Doctoral Thesis: Rigorously Tested & Reliable Machine Learning for Health
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: Thesis Defense
- Location: 32-G575
Additional Location Details:
Thesis Committee: Professors David Sontag (supervisor), Jonas Peters, Tommi Jaakkola
(zoom link available upon request)