Automatic Code Assignment to Medical Text

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.