ArXiv Preprint
Electronic health record (EHR) data is sparse and irregular as it is recorded
at irregular time intervals, and different clinical variables are measured at
each observation point. In this work, we propose a multi-view features
integration learning from irregular multivariate time series data by
self-attention mechanism in an imputation-free manner. Specifically, we devise
a novel multi-integration attention module (MIAM) to extract complex
information inherent in irregular time series data. In particular, we
explicitly learn the relationships among the observed values, missing
indicators, and time interval between the consecutive observations,
simultaneously. The rationale behind our approach is the use of human knowledge
such as what to measure and when to measure in different situations, which are
indirectly represented in the data. In addition, we build an attention-based
decoder as a missing value imputer that helps empower the representation
learning of the inter-relations among multi-view observations for the
prediction task, which operates at the training phase only. We validated the
effectiveness of our method over the public MIMIC-III and PhysioNet challenge
2012 datasets by comparing with and outperforming the state-of-the-art methods
for in-hospital mortality prediction.
Yurim Lee, Eunji Jun, Heung-Il Suk
2021-01-25
