(Submitted on 24 Mar 2020)

Abstract: In high stakes applications such as healthcare and finance analytics, the
interpretability of predictive models is required and necessary for domain
practitioners to trust the predictions. Traditional machine learning models,
e.g., logistic regression (LR), are easy to interpret in nature. However, many
of these models aggregate time-series data without considering the temporal
correlations and variations. Therefore, their performance cannot match up to
recurrent neural network (RNN) based models, which are nonetheless difficult to
interpret. In this paper, we propose a general framework TRACER to facilitate
accurate and interpretable predictions, with a novel model TITV devised for
healthcare analytics and other high stakes applications such as financial
investment and risk management. Different from LR and other existing RNN-based
models, TITV is designed to capture both the time-invariant and the
time-variant feature importance using a feature-wise transformation subnetwork
and a self-attention subnetwork, for the feature influence shared over the
entire time series and the time-related importance respectively. Healthcare
analytics is adopted as a driving use case, and we note that the proposed
TRACER is also applicable to other domains, e.g., fintech. We evaluate the
accuracy of TRACER extensively in two real-world hospital datasets, and our
doctors/clinicians further validate the interpretability of TRACER in both the
patient level and the feature level. Besides, TRACER is also validated in a
high stakes financial application and a critical temperature forecasting
application. The experimental results confirm that TRACER facilitates both
accurate and interpretable analytics for high stakes applications.