Title: Bayesian LSTMs in medicine,
Abstract: The medical field stands to see significant benefits from the recent advances
in deep learning. Knowing the uncertainty in the decision made by any machine
learning algorithm is of utmost importance for medical practitioners. This
study demonstrates the utility of using Bayesian LSTMs for classification of
medical time series. Four medical time series datasets are used to show the
accuracy improvement Bayesian LSTMs provide over standard LSTMs. Moreover, we
show cherry-picked examples of confident and uncertain classifications of the
medical time series. With simple modifications of the common practice for deep
learning, significant improvements can be made for the medical practitioner and
patient.