ML Lunch (Sept 30, 2013): Suchi Saria





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Published on Sep 30, 2013

Topic: Discovering Vocabulary in Clinical Temporal Data with Application to Tracking Illness Severity in Infants

Speaker: Suchi Saria

Large amounts of data are routinely collected in the Electronic Medical Record yet their use in informing patient care is limited to manual assessment by the caregivers. In this talk, we discuss probabilistic approaches motivated by clinical practice for analyzing continuously measured physiologic data. We develop our models on data collected from instrumenting a neonatal intensive care unit. Based on insights derived from modeling this data, we tackle the application of risk stratification. As part of routine care, every infant at birth is risk stratified based on the Apgar score. We develop a cheap, non-invasive and simple risk stratification tool using markers from physiologic data for predicting infants at risk, dubbed by Science news as the modern electronic Apgar.

Bio: Suchi Saria is an Assistant Professor at Johns Hopkins University within the Schools of Engineering and Public Health. She received her PhD in machine learning from Stanford University. Her research interests span computational modeling of diverse, large temporal data, and in particular those from sensing devices and the electronic health record for improving patient management. Her work on predictive modeling for clinical data from infants has been covered by numerous national and international press sources. She is the recipient of multiple awards including a best student, a best paper finalist, Microsoft Full Scholarships, Rambus Corporation fellowship, an NSF Computing Innovation fellowship, and a Gordon and Betty Moore foundation award.

More ML lunch talks: http://www.cs.cmu.edu/~learning/


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