SEMINAR: Statistical Models vs Machine Learning in Health Research [May 2nd; live streaming available]

The Dalla Lana School of Public Health at the University of Toronto will be hosting a public lecture by Prof. Frank E. Harrell Jr. on Thursday May 2, 2019 from 11am to 12 noon, 155 College St. Room HS574. The title of the talk is “Musings on Statistical Models vs Machine Learning in Health Research”.

Registration for the event is required. The talk will also be available to access remotely by live streaming. If you register to participate for the event remotely an access link will be forwarded to you closer to the event.

The link to the registration site is


Health researchers and practicing clinicians are with increasing frequency hearing about machine learning (ML) and artificial intelligence applications. They, along with many statisticians, are unsure of when to use traditional statistical models (SM) as opposed to ML to solve analytical problems related to diagnosis, prognosis, treatment selection, and health outcomes. And many advocates of ML do not know enough about SM to be able to appropriately compare performance of SM and ML. ML experts are particularly prone to not grasp the impact of the choice of measures of predictive performance. In this talk I attempt to define what makes ML distinct from SM, and to define the characteristics of applications for which ML is likely to offer advantages over SM, and vice-versa. The talk will also touch on the vast difference between prediction and classification and how this leads to many misunderstandings in the ML world. Other topics to be covered include the minimum sample size needed for ML, and problems ML algorithms have with absolute predictive accuracy (calibration). Presented by Professor Frank E. Harrell, Department of Biostatistics, Vanderbilt University.

About the Instructor:
  • Professor and founding Chair of the Department of Biostatistics, Vanderbilt University School of Medicine, Nashville TN USA
  • PhD in Biostatistics from U. North Carolina
  • Extensive work in biomedical and pharmaceutical research
  • ASA Fellow and winner of the ASA WJ Dixon Award for Excellence in Statistical Consulting in 2014
  • Publications
  • Active on – see my posts here
  • Written several R packages including Hmisc and rms
  • Used R intensively since 1999 and am a member of the R Foundation
  • Author of Regression Modeling Strategies, 2nd Edition
  • Statistical knowledge outside the areas of regression modeling strategies and Bayes is in BBR
  • Expert Statistical Advisor to the Office of Biostatistics, Center for Drug Evaluation and Research, FDA