Open Source Software for Efficient and Transparent 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
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TLDR
An open source machine learning-aided pipeline applying active learning: ASReview is developed and it is demonstrated by means of simulation studies that ASReview can yield far more efficient reviewing than manual reviewing, while providing high quality.Abstract:
To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool (ASReview) 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 to be checked systematically. Currently, scholars and practitioners screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that ASReview can yield far more efficient reviewing than manual reviewing, while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice.read more
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Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World
Farshad Firouzi,Bahar Farahani,Mahmoud Daneshmand,Kathy Grise,JaeSeung Song,Roberto Saracco,Lucy Lu Wang,Kyle Lo,Plamen Angelov,Eduardo Soares,Po-Shen Loh,Zeynab Talebpour,Reza Moradi,Mohsen Goodarzi,Haleh Ashraf,Mohammad Talebpour,Alireza Talebpour,Luca Romeo,Rupam Das,Hadi Heidari,Dana K. Pasquale,James Moody,Christopher W. Woods,Erich Huang,Payam Barnaghi,Majid Sarrafzadeh,Ron C. Li,Kristen L. Beck,Olexandr Isayev,Nak-Myoung Sung,Alan Luo +30 more
TL;DR: Several complementary and multidisciplinary techniques to combat COVID-19 are proposed, which offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research.
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Blockchain for Public Services: A Systematic Literature Review
TL;DR: In this paper, the authors provide a systematic literature review of the use of blockchain across all the main public services and highlight the main potential benefits, costs and risks of blockchain for government, civil servants and citizens.
Journal ArticleDOI
Artificial intelligence and the conduct of literature reviews
TL;DR: In this context, literature reviews stand out because they operate on large and rapidly growing data sets as discussed by the authors, which is not the case in many traditional research practices in many areas of AI.
Journal ArticleDOI
Targeted optical fluorescence imaging: a meta-narrative review and future perspectives.
H. M. Schouw,L. A. Huisman,Y. F. Janssen,Riemer H. J. A. Slart,Riemer H. J. A. Slart,Ronald Borra,Antoon T. M. Willemsen,Adrienne H. Brouwers,J.M. van Dijl,Rudi Dierckx,Rudi Dierckx,G. M. van Dam,Wiktor Szymanski,Hendrikus H. Boersma,Schelto Kruijff +14 more
TL;DR: In this article, a review of the current status of targeted optical fluorescence imaging in the field of oncology, cardiovascular, infectious and inflammatory diseases to further promote clinical translation is presented.
Journal ArticleDOI
Machine-Learning Microstructure for Inverse Material Design
Zongrui Pei,Kyle A. Rozman,Ömer N. Doğan,You-Hai Wen,Nan Gao,Elizabeth A. Holm,Jeffrey A. Hawk,David E. Alman,Michael C. Gao +8 more
TL;DR: In this article, a neural network method is proposed for the inverse design of alloys with 20 components, which can accelerate the design process based on microstructure images with extremely similar features.
References
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Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
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Posted Content
Scikit-learn: Machine Learning in Python
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TL;DR: An Explanation and Elaboration of the PRISMA Statement is presented and updated guidelines for the reporting of systematic reviews and meta-analyses are presented.
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