Developing A Visual-Interactive Interface for Electronic Health Record Labeling: An Explainable Machine Learning Approach

TL;DR

This paper introduces XLabel, a visual-interactive, explainable machine learning tool that aids medical experts in efficiently labeling electronic health records for non-communicable diseases, reducing workload and maintaining high accuracy even with noisy data.

Contribution

The paper presents XLabel, a novel explainable labeling assistant that combines EBM with visual explanations to improve accuracy and efficiency in medical record labeling.

Findings

XLabel reduces the number of labeling actions needed.

EBM classifier matches the accuracy of other models.

EBM recalls over 90% of mislabeled records.

Abstract

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.