Attention-based neural networks for clinical prediction modelling on electronic health records.

ABSTRACT:

Background

Deep learning models have had a lot of success in various fields. However, on structured data they have struggled. Here we apply four state-of-the-art supervised deep learning models using the attention mechanism and compare against logistic regression and XGBoost using discrimination, calibration and clinical utility.

Methods

We develop the models using a general practitioners database. We implement a recurrent neural network, a transformer with and without reverse distillation and a graph neural network. We measure discrimination using the area under the receiver operating characteristic curve (AUC) and the area under the precision recall curve (AUPRC). We assess smooth calibration using restricted cubic splines and clinical utility with decision curve analysis.

SUBMITTER: Fridgeirsson EA 

PROVIDER: S-EPMC10701944 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature