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An Open Competition Involving Thousands of Competitors Failed to Construct Useful Abstract Classifiers for New Diagnostic Test Accuracy Systematic Reviews
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Kataoka, Yuki; Taito, Shunsuke; Yamamoto, Norio; So, Ryuhei; Tsutsumi, Yusuke; Anan, Keisuke; Banno, Masahiro; Tsujimoto, Yasushi; Wada, Yoshitaka; Sagami, Shintaro; Tsujimoto, Hiraku; Nihashi, Takashi; Takeuchi, Motoki; Terasawa, Teruhiko; Iguchi, Masahiro; Kumasawa, Junji; Ichikawa, Takumi; Furukawa, Ryuki; Yamabe, Jun; Furukawa, Toshi A. – Research Synthesis Methods, 2023
There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers for new DTA systematic reviews through an open competition. We prepared a dataset of abstracts…
Descriptors: Competition, Classification, Diagnostic Tests, Accuracy
Adjusting for Misclassification of an Exposure in an Individual Participant Data Meta-Analysis
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de Jong, Valentijn M. T.; Campbell, Harlan; Maxwell, Lauren; Jaenisch, Thomas; Gustafson, Paul; Debray, Thomas P. A. – Research Synthesis Methods, 2023
A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate…
Descriptors: Classification, Meta Analysis, Bayesian Statistics, Evaluation Methods
Machine Learning in Systematic Reviews: Comparing Automated Text Clustering with Lingo3G and Human Researcher Categorization in a Rapid Review
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Muller, Ashley Elizabeth; Ames, Heather Melanie R.; Jardim, Patricia Sofia Jacobsen; Rose, Christopher James – Research Synthesis Methods, 2022
Systematic reviews are resource-intensive. The machine learning tools being developed mostly focus on the study identification process, but tools to assist in analysis and categorization are also needed. One possibility is to use unsupervised automatic text clustering, in which each study is automatically assigned to one or more meaningful…
Descriptors: Artificial Intelligence, Man Machine Systems, Automation, Literature Reviews
A Rule-Based Approach for Automatically Extracting Data from Systematic Reviews and Their Updates to Model the Risk of Conclusion Change
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Bashir, Rabia; Dunn, Adam G.; Surian, Didi – Research Synthesis Methods, 2021
Few data-driven approaches are available to estimate the risk of conclusion change in systematic review updates. We developed a rule-based approach to automatically extract information from reviews and updates to be used as features for modelling conclusion change risk. Rules were developed to extract relevant information from published Cochrane…
Descriptors: Literature Reviews, Data, Automation, Statistical Analysis
Discriminating between Empirical Studies and Nonempirical Works Using Automated Text Classification
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Langlois, Alexis; Nie, Jian-Yun; Thomas, James; Hong, Quan Nha; Pluye, Pierre – Research Synthesis Methods, 2018
Objective: Identify the most performant automated text classification method (eg, algorithm) for differentiating empirical studies from nonempirical works in order to facilitate systematic mixed studies reviews. Methods: The algorithms were trained and validated with 8050 database records, which had previously been manually categorized as…
Descriptors: Mixed Methods Research, Databases, Information Retrieval, Search Strategies
Machine Learning for Identifying Randomized Controlled Trials: An Evaluation and Practitioner's Guide
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Marshall, Iain J.; Noel-Storr, Anna; Kuiper, Joël; Thomas, James; Wallace, Byron C. – Research Synthesis Methods, 2018
Machine learning (ML) algorithms have proven highly accurate for identifying Randomized Controlled Trials (RCTs) but are not used much in practice, in part because the best way to make use of the technology in a typical workflow is unclear. In this work, we evaluate ML models for RCT classification (support vector machines, convolutional neural…
Descriptors: Randomized Controlled Trials, Accuracy, Computer Software, Classification