Earticle

초록

영어

For many years natural language processing (NLP) programming tools have been used to process information in various applications areas including medicine. However, most of such systems have been developed by expert programmers and very little or none by clinicians. The subject under consideration in this article is automatic categorization of clinical data. This topic requires great deal of clinical cognition and hence there is a need to let clinicians develop such systems. This article is an attempt in this direction where the RapidMiner environment has been used for this purpose. This article describes how RapidMiner as a visual programming environment can be used for tokenization and categorization of clinical narratives. It also describes how to select the best classifier for categorization. K-NN classifier categorizes clinical narratives with high performance accuracies even for large dataset like the i2b2 smoking challenge data.

목차

Abstract
 1. Introduction
 2. RapidMiner for Tokenization and Categorization
 3. Categorizing Clinical Narratives
 4. Validating the Categorization Abilility of the K-NN
 5. Discussion and Conclusion
 Acknowledgements
 References

키워드

저자

• Osama Mohammed [ SimBioSys Laboratory, University of Victoria, 3800 Finnerty Road, Victoria, British Columbia V8W 3P6, Canada ]
• Sabah Mohammed [ Department of Computer Science, Lakehead University, Thunder Bay, Ontario P7b 5E1, Canada ]
• Jinan Fiaidhi [ Department of Computer Science, Lakehead University, Thunder Bay, Ontario P7b 5E1, Canada ]
• Simon Fong [ Department of Computer Science, Lakehead University, Thunder Bay, Ontario P7b 5E1, Canada ]
• Tia-hoon Kim [ Department of Computer and Information Science University of Macau, Av. Padre Tomas Pereira, Taipa, Macau ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605