Modeling asynchronous event sequences with RNNs

• Published in: Journal of biomedical informatics Vol. 83; pp. 167 - 177
• Main Authors: Wu, Stephen, Liu, Sijia, Sohn, Sunghwan, Moon, Sungrim, Wi, Chung-il, Juhn, Young, Liu, Hongfang
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2018
• Subjects: Asthma; Asthma - classification; Child; Child, Preschool; Computer Simulation; Deep learning; Electronic Health Records; Humans; Infant; Intensive Care Units - statistics & numerical data; Neural Networks, Computer; Recurrence; Temporal data; Deep learning; Temporal data; Electronic health records; Asthma

[Display omitted] •Asynchronous clinical data can be modeled as event sequences.•Event sequences are not necessarily synchronous, even, or co-cardinal.•Recurrent Neural Networks (RNNs) can support timing alongside sequence values.•Chronic diseases (asthma) and critical care (ICU mortality) differ in...