Supervised Learning in the Presence of Noise: Application in ICD-10 Code Classification

TL;DR

This paper addresses the challenge of training accurate ICD-10 classifiers despite systematic label noise caused by human coder errors, proposing a novel noise-aware training method that improves classification performance.

Contribution

It introduces a new approach to handle systematic label noise in ICD-10 coding by identifying common misuses and developing a noise-aware training strategy.

Findings

Proposed method outperforms baseline classifiers on expert-validated labels.

Systematic noise in ICD coding is linked to code hierarchy and coder confusion.

Handling systematic noise improves classifier robustness and accuracy.

Abstract

ICD coding is the international standard for capturing and reporting health conditions and diagnosis for revenue cycle management in healthcare. Manually assigning ICD codes is prone to human error due to the large code vocabulary and the similarities between codes. Since machine learning based approaches require ground truth training data, the inconsistency among human coders is manifested as noise in labeling, which makes the training and evaluation of ICD classifiers difficult in presence of such noise. This paper investigates the characteristics of such noise in manually-assigned ICD-10 codes and furthermore, proposes a method to train robust ICD-10 classifiers in the presence of labeling noise. Our research concluded that the nature of such noise is systematic. Most of the existing methods for handling label noise assume that the noise is completely random and independent of features or labels, which is not the case for ICD data. Therefore, we develop a new method for training robust classifiers in the presence of systematic noise. We first identify ICD-10 codes that human coders tend to misuse or confuse, based on the codes' locations in the ICD-10 hierarchy, the types of the codes, and baseline classifier's prediction behaviors; we then develop a novel training strategy that accounts for such noise. We compared our method with the baseline that does not handle label noise and the baseline methods that assume random noise, and demonstrated that our proposed method outperforms all baselines when evaluated on expert validated labels.