Automatic code assignment to medical text

Authors:
Koby Crammer;Mark Dredze;Kuzman Ganchev;Partha Pratim Talukdar;Steven Carroll
Affiliations:
University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;The Children's Hospital of Philadelphia, Philadelphia, PA
Venue:
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Year:
2007

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Abstract

Code assignment is important for handling large amounts of electronic medical data in the modern hospital. However, only expert annotators with extensive training can assign codes. We present a system for the assignment of ICD-9-CM clinical codes to free text radiology reports. Our system assigns a code configuration, predicting one or more codes for each document. We combine three coding systems into a single learning system for higher accuracy. We compare our system on a real world medical dataset with both human annotators and other automated systems, achieving nearly the maximum score on the Computational Medicine Center's challenge.