The Symposium on Risks and Opportunities of AI in Clinical Drug Development is an event jointly sponsored by Pfizer Inc., Northeastern University, the American Statistical Association (ASA), the Statistics Department and Data Science Institute at Columbia University, and OHDSI. This event is designed to serve as a platform for distinguished statisticians, data scientists, regulators, and other professionals to address the challenges and opportunities of AI in pharmaceutical medicine; to foster collaboration among industry, academia, regulatory agencies, and professional associations; and to propose recommendations with policy implications for proper implementation of AI in promoting public health. As a convener of researchers in the fields of AI, data science, biotechnology, computational medicine, and more, industry partners, academic faculty, and entrepreneurs, the Roux Institute at Northeastern University is uniquely positioned to host this event and connect the experts needed to tackle the challenges and opportunities presented. The Roux Institute at Northeastern University is a center of activity for both the Observational Health Data Sciences and Informatics Center (OHDSI), which advances healthcare by fostering reproducible research through open science, and the Institute for Experiential Artificial Intelligence (IEAI), which researches and develops human-centric AI solutions that leverage machine technology to extend human intelligence. Co-sponsored by Pfizer, the American Statistical Association, Northeastern University, and Columbia University, the Roux Institute at Northeastern University is pleased to host this symposium focused on Risks and Opportunities in Clinical Drug Development.

A group of Northeastern researchers is tapping into the power of machine learning to develop new models for identifying patients who may have post-acute sequelae of SARS-CoV-2 infection, or so-called "long COVID." Using electronic health records from the National COVID Cohort Collaborative, a federal database that compiles medical information about COVID-19 patients, researchers were able to develop models that helped identify COVID long haulers across a range of features--from past COVID diagnosis, to the types of medications they've been prescribed, according to new research published in Lancet Digital Health. The data harmonization effort drew from a variety of information sources to construct a picture of what long COVID looks like in the U.S.--and who is most likely to have it. Those sources include demographic data, healthcare visit details, diagnoses and medications for 97,995 adults with COVID-19, the study says. Patients most likely suffering from the post-infection illness, which is estimated to plague between 10-30% of people who contract COVID-19, are often characterized as having new or lingering symptoms that are present 90 days after being diagnosed with the viral infection--a criteria researchers also used to determine their base population in their analysis.