A comparison of attentional neural network architectures for modeling with electronic medical records.

ABSTRACT:

Objective

Attention networks learn an intelligent weighted averaging mechanism over a series of entities, providing increases to both performance and interpretability. In this article, we propose a novel time-aware transformer-based network and compare it to another leading model with similar characteristics. We also decompose model performance along several critical axes and examine which features contribute most to our model's performance.

Materials and methods

Using data sets representing patient records obtained between 2017 and 2019 by the Kaiser Permanente Mid-Atlantic States medical system, we construct four attentional models with varying levels of complexity on two targets (patient mortality and hospitalization). We examine how incorporating transfer learning and de

SUBMITTER: Finch A 

PROVIDER: S-EPMC8358476 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature