Publication | Closed Access
A method of extracting the number of trial participants from abstracts describing randomized controlled trials
43
Citations
9
References
2008
Year
EngineeringControlled TrialsCorpus LinguisticsSocial SciencesText MiningNatural Language ProcessingGold StandardInformation RetrievalComputational LinguisticsDocument ClassificationRandomized Controlled TrialBiostatisticsBiomedical Text MiningStatisticsMedical StatisticMeta-analysisQuestion AnsweringNlp TaskAutomatic ExtractionInformation ExtractionTrial ParticipantsClinical Trial EvaluationClinical Trial Design
We have developed a method for extracting the number of trial participants from abstracts describing randomized controlled trials (RCTs); the number of trial participants may be an indication of the reliability of the trial. The method depends on statistical natural language processing. The number of interest was determined by a binary supervised classification based on a support vector machine algorithm. The method was trialled on 223 abstracts in which the number of trial participants was identified manually to act as a gold standard. Automatic extraction resulted in 2 false-positive and 19 false-negative classifications. The algorithm was capable of extracting the number of trial participants with an accuracy of 97% and an F-measure of 0.84. The algorithm may improve the selection of relevant articles in regard to question-answering, and hence may assist in decision-making.
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