Fusing Continuous-valued Medical Labels using a Bayesian Model

TL;DR

This paper introduces a Bayesian model for aggregating continuous-valued medical labels from multiple algorithms, improving accuracy and estimating individual algorithm bias and precision, demonstrated on ECG QT interval estimation.

Contribution

The Bayesian Continuous-valued Label Aggregator (BCLA) is a novel method that reliably combines multiple algorithm labels and infers their biases and precisions, outperforming existing approaches.

Findings

BCLA achieved an RMSE of 11.78ms, outperforming other methods.

It accurately estimates the bias and precision of each labeling algorithm.

BCLA significantly outperformed the best challenge entry and other aggregation strategies.

Abstract

With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels include interventions, changes in activity (e.g. sleep) and changes in physiology (e.g. arrhythmias). However, automated algorithms tend to be unreliable resulting in lower quality care. Expert annotations are scarce, expensive, and prone to significant inter- and intra-observer variance. To address these problems, a Bayesian Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic indicator) estimation from the electrocardiogram using labels from the 2006 PhysioNet/Computing in Cardiology Challenge database. It was compared to the mean, median, and a previously proposed Expectation Maximization (EM) label aggregation approaches. While accurately predicting each labelling algorithm's bias and precision, the root-mean-square error of the BCLA was 11.78$\pm$0.63ms, significantly outperforming the best Challenge entry (15.37$\pm$2.13ms) as well as the EM, mean, and median voting strategies (14.76$\pm$0.52ms, 17.61$\pm$0.55ms, and 14.43$\pm$0.57ms respectively with $p<0.0001$).