Predicting ICU mortality by supervised bidirectional LSTM networks.
Yao Tai Zhu, Xiaoliang Fan, Jinzhun Wu, Xiao Liu, Jia Shun Shi, Cheng Wang · Own your potential (DEAKIN) · 2018
Mortality prediction in the Intensive Care Unit (ICU) is considered as one of critical steps for the treatment of patients in serious condition. It is a big challenge to model time-series variables for mortality prediction in ICU, because physiological variables such as heart rate and blood pressure are sampled with inconsistent time frequencies. In addition, it is difficult to capture the timing changes of clinical data and to interpret the prediction result of ICU mortality. To deal with these challenges, in this paper, we propose a novel ICU mortality prediction algorithm combining bidirectional LSTM (Long Short-Term Memory) model with supervised learning. First, we preprocess 37 time-series variables related to patients’ signs. Second, we construct the Bidirectional LSTM model with supervision technique to accurately reflect significant changes in patients’ signs. Finally, we train and evaluate our model using a real-world dataset containing 4,000 ICU patients. Experimental results show that our proposed method can significantly outperform many baseline methods. © CEUR-WS. All rights reserved.