A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data
Name
Szolovits_A multivariate.pdf
Size
601.55 KB
Format
Adobe PDF
Checksum (MD5)
6f2f2ebf4c55721cca742474ac23bb60
Author(s)
Pimentel, Marco A. F.
Clifton, David A.
Ghassemi, Marzyeh
Szolovits, Peter
Feng, Mengling
Naumann, Tristan Josef
Brennan, Thomas Patrick
Date Issued
July 2015
Journal
Proceedings of the 29th AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Ghassemi, Marzyeh et al. "A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data." Proceedings of the 29th AAAI Conference on Artificial Intelligence, 25-30 January, 2015, Austin, Texas, AAII, 2015, pp. 446-453.
Version
Author's final manuscript
Abstract
The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC).
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9393/9279
