An open source machine learning framework for efficient and transparent systematic reviews

To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool to accelerate the step of screening titles and abstracts. For many tasks—including but not limited to systematic reviews and meta-analyses—the scientific literature needs...

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Bibliographic Details
Published in:	Nature machine intelligence Vol. 3; no. 2; pp. 125 - 133
Main Authors:	van de Schoot, Rens, de Bruin, Jonathan, Schram, Raoul, Zahedi, Parisa, de Boer, Jan, Weijdema, Felix, Kramer, Bianca, Huijts, Martijn, Hoogerwerf, Maarten, Ferdinands, Gerbrich, Harkema, Albert, Willemsen, Joukje, Ma, Yongchao, Fang, Qixiang, Hindriks, Sybren, Tummers, Lars, Oberski, Daniel L.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 01.02.2021 Nature Publishing Group
Subjects:	631/114 631/326/596/4130 639/705/117 692/308 Active learning Algorithms Engineering Error analysis Freeware General Data Protection Regulation Literature reviews Machine learning Medical Subject Headings-MeSH Open source software Privacy Reviewing Software utilities Support vector machines User experience Qatar
ISSN:	2522-5839, 2522-5839
Online Access:	Get full text
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