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Predicting ICU mortality by supervised bidirectional LSTM networks

conference contribution
posted on 2018-01-01, 00:00 authored by Y 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.

History

Event

European Association for Artificial Intelligence. Workshop (1st : 2018 : Stockholm, Sweden)

Volume

2142

Series

European Association for Artificial Intelligence Workshop

Pagination

49 - 60

Publisher

M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen

Location

Stockholm, Sweden

Place of publication

Aachen, Germany

Start date

2018-07-13

End date

2018-07-14

ISSN

1613-0073

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2018, the authors

Editor/Contributor(s)

I Bichindaritz, C Guttmann, P Herrero, F Koch, A Koster, R Lenz, B López Ibáñez, C Marling, C Martin, S Montagna, S Montani, M Reichert, D Riaño, M Schumacher, A ten Teije, N Wiratunga

Title of proceedings

AIH 2018 : Proceedings of the First Joint Workshop on AI in Health 2018

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