NDLI: Patient prognosis from vital sign time series: Combining convolutional neural networks with a dynamical systems approach

Content Provider	IEEE Xplore Digital Library
Author	Lehman, Li-wei Ghassemi, Mohammad Snoek, Jasper Nemati, Shamim
Copyright Year	2015
Description	Author affiliation: Massachusetts Institute of Technology, Cambridge, USA (Lehman, Li-wei; Ghassemi, Mohammad) \|\| Emory University, Atlanta, GA, USA (Nemati, Shamim) \|\| Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA (Snoek, Jasper)
Abstract	In this work, we propose a stacked switching vector-autoregressive (SVAR)-CNN architecture to model the changing dynamics in physiological time series for patient prognosis. The SVAR-layer extracts dynamical features (or modes) from the time-series, which are then fed into the CNN-layer to extract higher-level features representative of transition patterns among the dynamical modes. We evaluate our approach using 8-hours of minute-by-minute mean arterial blood pressure (BP) from over 450 patients in the MIMIC-II database. We modeled the time-series using a third-order SVAR process with 20 modes, resulting in first-level dynamical features of size 20×480 per patient. A fully connected CNN is then used to learn hierarchical features from these inputs, and to predict hospital mortality. The combined CNN/SVAR approach using BP time-series achieved a median and interquartile-range AUC of 0.74 [0.69, 0.75], significantly outperforming CNN-alone (0.54 [0.46, 0.59]), and SVAR-alone with logistic regression (0.69 [0.65, 0.72]). Our results indicate that including an SVAR layer improves the ability of CNNs to classify nonlinear and nonstationary time-series.
Starting Page	1069
Ending Page	1072
File Size	708632
Page Count	4
File Format	PDF
ISBN	9781509006854
ISSN	2325887X
e-ISBN	9781509006847
DOI	10.1109/CIC.2015.7411099
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-06
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Creative Commons Attribution License 2.5 (CCAL)
Subject Keyword	Switches Prognostics and health management Physiology
Content Type	Text
Resource Type	Article
Subject	Computer Science Cardiology and Cardiovascular Medicine

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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