Scientific Premise:

That machine learning natural language processing algorithms can be employed to extract patterns from pathology reports to inform care and reimbursement and that there exists ambiguity in the text which may obfuscate findings/reimbursement.

Motivation:

“…Pathology reports serve as an auditable trial of a patient’s clinical narrative, containing text pertaining to diagnosis, prognosis, and specimen processing. Recent works have utilized natural language processing (NLP) pipelines, which include rule-based or machine- learning analytics, to uncover textual patterns that inform clinical endpoints and biomarker information. Although deep learning methods have come to the forefront of NLP, there have been limited comparisons with the performance of other machine-learning methods in extracting key insights for the prediction of medical procedure information, which is used to inform reimbursement for pathology departments….”

Manuscripts:

1. Levy, J., Vattikonda, N., Haudenschild, C., et al. Comparison of machine-learning algorithms for the prediction of current procedural terminology (CPT) codes from pathology reports. Journal of Pathology Informatics 13, 3 (2022).