*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Jeffrey Valdez (valdez@cc.gatech.edu)
Jimeng Sun (jsun@cc.gatech.edu)
Summary Sentence: CHAI Seminar Series: David Page, PhD, University of Wisconsin Madison
Full Summary: No summary paragraph submitted.
Dr. David Page, PhDSpeaker: David Page, PhD, Vilas Distinguished Achievement Professor in the Department of Biostatistics and Medical Informatics at the School of Medicine and Public Health at the University of Wisconsin-Madison. Joint Appointment in the Department of Computer Sciences.
Date: Tuesday, February 27, 2018
Time: 03:00pm – 04:15pm
Location: Klaus Advanced Computing Building, Room 2443
Abstract: Much of the world’s real data on people is irregularly-sampled, temporal, and observational (meaning we don’t get to experiment as in a randomized clinical trial). For example, customers make purchases on various dates of their choice, not necessarily once a week or once a month, and we only observe rather than intervene in their decisions. Patients visit the doctor whenever they feel the need, and we observe their doctors’ entries in the electronic health record (EHR), without the ability to randomize patient treatments. We show that despite this lack of control or sampling regularity, we can predict future events from such data with surprising accuracy, for example better than 80% on average across a variety of diagnosis codes in the EHR a month in advance. We further show that despite many types of potential confounding, we can actually discover causal factors (e.g., effect of a drug on a disease or on a measurement such as blood pressure) at similar levels of accuracy for real problems. The key to doing so is modeling person-specific, time-varying baseline levels, e.g. of a measurement such as blood pressure or a risk such as for heart attack. On the applied side this talk will focus entirely on medical applications, but the approaches developed and employed are general-purpose machine learning algorithms with broad potential applicability.
Bio: David Page is a Kellett and Vilas Distinguished Achievement Professor at the University of Wisconsin-Madison. His tenure home is in the School of Medicine and Public Health, Dept. of Biostatistics and Medical Informatics, and he also has an appointment in the Department of Computer Sciences where he supervises PhD students and teaches machine learning. David has a PhD in Computer Science from the University of Illinois at Urbana-Champaign (1993), where his thesis proved properties of algorithms that learn from relational databases and/or first-order logic. He became involved in biomedical applications of machine learning while a post-doc and visiting faculty member at Oxford University, collaborating with various pharmaceutical companies and what was then the Imperial Cancer Research Fund. David is the faculty lead of the Cancer Informatics Core of UW-Madison’s Carbone Cancer Center, is a member of the Genome Center of Wisconsin, and served on scientific advisory boards for the Observational Medical Outcomes Partnership and the Wisconsin Genomics Initiative. He has served as a standing member of NIH study sections BDMA (Biodata Management and Analysis, when it first became a standing study section) and BLR (Biomedical Data, Library and Data Science Review Committee, currently serving).
