Developing A Visual-Interactive Interface for Electronic Health Record Labeling: An Explainable Machine Learning Approach
This addresses the workload of medical experts in labeling health records, but it is incremental as it applies an existing explainable method to a specific domain.
The paper tackles the problem of expensive and time-consuming labeling of electronic health records by introducing XLabel, a visual-interactive tool that uses Explainable Boosting Machine (EBM) to classify labels and visualize explanations, resulting in reduced labeling actions, EBM matching the accuracy of other models while outperforming a rule-based one, and recalling over 90% of correct labels even with 40% mislabeled records.
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.