Doctoral Thesis: Rigorously Tested & Reliable Machine Learning for Health

Thursday, May 25
10:00 am - 11:30 am


Michael Oberst

How do we make machine learning as rigorously tested and reliable as any medication or diagnostic test? Machine learning (ML) has the potential to improve decision-making in healthcare, from predicting treatment effectiveness to diagnosing disease. However, standard retrospective evaluations can give a misleading sense for how well models will perform in practice. Evaluation of ML-derived treatment policies can be biased when using observational data, and predictive models that perform well in one hospital may perform poorly in another.
In this thesis, I introduce new tools to proactively assess and improve the reliability of machine learning in healthcare. A central theme will be the application of external knowledge, including review of patient records, incorporation of limited clinical trial data, and interpretable stress tests. Throughout, I will discuss how evaluation can directly inform model design.


  • Date: Thursday, May 25
  • Time: 10:00 am - 11:30 am
  • Category:
  • Location: 32-G575
Additional Location Details:

Thesis Committee: Professors David Sontag (supervisor), Jonas Peters, Tommi Jaakkola

(zoom link available upon request)