  The recent release of large-scale healthcare datasets has greatly propelled
the research of data-driven deep learning models for healthcare applications.
However, due to the nature of such deep black-boxed models, concerns about
interpretability, fairness, and biases in healthcare scenarios where human
lives are at stake call for a careful and thorough examinations of both
datasets and models. In this work, we focus on MIMIC-IV (Medical Information
Mart for Intensive Care, version IV), the largest publicly available healthcare
dataset, and conduct comprehensive analyses of dataset representation bias as
well as interpretability and prediction fairness of deep learning models for
in-hospital mortality prediction. In terms of interpretabilty, we observe that
(1) the best performing interpretability method successfully identifies
critical features for mortality prediction on various prediction models; (2)
demographic features are important for prediction. In terms of fairness, we
observe that (1) there exists disparate treatment in prescribing mechanical
ventilation among patient groups across ethnicity, gender and age; (2) all of
the studied mortality predictors are generally fair while the IMV-LSTM
(Interpretable Multi-Variable Long Short-Term Memory) model provides the most
accurate and unbiased predictions across all protected groups. We further draw
concrete connections between interpretability methods and fairness metrics by
showing how feature importance from interpretability methods can be beneficial
in quantifying potential disparities in mortality predictors.
