posted on 2018-01-01, 00:00authored byY Zhu, X Fan, J Wu, Xiao LiuXiao Liu, J Shi, C Wang
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.
Bichindaritz I, Guttmann C, Herrero P, Koch F, Koster A, Lenz R, López Ibáñez B, Marling C, Martin C, Montagna S, Montani S, Reichert M, Riaño D, Schumacher MI, ten Teije A, Wiratunga N
Title of proceedings
AIH 2018 : Proceedings of the First Joint Workshop on AI in Health 2018
Event
European Association for Artificial Intelligence. Workshop (1st : 2018 : Stockholm, Sweden)
Publisher
M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen
Place of publication
Aachen, Germany
Series
European Association for Artificial Intelligence Workshop