Aug. 4, 2022, 1:11 a.m. | Chirag Nagpal, Willa Potosnak, Artur Dubrawski

cs.LG updates on arXiv.org arxiv.org

Applications of machine learning in healthcare often require working with
time-to-event prediction tasks including prognostication of an adverse event,
re-hospitalization or death. Such outcomes are typically subject to censoring
due to loss of follow up. Standard machine learning methods cannot be applied
in a straightforward manner to datasets with censored outcomes. In this paper,
we present auton-survival, an open-source repository of tools to streamline
working with censored time-to-event or survival data. auton-survival includes
tools for survival regression, adjustment in the …

arxiv data evaluation event lg package regression survival time time-to-event data

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