long-term study that has tracked subjects born in a single week over the course of their life. In one project, I looked at when these subjects started learning to sit, stand and walk, and saw that this was associated with their ability to perform more complicated tasks later in life. I also looked at whether into the reasons behind this association — were there differences in genetics or in brain structure? In digital health, my focus has been on interoperability — the connections between electronic medical records in hospitals that enable sharing of data about patients between hospitals. This is really important for direct clinical care, so a doctor has a complete idea of what’s happened to you before, but it’s also really important to enable to use of machine learning models in the clinical setting.
OWKIN is spearheading an AI-driven research collaboration called the COVID-19 Open AI Consortium (COAI). Could you describe what this project is?
COAI is Owkin’s response to the concerns we’ve heard from our partner clinical and academic institutions. It’s clear to us that there are important clinical questions that need to be answered for Covid-19 — for example, how can we identify patients at risk of severe disease? What are the potential treatments that could be trialled against COVID-19 infections? Our aim is to increase collaborative research and share all findings with the global medical and scientific community. COAI draws on the strengths of collaborators across the health and tech space — including universities, hospitals, startups and biopharma companies. We are creating specific research areas, and the first area we’ve announced is in cardiovascular complications in Covid-19 patients, with additional research areas going live soon.
One of the initial projects will be understanding cardiovascular complications. What type of insights are we hoping to gain from the COAI?
Our aim is to produce clinically useful information about the risk of acute cardiovascular complications from Covid-19 infections. We’re exploring this question from multiple angles, using different types of data across different countries. It’s great to work with internationally leading clinical researchers to get to the heart of these questions.
Prediction and characterization of immune responses is another aspect of COAI. What are some of the data points that you believe should be analyzed to fully understand why some humans are capable of building an immune response, while others require medical assistance?
Our body’s system of defence is amazingly complex and intricate. There are many types of cells involved in our immune response. Some of the cells directly combat foreign invaders. Other cells will produce pro-inflammatory chemicals called cytokines, which act as homing signals to target the immune response, and tagging specific cells for destruction. What we’re learning is that the balance of particular cytokines – including IFN1, IFN gamma and IL-10 – is very important in mediating this immune response. Machine learning can be very helpful to examine a very rich dataset containing the levels of many cytokines and other blood markers, and generate insights into what the key players are here, while taking into account the complex interplay between different factors.
Understanding how to treat patients in order to achieve the best patient outcome, is possibly one of the most important projects being undertaken by COAI. In your opinion, what are the first steps that need to be undertaken to understand this?
An important first step is risk stratification. We want to understand which patients are at the highest risk of having severe disease — including lung complications like acute respiratory distress syndrome, heart complications such as myocarditis, and other organ or system-specific sequelae. This risk stratification question is important for several reasons. First, as a doctor you might want to monitor a patient differently if you know they’re at higher risk of compilations. Second, as a hospital, you want to be able to predict the demand for intensive care facilities and plan according to that demand. Third, if you’re a researcher or biopharma company, you can include that subgroup of patients in trials, a treat them early to get an optimal response to your medication. In all of those cases, our ultimate aim is to improve patient outcomes
Can you explain why data science is so important for fighting COVID-19?
Data science, in its broadest sense, is at the heart of the fight against COVID-19. Important questions about the modelling of COVID-19 infection rates remain. We can use real-world patient data to identify drugs which could be usefully repurposed to treat COVID-19 patients. There is an incredible amount of information we are discovering about the virus which will help us to better design a vaccine. There is so much that we don’t know about the virus including how it affects people and we are learning more and more thanks to many varieties of data – biochemical, genetic, clinical, and from cellphones.
What do you believe are some of the insights that we can learn from AI analyzing this data?
For me, the sweetspot of AI is really in helping to derive conclusions at the level of the individual from population-level data. We can think about which patients might benefit from which therapies to combat COVID-19 infection, or help to predict which areas might become local hotspots for COVID-19 infection. There’s also a lot of activity in the discovery space, both in terms of potential medications, and for vaccine candidates. AI can really help us deliver novel biological insights much more quickly.
Who should be joining the COVID-19 Open AI Consortium project?
We’re speaking to a number of players within and outside of the healthcare space. This includes hospitals, universities and pharmaceutical companies, but also other start-ups, NGOs and policy organisations. We’re particularly excited to hear from clinicians who have gathered data and would like help with analysis.
Is there anything else that you would like to share about either the COAI project or COVID-19?
I’m really excited to share this initiative with you! If you want to collaborate, we’d be happy to discuss — get in touch at [email protected]
Thank you for the fantastic interview. Readers who wish to learn more, may read our article describing the COAI project.
The second interview in this series was with Dr. Stephen Weng, Principal Investigator.
The third interview in this series was with Folkert W. Asselbergs, Principal Investigator
You may also visit the Covid-19 Open AI Consortium website.
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