  The automated analysis of medical time series, such as the electrocardiogram
(ECG), electroencephalogram (EEG), pulse oximetry, etc, has the potential to
serve as a valuable tool for diagnostic decisions, allowing for remote
monitoring of patients and more efficient use of expensive and time-consuming
medical procedures. Deep neural networks (DNNs) have been demonstrated to
process such signals effectively. However, previous research has primarily
focused on classifying medical time series rather than attempting to regress
the continuous-valued physiological parameters central to diagnosis. One
significant challenge in this regard is the imbalanced nature of the dataset,
as a low prevalence of abnormal conditions can lead to heavily skewed data that
results in inaccurate predictions and a lack of certainty in such predictions
when deployed. To address these challenges, we propose HypUC, a framework for
imbalanced probabilistic regression in medical time series, making several
contributions. (i) We introduce a simple kernel density-based technique to
tackle the imbalanced regression problem with medical time series. (ii)
Moreover, we employ a probabilistic regression framework that allows
uncertainty estimation for the predicted continuous values. (iii) We also
present a new approach to calibrate the predicted uncertainty further. (iv)
Finally, we demonstrate a technique to use calibrated uncertainty estimates to
improve the predicted continuous value and show the efficacy of the calibrated
uncertainty estimates to flag unreliable predictions. HypUC is evaluated on a
large, diverse, real-world dataset of ECGs collected from millions of patients,
outperforming several conventional baselines on various diagnostic tasks,
suggesting a potential use-case for the reliable clinical deployment of deep
learning models.
