ICU Mortality Prediction Using Long Short-Term Memory Networks
Manel Mili Asma Kerkeni Asma Ben Abdallah Mohamed Hedi Bedoui
Extensive bedside monitoring in Intensive Care Units (ICUs) has resulted in
complex temporal data regarding patient physiology, which presents an upscale
context for clinical data analysis. In the other hand, identifying the
time-series patterns within these data may provide a high aptitude to predict
clinical events. Hence, we investigate, during this work, the implementation of
an automatic data-driven system, which analyzes large amounts of multivariate
temporal data derived from Electronic Health Records (EHRs), and extracts
high-level information so as to predict in-hospital mortality and Length of
Stay (LOS) early. Practically, we investigate the applicability of LSTM network
by reducing the time-frame to 6-hour so as to enhance clinical tasks. The
experimental results highlight the efficiency of LSTM model with rigorous
multivariate time-series measurements for building real-world prediction
engines.