Edges are all you need: Potential of Medical Time Series Analysis with Graph Neural Networks.

Machine learning is a powerful tool to develop algorithms for clinical diagnosis. However, standard machine learning algorithms are not perfectly suited for clinical data since the data are interconnected and may contain time series. As shown for recommender systems and molecular property predictions graph neural networks (GNNs) may represent a powerful alternative. In this study, we evaluated the performance and time consumption of GNNs compared to state-of-the-art algorithms on the classification of sepsis from blood count data as well as the importance and slope of each feature for the final classification. GNNs on evaluated similarity-graphs achieved an AUROC of up to 0.8747 comparable to the performance of ensemble-based machine learning algorithms and a neural network. The exploitation of time series information with GNNs achieved a superior AUROC of up to 0.9565. Finally, we discovered that feature slope and importance highly differ between tree-based (e.g., XGBoost) and non-tree-based algorithms (e.g., GNN).