Paper Type
short
Description
Mortality models in Intensive Care Units (ICU) are important for clinical decision support tasks such as identifying high-risk patients and prioritizing their care. Previous mortality models have used predictive variables mainly from Electronic Medical Records (EMR) where each patient observation can be represented as a sparse multivariate time series. Bedside monitors are another common data source in ICUs containing high-resolution time series, which have not been explored in combination with EMR data for mortality modelling. We take the first step towards building such a model. Specialized techniques developed for sparse time series cannot be used to model multiple time series at different resolutions. To address this problem, we develop MTS-RNN, a new deep recurrent neural network architecture. Our preliminary experiments on real clinical data show that MTS-RNN outperforms state-of-the-art mortality models in predictive accuracy, highlighting the importance of using clinical time series at multiple resolutions for ICU mortality prediction.
Recommended Citation
Ghanvatkar, Suparna and Rajan, Vaibhav, "Deep Recurrent Neural Networks for Mortality Prediction in Intensive Care using Clinical Time Series at Multiple Resolutions" (2019). ICIS 2019 Proceedings. 12.
https://aisel.aisnet.org/icis2019/data_science/data_science/12
Deep Recurrent Neural Networks for Mortality Prediction in Intensive Care using Clinical Time Series at Multiple Resolutions
Mortality models in Intensive Care Units (ICU) are important for clinical decision support tasks such as identifying high-risk patients and prioritizing their care. Previous mortality models have used predictive variables mainly from Electronic Medical Records (EMR) where each patient observation can be represented as a sparse multivariate time series. Bedside monitors are another common data source in ICUs containing high-resolution time series, which have not been explored in combination with EMR data for mortality modelling. We take the first step towards building such a model. Specialized techniques developed for sparse time series cannot be used to model multiple time series at different resolutions. To address this problem, we develop MTS-RNN, a new deep recurrent neural network architecture. Our preliminary experiments on real clinical data show that MTS-RNN outperforms state-of-the-art mortality models in predictive accuracy, highlighting the importance of using clinical time series at multiple resolutions for ICU mortality prediction.