Integrating Physiological Time Series and Clinical Notes with Deep
Learning for Improved ICU Mortality Prediction
- URL: http://arxiv.org/abs/2003.11059v2
- Date: Thu, 18 Mar 2021 21:00:52 GMT
- Title: Integrating Physiological Time Series and Clinical Notes with Deep
Learning for Improved ICU Mortality Prediction
- Authors: Satya Narayan Shukla, Benjamin M. Marlin
- Abstract summary: We study how physiological time series data and clinical notes can be integrated into a unified mortality prediction model.
Our results show that a late fusion approach can provide a statistically significant improvement in prediction mortality over using individual modalities in isolation.
- Score: 21.919977518774015
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Intensive Care Unit Electronic Health Records (ICU EHRs) store multimodal
data about patients including clinical notes, sparse and irregularly sampled
physiological time series, lab results, and more. To date, most methods
designed to learn predictive models from ICU EHR data have focused on a single
modality. In this paper, we leverage the recently proposed
interpolation-prediction deep learning architecture(Shukla and Marlin 2019) as
a basis for exploring how physiological time series data and clinical notes can
be integrated into a unified mortality prediction model. We study both early
and late fusion approaches and demonstrate how the relative predictive value of
clinical text and physiological data change over time. Our results show that a
late fusion approach can provide a statistically significant improvement in
mortality prediction performance over using individual modalities in isolation.
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