Skip Navigation
Click Subscribe to join the NIH Calendar of Events e-mail list or Sign In to add or modify your events.  

Event Details

    E-mail this page E-mail this page

Event Type:  Lecture
Title:  High-Throughput Machine Learning from EHR Data
Description:  The widespread use of electronic health records and the many recent successes of machine learning raise at least two natural questions. How well can future health events of patients be predicted from EHR data, at various lengths of time in advance? And how can such predictions improve human health? This talk answers the first question via a new approach called "high-throughput machine learning," and it speculates about answers to the second question. In particular, this talk argues that many healthcare applications require not just accurate prediction, but accurate prediction by causally-faithful models. Causal discovery from observational data is already a major research direction in machine learning and statistics, and this talk discusses new approaches across the spectrum from when "we know all the relevant variables" to when "we know only one relevant variable" for the task at hand. If time permits, the talk will also touch on the issue of protecting patient privacy while empowering the construction of accurate predictive models.
Series Name:  National Library of Medicine Informatics Lecture Series
Videocast:  Event will be videocast LIVE on the Web
Videocast URL:
  Event will be available in the videocast ARCHIVE
Special Instructions:  To arrange sign language interpretation for an event go to the Office
of Research Services (ORS) Interpreting Service Requests web page.

Wednesday, March 08, 2017   2:00pm - 3:00pm Add To Outlook Calendar     Add To iCal Calendar     Add To Entourage Calendar
  Click on MS Outlook Calendar Icon (MS Outlook) or Mac iCal Calendar Icon (Mac iCal) or Entourage Calendar Icon (MS Entourage for MAC) to add an event into your Calendar. Need Help?

Name:   David Page, PhD
Title:   Professor Department of Biostatistics & Medical Informatics
Organization:   University of Wisconsin-Madison
City/Province:   Madison
State:   Wisconsin
Country:   USA

Organization(s):  [NIH] National Library of Medicine (NLM)

Location:  On the main NIH Campus
Building:  Building 38A (Lister Hill National Center)
Street Address:  38A Library Drive
City:  Bethesda
State:  Maryland
Zip Code:  20892

Name:   Jane Ye
Phone:   3015944927
Fax:   3014022952
Name:   Ebony Hughes
Phone:   3014518038
Fax:   3014022952
View by: Day | Week | Month | Year

Calendar Home Page | Feedback | NIH Home Page