Panel
Technical Panels and Workshops
:
Best practices for validating machine learning in medicine
Panel Moderator
Event Type
Panel
Technical Panels and Workshops
Tracks
Technical
TimeFriday, September 182:30pm - 3:15pm
LocationAyanna Howard Room
DescriptionMany students build classifiers and perform regressions in data-driven courses including machine learning, data science, and applied statistics. However, even for more advanced students there are particular mistakes made when applying those predictive modeling skills in health care settings in which data can be scarce and uncertain with significant consequences for errors. In this panel, we explore those issues with a range of perspectives, seeking practical advice for computer scientists along with illustrative cautionary tales. Despite all that artificial intelligence has accomplished there is a considerable degree of skepticism among clinicians about the real-world applicability of AI in medical contexts. We address a variety of techniques that can remedy this by using proper validation strategies - some clinically oriented during data collection and a few computational approaches. This is particularly important as many complex models may be less interpretable in how they function (for example, ensemble methods or deep learning), but can be useful to sift through large data sets as recommendation systems for clinical decision making. With healthcare providers exposed to more properly trained and validated models machine learning strategies will be easier to adopt and guide clinical decisions in practice.
Session Organizers
Panel Moderator
Assistant Professor of Computer Science