Multi-view Integration Learning for Irregularly-sampled Clinical Time Series

• URL: http://arxiv.org/abs/2101.09986v2
• Date: Tue, 26 Jan 2021 03:25:12 GMT
• Title: Multi-view Integration Learning for Irregularly-sampled Clinical Time Series
• Authors: Yurim Lee, Eunji Jun, Heung-Il Suk
• Abstract summary: We propose a multi-view features integration learning from irregular time series data by self-attention mechanism in an imputation-free manner. We explicitly learn the relationships among the observed values, missing indicators, and time interval between the consecutive observations, simultaneously. 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.
• Score: 1.9639092030562577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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