Leveraging patient similarities via graph neural networks to predict phenotypes from temporal data

Dimitrios Proios, Anthony Yazdani, Alban Bornet, Julien Ehrsam, Islem Rekik, Douglas Teodoro

Abstract: Several machine learning approaches have been proposed to automatically derive clinical phenotypes from patient data. Nevertheless, methods leveraging similarity-based patient networks remain underexplored for temporal data. In this work, we propose a graph neural network (GNN) model that learns patient representation using different network configurations and feature modes. To explore the sequential nature of time series, features were extracted using a recurrent neural network (RNN) and embedded using information from the network structure via the GNN. Our method improves upon statistical and RNN baselines, with performance boosts up to 1% and 22% accuracy in the inductive and transductive settings, respectively. We also show that network configurations significantly impact performance in the transductive learning setting. Thus, automated phenotyping models based on GNNs could be used to support phenotype-based clinical research and ultimately for personalized clinical decision support.Data and Code Availability: This paper uses the MIMIC-III dataset [1], which is available on the PhysioNet repository [2]. The experiments are based on the public open source phenotyping benchmark of Harutyunyan et al. [3]. All our source code is publicly available at https://github.com/ds4dh/mimic3-benchmarks-GraDSCI23.