Developing Feature Types for Classifying Clinical Notes

نویسندگان

  • Jon D. Patrick
  • Yitao Zhang
  • Yefeng Wang
چکیده

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.

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تاریخ انتشار 2007