Assigning ICD-O-3 codes to pathology reports using neural multi-task training with hierarchical regularization

Abstract

Tracking population-level cancer information is essential for researchers, clinicians, policymakers, and the public. Unfortunately, much of the information is stored as unstructured data in pathology reports. Thus, too process the information, we require either automated extraction techniques or manual curation. Moreover, many of the cancer-related concepts appear infrequently in real-world training datasets. Automated extraction is difficult because of the limited data. This study introduces a novel technique that incorporates structured expert knowledge to improve histology and topography code classification models. Using pathology reports collected from the Kentucky Cancer Registry, we introduce a novel multi-task training approach with hierarchical regularization that incorporates structured information about the International Classification of Diseases for Oncology, 3rd Edition classes to improve predictive performance. Overall, we find that our method improves both micro and macro F1. For macro F1, we achieve up to a 6% absolute improvement for topography codes and up to 4% absolute improvement for histology codes.