Interpretable machine learning for time-to-event prediction in medicine and healthcare

arXiv:2303.09817v2 Announce Type: replace
Abstract: Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, only a few interpretable machine learning methods comply with its challenges. To facilitate a comprehensive explanatory analysis of survival models, we formally introduce time-dependent feature effects and global feature importance explanations. We show how post-hoc interpretation methods allow for finding biases in AI systems predicting length of stay using a novel …

abstract analysis applications arxiv cancer challenges cs.cv event healthcare hospital however machine machine learning medical medicine prediction stat.ap survival type