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Intensive Care in-hospital mortality prediction has various clinicalapplications. Neural prediction models, especially when capitalising onclinical notes, have been put forward as improvement on currently existingmodels. However, to be acceptable these models should be performant andtransparent. This work studies different attention mechanisms for clinicalneural prediction models in terms of their discrimination and calibration.Specifically, we investigate sparse attention as an alternative to denseattention weights in the task of in-hospital mortality prediction from clinicalnotes. We evaluate the attention mechanisms based on: i) local self-attentionover words in a sentence, and ii) global self-attention with a transformerarchitecture across sentences. We demonstrate that the sparse mechanismapproach outperforms the dense one for the local self-attention in terms ofpredictive performance with a publicly available dataset, and puts higherattention to prespecified relevant directive words. The performance at thesentence level, however, deteriorates as sentences including the influentialdirective words tend to be dropped all together.

Mortality Prediction Models with Clinical Notes Using Sparse Attention at the Word and Sentence Levels