TAILIEUCHUNG - Báo cáo khoa học: "Annotating and Recognising Named Entities in Clinical Notes"

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 entities. . | Annotating and Recognising Named Entities in Clinical Notes Yefeng Wang School of Information Technology The University of Sydney Australia 2006 ywang1@ Abstract 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 entities. The information extraction task has initially used two approaches a rule based system and a machine learning system using Conditional Random Fields CRF . Different features are investigated to assess the interaction of feature sets and the supervised learning approaches to establish the combination best suited to this data set. The rule based and CRF systems achieved an F-score of and respectively. 1 Introduction A substantial amount of clinical data is locked away in a non-standardised form of clinical language which if standardised could be usefully mined to improve processes in the work of clinical wards and to gain greater understanding of patient care as well as the progression of diseases. However in some clinical contexts these clinical notes as written by a clinicians are in a less structured and often minimal grammatical form with idiosyncratic and cryptic shorthand. Whilst there is increasing interest in the automatic extraction of the contents of clinical text this particular type of notes cause significant difficulties for automatic extraction processes not present for well-written prose notes. The first step to the extraction of structured information from these clinical notes is to achieve accurate identification of clinical concepts or named entities. An entity may refer to a

• Báo cáo khoa học
• Annotating and Recognising Named Entities
• Clinical Notes
• Yefeng Wang
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình ngôn ngữ
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• Dịch máy
• Phương pháp dịch máy
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• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
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• CKY kết hợp xác suất
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