Evaluating Feature Types for Encoding Clinical Notes

This paper proposes a machine learning approach to the task of assigning the international standard on classification of diseases ICD-9-CM codes to clinical records. By treating the task as a text categorisation problem, a classification system was built which explores a variety of features including negation, different strategies of measuring gloss overlaps between the content of clinical records and ICD-9-CM code descriptions together with expansion of the glosses from the ICD-9-CM hierarchy. The best classifier achieved an overall F1 value of 88.2 on a data set of 978 free text clinical records, and was better than the performance of two out of three human annotators.