  Labeling a large number of electronic health records is expensive and time
consuming, and having a labeling assistant tool can significantly reduce
medical experts' workload. Nevertheless, to gain the experts' trust, the tool
must be able to explain the reasons behind its outputs. Motivated by this, we
introduce Explainable Labeling Assistant (XLabel) a new visual-interactive tool
for data labeling. At a high level, XLabel uses Explainable Boosting Machine
(EBM) to classify the labels of each data point and visualizes heatmaps of
EBM's explanations. As a case study, we use XLabel to help medical experts
label electronic health records with four common non-communicable diseases
(NCDs). Our experiments show that 1) XLabel helps reduce the number of labeling
actions, 2) EBM as an explainable classifier is as accurate as other well-known
machine learning models outperforms a rule-based model used by NCD experts, and
3) even when more than 40% of the records were intentionally mislabeled, EBM
could recall the correct labels of more than 90% of these records.
