From Data to Knowledge - 408 - David Kale
Channel: ckleinastro
Date: 11/05/2012

Slides: http://lyra.berkeley.edu/CDIConf/pdfs/kale-vpicu-clinical.pdf

David Kale: "Unsupervised Pattern Discovery in Sparsely Sampled Clinical Time Series". A video from the UC Berkeley Conference: From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications (May 7-11, 2012).

Abstract
David Kale (Children's Hospital LA, Virtual Pediatric Intensive Care Unit)
Time series data recorded in real electronic health care record (EHR) systems, such as from pediatric intensive care units (PICUs), presents a challenging set of problems that appear to differ from those present in other streaming data domains. In addition to the complexity of the underlying dynamic system (the human body, affected by various disease processes), there is a requirement that observations be manually verified by clinical staff, resulting in data that is sparse, irregularly sampled, incomplete, and potentially biased in subtle ways. 

Through a particular research question (e.g., searching for similar episodes within a historical database), we will explore the opportunities and challenges presented by such data, increasingly prevalent in commercial EHRs. We will present some results obtained using a simple model and discuss active research directions we are pursuing with collaborators such as Benjamin Marlin from University of Massachusetts Amherst, Christian Shelton at UC Riverside, and Yan Liu at USC. On a more practical note, we will mention how researchers can get started in this exciting new area, including how they can obtain data.