Annotating and Recognising Named Entities in Clinical Notes

  title={Annotating and Recognising Named Entities in Clinical Notes},
  author={Yefeng Wang},
This paper presents ongoing research in clinical information extraction. This work introduces a new genre of text which are not well-written, noise prone, ungrammatical and with much cryptic content. A corpus of clinical progress notes drawn form an Intensive Care Service has been manually annotated with more than 15000 clinical named entities in 11 entity types. This paper reports on the challenges involved in creating the annotation schema, and recognising and annotating clinical named… CONTINUE READING
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