Owkin Launches the Collaborative COVID-19 Open AI Consortium (COAI)
After a fresh round of funding, Owkin recently launched the Covid-19 Open AI Consortium (COAI). This consortium will enable advanced collaborative research and accelerate clinical development of effective treatments for patients who are infected with COVID-19.
The first stage of the project is on fully understanding and treating cardiovascular complications in COVID-19 patients, this will be performed in collaboration with CAPACITY, an international registry working with over 50 centers around the world. Other areas of research will include patient outcomes and triage, and the prediction and characterization of immune response.
Owkin's manifesto perfectly states the company's vision:
“We are fully engaged in this new frontier with the goal of improving drug development and patient outcomes. Founded in 2016, Owkin has quickly emerged as a leader in bringing Artificial Intelligence (AI) and Machine Learning (ML) technologies to the healthcare industry. Our solutions improve the traditional medical research paradigm by turning a previously siloed, disjointed system into an innovative and collaborative one that, above all, puts the privacy of patients first.”
To understand the model that Owkin is engaging one must fully understand a new technology which is called Federated Learning. Federated learning offers a framework for AI development that enables enterprises to train machine learning models on data that is distributed at scale across multiple medical institutions without centralizing the data. The benefits of this are two-fold, there is no loss of privacy since the data is not directly linked to any specific patient, and the data is maintained at the healthcare institution that collects this data.
The use of Federated Learning thereby enables a significantly wider range of data than what any single organization possesses in-house. What this means is that by used Federated Learning researchers have access to as much data as available, and the more big data a machine learning system possesses, the more accurate the AI becomes.
There are currently multiple national efforts in using AI to tackle COVID-19. The problem with many of these nationalistic disjointed efforts is that the data is specific to one country. Collecting data from a single region may fail to reveal important information that would enable researchers to fully understand how exposure to environmental elements, ethnic makeup, genetics, age, and gender may play important roles in understanding this disease. This is why collaboration is so important, and why gathering data from multiple jurisdictions is even more important.
As described by Owkin, they seek to used Federated Learning for the following:
“We aim to help them understand why drug efficacy varies from patient to patient, enhance the drug development process and identify the best drug for the right patient at the right time, to improve treatment outcomes.”
Understanding and treading cardiovascular health issues will be the first challenge undertaken by Owkin. As important as data is, what is even more important are the efforts of researchers and contributors who are spearheading this effort. This is why Unite.AI will be releasing three interviews with researchers that are contributing to the COAI project.
Sanjay Budhdeo, MD, Business Development:
Sanjay is a practicing physician. He holds Medical Sciences and Medical degrees from Oxford University and a Masters Degree from Cambridge University. 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.
Click Here to read the interview with Sanjay.
Dr. Stephen Weng, Principal Researcher:
Stephen is an Assistant Professor of Integrated Epidemiology and Data Science who leads the data science research within the Primary Care Stratified Medicine Research Group.
He integrate traditional epidemiological methods and study design with new informatics-based approaches, harnessing and interrogating “big health care data” from electronic medical records for the purpose of risk prediction modeling, phenotyping chronic diseases, data science methods research, and translation of stratified medicine into primary care.
Click Here to read the interview with Stephen
Folkert W. Asselbergs, Principal Investigator
Folkert is professor of precision medicine in cardiovascular disease at Institute of Cardiovascular Science, UCL, Director NIHR BRC Clinical Research Informatics Unit at UCLH, professor of cardiovascular genetics and consultant cardiologist at the department of Cardiology, University Medical Center Utrecht, and chief scientific officer of the Durrer Center for Cardiovascular Research, Netherlands Heart Institute. Prof Asselbergs published more than 275 scientific papers and obtained funding from leDucq foundation, British and Dutch Heart Foundation, EU (FP7, ERA-CVD, IMI, BBMRI), and RO1 National Institutes of Health.
Click Here to read the interview with Folkert
The hope of Unite.AI is that by using biomedical images, genomics, and clinical data to discover biomarkers and mechanisms associated with diseases and treatment outcomes this will propel the next generation of treatment to tackle COVID-19. We are contributing to this important project by highlighting the personalities behind this important global effort.
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