지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2012.1
- 수록면
- 18 - 28 (11page)
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초록· 키워드
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Objectives: Machine learning systems can considerably reduce the time and effort needed by experts to perform new systematic reviews (SRs). This study investigates categorization models, which are trained on a combination of included and commonly excluded articles, which can improve performance by identifying high quality articles for new procedures or drug SRs.
Methods: Test collections were built using the annotated reference files from 19 procedure and 15 drug systematic reviews.
The classification models, using a support vector machine, were trained by the combined even data of other topics, excepting the desired topic. This approach was compared to the combination of included and commonly excluded articles with the combination of included and excluded articles. Accuracy was used for the measure of comparison. Results: On average, the performance was improved by about 15% in the procedure topics and 11% in the drug topics when the classification models trained on the combination of articles included and commonly excluded, were used. The system using the combination of included and commonly excluded articles performed better than the combination of included and excluded articles in all of the procedure topics. Conclusions: Automatically rigorous article classification using machine learning can reduce the workload of experts when they perform systematic reviews when the topic-specific data are scarce. In particular, when the combination of included and commonly excluded articles is used, this system will be more effective.
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참고문헌 (18)
참고문헌 신청
/ / Committee for New Health Technology Assessment
Aphinyanaphongs Y. / 2005 / Text categorization models for highquality article retrieval in internal medicine / J Am Med Inform Assoc 12 : 207 ~ 216
Chalmers I. / 2009 / Avoidable waste in the production and reporting of research evidence / Lancet 374 : 86 ~ 89
Cohen AM / / Systematic drug class review gold standard data
Cohen AM. / 2006 / Reducing workload in systematic review preparation using automated citation classification / J Am Med Inform Assoc 13 : 206 ~ 219
Aphinyanaphongs Y. / 2005 / Text categorization models for highquality article retrieval in internal medicine / J Am Med Inform Assoc 12 : 207 ~ 216
Chalmers I. / 2009 / Avoidable waste in the production and reporting of research evidence / Lancet 374 : 86 ~ 89
Cohen AM / / Systematic drug class review gold standard data
Cohen AM. / 2006 / Reducing workload in systematic review preparation using automated citation classification / J Am Med Inform Assoc 13 : 206 ~ 219
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