Reducing systematic review workload through certainty-based screening
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In this paper, the authors applied active learning with two criteria (certainty and uncertainty) and several enhancements in both clinical medicine and social science (specifically, public health) areas, and compared the results in both.About:
This article is published in Journal of Biomedical Informatics.The article was published on 2014-10-01 and is currently open access. It has received 141 citations till now. The article focuses on the topics: Active learning (machine learning) & Latent Dirichlet allocation.read more
Citations
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Using text mining for study identification in systematic reviews: a systematic review of current approaches
TL;DR: Using text mining to prioritise the order in which items are screened should be considered safe and ready for use in ‘live’ reviews, and the use of text mining as a ‘second screener’ may also be used cautiously.
Journal ArticleDOI
Searching for studies: a guide to information retrieval for Campbell systematic reviews
Shannon Kugley,Anne Wade,James Thomas,Quenby Mahood,Anne-Marie Klint Jørgensen,Karianne Thune Hammerstrøm,Nila A Sathe +6 more
TL;DR: Report abstracts can be used to eliminate clearly irrelevant reports, obviating the need to obtain the full text of those reports or to return to the bibliographic database at a later time.
Journal ArticleDOI
Automating data extraction in systematic reviews: a systematic review
TL;DR: A systematic review of published and unpublished methods to automate data extraction for systematic reviews found no unified information extraction framework tailored to the systematic review process and published reports focused on a limited number of data elements.
Journal ArticleDOI
An open source machine learning framework for efficient and transparent systematic reviews
Rens van de Schoot,Jonathan de Bruin,Raoul Schram,Parisa Zahedi,Jan de Boer,Felix Weijdema,Bianca Kramer,Martijn Huijts,Maarten Hoogerwerf,Gerbrich Ferdinands,Albert Harkema,Joukje Willemsen,Yongchao Ma,Qixiang Fang,Sybren Hindriks,Lars Tummers,Daniel L. Oberski +16 more
TL;DR: An open source machine learning-aided pipeline applying active learning: ASReview is developed and it is demonstrated by means of simulation studies that active learning can yield far more efficient reviewing than manual reviewing while providing high quality.
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Decision aids to help older people make health decisions: a systematic review and meta-analysis
Julia C.M. van Weert,Barbara C. van Munster,Barbara C. van Munster,Remco Sanders,René Spijker,René Spijker,Lotty Hooft,Jesse Jansen +7 more
TL;DR: This review shows promising results on the effectiveness of decision aids for older adults, which improve older adults’ knowledge, increase their risk perception, decrease decisional conflict and seem to enhance participation in SDM.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Cochrane Handbook for Systematic Reviews of Interventions, Version 5.1.0. The Cochrane Collaboration
Journal Article
LIBLINEAR: A Library for Large Linear Classification
TL;DR: LIBLINEAR is an open source library for large-scale linear classification that supports logistic regression and linear support vector machines and provides easy-to-use command-line tools and library calls for users and developers.