A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse , Heterogeneous Clinical Data

  • Ghassemi M
  • Naumann T
  • Brennan T
 et al. 
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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 sig- nals 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 pa- tients: firstly, estimating cerebrovascular pressure reac- tivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the inter- actions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality predic- tion using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal in- terpolation 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). 1

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  • Applications Track

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Authors

  • Marzyeh Ghassemi

  • Tristan Naumann

  • Thomas Brennan

  • David a Clifton

  • Peter Szolovits

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