Guest conversation with Prof. Ron Do from New York: Publishing machine learning models of electronic healthcare records in The Lancet

Join us on the 30th May 30 at 4 pm, for a Journal Club discussion, with the exception that the team behind the work can also join the discussion.  We will talk how machine learning helps diagnose and predict various diseases.

Ron Do is Professor in The Charles Bronfman Institute for Personalized Medicine at the Icahn School of Medicine at Mount Sinai, New York. His lab applies computational genomic, machine learning and population genetics methods to large clinical datasets. A focus of his lab has been on deriving disease prediction models using multi-modal data. In the past several years, Ron has published several research papers as senior author, including in The Lancet, JAMA, JACC and Eur H J. We are privileged that he and his colleagues Ben Omega Petrazzini and Daniel Jordan will be available from New York (via Zoom) to answer our questions.

Attendees are asked to read three papers before attendance that Ron’s team have published in the past two years. You will have the oppurtunity to can ask any question about any paper, or the process of getting there, in a relaxed and informal atmosphere. 

Thursday 30th May, 16:00-17:00 (Estonian Time, and Eastern European Time)
You can join hybrid meeting, either:
* In person at room 2049, Delta Centre, Narva mnt 18, University of Tartu, or
* Online on https://ut-ee.zoom.us/j/91020203604?pwd=YnptVXc2N0NLQnA3NTQ5dndXM1JIUT09 

16:00 predicting 1-year risk of heart disease:
Petrazzini BO, […] Do R. Coronary risk estimation based on clinical data in electronic health records. JACC. 2022. https://doi.org/10.1016/j.jacc.2022.01.021

16:20 using machine learning to diagnose heart disease:
Forrest IS, Petrazzini BO, […] Jordan DM, […] Do R. Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts. The Lancet. 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10069625/

16:40 discussion, on diagnosing rare disease:
Jordan DM, Vy HM, Do R. A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems. medRxiv. 2023. https://doi.org/10.1101/2023.12.21.23300393

17:00 Close