Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling. (arXiv:2210.12156v1 [cs.LG])

Health conditions among patients in intensive care units (ICUs) are monitored
via electronic health records (EHRs), composed of numerical time series and
lengthy clinical note sequences, both taken at irregular time intervals.
Dealing with such irregularity in every modality, and integrating irregularity
into multimodal representations to improve medical predictions, is a
challenging problem. In this paper, we address this problem by (1) modeling
irregular time series by incorporating hand-crafted imputation embeddings into
learned interpolation embeddings via a gating mechanism; (2) casting a series
of clinical note representations as multivariate irregular time series and
tackling irregularity via a time attention mechanism; and (3) fusing
multimodalities with an interleaved attention mechanism across temporal steps
to integrate irregularity into multimodal representations. To the best of our
knowledge, this is the first work to thoroughly model irregularity in
multimodalities and to take into account temporal knowledge in multimodal
fusion, for improving medical predictions. The results on two medical
prediction tasks show that our proposed methods outperform the state-of-the-art
(SOTA) methods in both every single modality and multimodal fusion scenarios,
illustrating the effectiveness of our methods and the value of modeling
irregularity in multimodal fusion.



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