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A Label Attention Model for ICD Coding from Clinical Text
Multi-modal fusion approaches aim to integrate information from different
data sources. Unlike natural datasets, such as in audio-visual applications,
where samples consist of "paired" modalities, data in healthcare is often
collected asynchronously. Hence, requiring the presence of all modalities for a
given sample is not realistic for clinical tasks and significantly limits the
size of the dataset during training. In this paper, we propose MedFuse, a
conceptually simple yet promising LSTM-based fusion module that can accommodate
uni-modal as well as multi-modal input. We evaluate the fusion method and
introduce new benchmark results for in-hospital mortality prediction and
phenotype classification, using clinical time-series data in the MIMIC-IV
dataset and corresponding chest X-ray images in MIMIC-CXR. Compared to more
complex multi-modal fusion strategies, MedFuse provides a performance
improvement by a large margin on the fully paired test set. It also remains
robust across the partially paired test set containing samples with missing
chest X-ray images. We release our code for reproducibility and to enable the
evaluation of competing models in the future.
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