Finale Doshi-Velez: Antidepressant Recommendation Systems: An Ongoing Journey for Technical and Clinical Impact

Title: Antidepressant Recommendation Systems: An Ongoing Journey for Technical and Clinical Impact

Abstract: How to choose antidepressants is an important question for patients who need medications to help manage their depression.  In this talk, I will discuss how our ongoing effort toward clinical relevance -- that is, our search for ways to assist with antidepressant treatment recommendations -- has led to insights in core machine learning (we corrected a decade-old flaw in supervised generative modeling) as well as HCI (how psychiatrists react to incorrect recommendations).  I will share how we select directions with interesting technical questions and societal impact, as well as what kinds of directions are good candidates for ML at all.  (I hope to make some of this a more informal discussion.)

Work in collaboration with Roy Perlis, Tom McCoy, Michael Hughes, Eric Sudderth, Gabe Hope, Leah Weiner, Melanie Pradier, Maia Jacobs, Krzyzstof Gajos. 

Finale Doshi-Velez is a John L. Loeb associate professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences.  She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School.  Her interests lie at the intersection of machine learning, healthcare, and interpretablity.