Classification of PICO elements by text features systematically extracted from PubMed abstracts

Ke Chun Huang, Charles Chih Ho Liu, Shung Shiang Yang, Furen Xiao, Jau Min Wong, Chun Chih Liao, I. Jen Chiang

研究成果: 書貢獻/報告類型會議貢獻

11 引文 斯高帕斯(Scopus)

摘要

We propose and evaluate a systematic approach to detect and classify Patient/Problem, Intervention, Comparison and Outcome (PICO) from the medical literature. The training and test corpora were generated systematically and automatically from structured PubMed abstracts. 23,472 sentences by exact pattern match of head words of P-I-O categories. Afterward, the terms with top frequencies were used as the features of Naïve Bayesian classifier. This approach achieves F-measure values of 0.91 for Patient/Problem, 0.75 for Intervention and 0.88 for Outcome, comparable to previous studied based on mixed textural, paragraphical, and semantic features. In conclusion, we show that by stricter pattern matching criteria of training set, detection and classification of PICO elements can be reproducible with minimal expert intervention. The results of this work are higher than previous studies.

原文英語
主出版物標題Proceedings - 2011 IEEE International Conference on Granular Computing, GrC 2011
頁面279-283
頁數5
DOIs
出版狀態已發佈 - 2011
事件2011 IEEE International Conference on Granular Computing, GrC 2011 - Kaohsiung, 臺灣
持續時間: 十一月 8 2011十一月 10 2011

其他

其他2011 IEEE International Conference on Granular Computing, GrC 2011
國家/地區臺灣
城市Kaohsiung
期間11/8/1111/10/11

ASJC Scopus subject areas

  • 軟體

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