Exacerbations of chronic obstructive pulmonary disease (COPD) are a major cause of morbidity and mortality. Various models for identifying exacerbations have been proposed, but few models are based on continuous and passively recorded respiratory physiology data. We enrolled 17 subjects with COPD with at least 2 exacerbations in the prior 12 months. We developed machine learning models using aggregated respiratory rates and expiratory times collected passively in a home-setting to identify COPD exacerbations. Models achieved areas under the ROC curve of 0.7989 and 0.8720 respectively, for exacerbations defined by self-reported outcomes and COPD Assessment Test (CAT) score changes. This pilot study demonstrates the feasibility of using passively recorded respiratory physiological data to identify COPD exacerbations.
Fast Approximation Algorithm for Non-Monotone DR-submodular Maximization under Size Constraint
arXiv:2511.02254v1 Announce Type: cross Abstract: This work studies the non-monotone DR-submodular Maximization over a ground set of $n$ subject to a size constraint $k$. We