Patient risk stratification for hospital-associated C. diff as a time-series classification task
Name
4525-patient-risk-stratification-for-hospital-associated-c-diff-as-a-time-series-classification-task.pdf
Description
Published version
Size
500.03 KB
Format
Adobe PDF
Checksum (MD5)
c72bed8ddd09ec5273cef3a66403303a
Author(s)
Wiens, Jenna Anne Marleau
Guttag, John V
Horvitz, Eric
Date Issued
2012
Journal
Advances in Neural Information Processing Systems 25 (NIPS 2012)
Publisher
Neural Information Processing Systems Foundation, Inc
Citation
Wiens, Jenna et al. "Patient risk stratification for hospital-associated C. diff as a time-series classification task."Advances in Neural Information Processing Systems 25 (NIPS 2012), December 2012, Lake Tahoe, Nevada, Neural Information Processing Systems Foundation, 2012.
Version
Final published version
Abstract
A patient's risk for adverse events is affected by temporal processes including the nature and timing of diagnostic and therapeutic activities, and the overall evolution of the patient's pathophysiology over time. Yet many investigators ignore this temporal aspect when modeling patient outcomes, considering only the patient's current or aggregate state. In this paper, we represent patient risk as a time series. In doing so, patient risk stratification becomes a time-series classification task. The task differs from most applications of time-series analysis, like speech processing, since the time series itself must first be extracted. Thus, we begin by defining and extracting approximate risk processes, the evolving approximate daily risk of a patient. Once obtained, we use these signals to explore different approaches to time-series classification with the goal of identifying high-risk patterns. We apply the classification to the specific task of identifying patients at risk of testing positive for hospital acquired Clostridium difficile. We achieve an area under the receiver operating characteristic curve of 0.79 on a held-out set of several hundred patients. Our two-stage approach to risk stratification outperforms classifiers that consider only a patient's current state (p<0.05).
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/4525-patient-risk-stratification-for-hospital-associated-c-diff-as-a-time-series-classification-task
