Aiming for Relevance

arXiv:2403.18668v1 Announce Type: new
Abstract: Vital signs are crucial in intensive care units (ICUs). They are used to track the patient's state and to identify clinically significant changes. Predicting vital sign trajectories is valuable for early detection of adverse events. However, conventional machine learning metrics like RMSE often fail to capture the true clinical relevance of such predictions. We introduce novel vital sign prediction performance metrics that align with clinical contexts, focusing on deviations from clinical norms, overall trends, and …

abstract arxiv clinical cs.ai cs.hc cs.lg detection events however identify machine machine learning metrics patient state stat.ml true type units vital