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NLM Informatics and Data Science: From Data to Decisions: Large-Scale Causal Inference in Biomedicine

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Air date: Wednesday, March 6, 2019, 4:00:00 PM
Time displayed is Eastern Time, Washington DC Local
Views: Total views: 319, (192 Live, 127 On-demand)
Category: Special
Runtime: 01:03:01
Description: NLM Informatics and Data Science Lecture Series

The collection of massive observational datasets has led to unprecedented opportunities for causal inference, such as using electronic health records to identify risk factors for disease. However, our ability to understand these complex data sets has not grown at the same pace as our ability to collect them. While causal inference has traditionally focused on pair-wise relationships between variables, biological systems are highly complex and knowing when events may happen is often as important as knowing whether they will. Motivated by the analysis of intensive care unit data, this talk discusses new methods to automatically extract causal relationships from data and how these have been applied to gain new insight into stroke recovery. Finally, the speaker will discuss recent findings in cognitive science and how they can help us make better use of causal information for decision-making.

Samantha Kleinberg is an Associate Professor of Computer Science at Stevens Institute of Technology. She received her PhD in Computer Science from New York University and was a Computing Innovation Fellow at Columbia University in the Department of Biomedical informatics. She is the recipient of NSF CAREER and JSMF Complex Systems Scholar Awards and is a 2016 Kavli Fellow of the National Academy of Sciences. She is the author of “Causality, Probability, and Time” (Cambridge University Press, 2012) and “Why: A Guide to Finding and Using Causes” (O’Reilly Media, 2015). Dr. Kleinberg is a current member of NLM's Biomedical Informatics, Library and Data Sciences Review Committee.
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NLM Title: From data to decisions : large-scale causal inference in biomedicine / Samantha Kleinberg.
Author: Kleinberg, Samantha.
National Institutes of Health (U.S.),
Publisher:
Abstract: (CIT): The collection of massive observational datasets has led to unprecedented opportunities for causal inference, such as using electronic health records to identify risk factors for disease. However, our ability to understand these complex data sets has not grown at the same pace as our ability to collect them. While causal inference has traditionally focused on pair-wise relationships between variables, biological systems are highly complex and knowing when events may happen is often as important as knowing whether they will. Motivated by the analysis of intensive care unit data, this talk discusses new methods to automatically extract causal relationships from data and how these have been applied to gain new insight into stroke recovery. Finally, the speaker will discuss recent findings in cognitive science and how they can help us make better use of causal information for decision-making.
Subjects: Big Data
Biomedical Research
Causality
Data Science
Decision Making
Publication Types: Lecture
Webcasts
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Caption Text: Download Caption File
NLM Classification: W 20.5
NLM ID: 101744877
CIT Live ID: 31437
Permanent link: https://videocast.nih.gov/launch.asp?27366