State Based Hidden Markov Models for Temporal Pattern Discovery in Critical Care

Catherine Inibhunu1, Carolyn McGregor AM1

  • 1University of Ontario Institute of Technology



Poster Session


10:00 - 17:00 | Mon 29 Oct | Foyer | A1P-E

Cognitive Computing & Deep Learning in Life Sciences

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We are studying the challenge of finding a good set of features that represent well the temporal aspects in time series data. We argue that discovery of such features could be crucial to understanding hidden relationships in data. In particular, in critical care where time oriented data is generated every second on patients physiological features, discovery of any hidden relationships could aid in discovery of unknown and potentially life threatening conditions before they happen. Additionally, this discovery could help in better dissemination of healthcare services leading to better outcomes and experiences for patients.

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