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High-Throughput Machine Learning from EHR Data

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Air date: Wednesday, March 8, 2017, 2:00:00 PM
Time displayed is Eastern Time, Washington DC Local
Views: Total views: 385, (215 Live, 170 On-demand)
Category: Special
Runtime: 01:04:12
Description: NLM Informatics Lecture

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.

David Page is a Vilas Distinguished Achievement Professor at the University of Wisconsin-Madison. His primary appointment is in the Dept. of Biostatistics and Medical Informatics in the School of Medicine and Public Health, with an appointment in the Dept. of Computer Sciences where he teaches machine learning. His PhD in CS is from the University of Illinois at Urbana- Champaign, and he became involved in biomedical applications of machine learning as a post-doc in what was then the Computing Laboratory at Oxford University. He directs the Cancer Informatics Shared Resource of the Carbone Cancer Center and is a member of the Genome Center of Wisconsin. He previously served on the NIH's BioData Management and Analysis Study Section and the scientific advisory boards for the Wisconsin Genomics Initiative and the Observational Medical Outcomes Partnership, as well as the editorial boards for Machine Learning and Data Mining and Knowledge Discovery. He currently is on the National Library of Medicine Study Section (BLIRC) and directs the EHR project within UW-Madison's BD2K Center for Predictive Computational Phenotyping.
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NLM Title: High-throughput machine learning from EHR data / David Page.
Author: Page, David.
National Institutes of Health (U.S.),
Publisher:
Abstract: (CIT): NLM Informatics Lecture 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. David Page is a Vilas Distinguished Achievement Professor at the University of Wisconsin-Madison. His primary appointment is in the Dept. of Biostatistics and Medical Informatics in the School of Medicine and Public Health, with an appointment in the Dept. of Computer Sciences where he teaches machine learning. His PhD in CS is from the University of Illinois at Urbana- Champaign, and he became involved in biomedical applications of machine learning as a post-doc in what was then the Computing Laboratory at Oxford University. He directs the Cancer Informatics Shared Resource of the Carbone Cancer Center and is a member of the Genome Center of Wisconsin. He previously served on the NIH's BioData Management and Analysis Study Section and the scientific advisory boards for the Wisconsin Genomics Initiative and the Observational Medical Outcomes Partnership, as well as the editorial boards for Machine Learning and Data Mining and Knowledge Discovery. He currently is on the National Library of Medicine Study Section (BLIRC) and directs the EHR project within UW-Madison's BD2K Center for Predictive Computational Phenotyping.
Subjects: Algorithms
Computer Simulation
Electronic Health Records
Machine Learning
Publication Types: Lectures
Webcasts
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NLM Classification: WX 175
NLM ID: 101703920
CIT Live ID: 21815
Permanent link: https://videocast.nih.gov/launch.asp?22171