  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.
