The increasing availability of large collections of electronic health record (EHR) data and
unprecedented technical advances in deep learning (DL) have sparked a surge of research
interest in developing DL based clinical decision support systems for diagnosis, prognosis,
and treatment. Despite the recognition of the value of deep learning in healthcare,
impediments to further adoption in real healthcare settings remain due to the black‐box
nature of DL. Therefore, there is an emerging need for interpretable DL, which allows end …

The increasing availability of large collections of electronic health record (EHR) data and
unprecedented technical advances in deep learning (DL) have sparked a surge of research
interest in developing DL based clinical decision support systems for diagnosis, prognosis,
and treatment. Despite the recognition of the value of deep learning in healthcare,
impediments to further adoption in real healthcare settings remain due to the black-box
nature of DL. Therefore, there is an emerging need for interpretable DL, which allows end …