Using a manifold-based approach to extract clinical codes associated with winter respiratory viruses at an emergency department

Abstract

Every winter, respiratory viruses put most Emergency Departments (ED) around the world under intense pressure. To reduce the consequent stress for hospitals, anticipation of the massive increase of intakes for illness-based symptoms is essential. As the Covid-19 2020 pandemic clearly illustrates, patients are not systematically tested. The ED staff therefore has no real-time knowledge of the presence of the virus in the patients flow. To address this issue, we propose here to use the hospital’s laboratory-confirmed database as an attractor for the manifold-based approach for clustering the clinical codes associated with respiratory viruses. We propose a new framework based on the embedding of time series onto the Stiefel manifold, coupled with a density-based clustering algorithm (HDBSCAN) enhanced by a reduction of dimension (UMAP) for the clustering on that manifold. In particular, we show, based on real data sets of two academic hospitals in France, the significant benefits of using geometrical approaches for time series clustering as compared to traditional methods.