TY - GEN
T1 - DeSpin
T2 - 19th SIGBioMed Workshop on Biomedical Language Processing, BioNLP 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
AU - Koroleva, Anna
AU - Kamath, Sanjay
AU - Bossuyt, Patrick M.M.
AU - Paroubek, Patrick
N1 - Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 676207. Funding Information: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement No 676207. Publisher Copyright: © Association for Computation Linguistics.
PY - 2020
Y1 - 2020
N2 - Improving the quality of medical research reporting is crucial to reduce avoidable waste in research and to improve the quality of health care. Despite various initiatives aiming at improving research reporting - guidelines, checklists, authoring aids, peer review procedures, etc. - overinterpretation of research results, also known as distorted reporting or spin, is still a serious issue in research reporting. In this paper, we propose a Natural Language Processing (NLP) system for detecting several types of spin in biomedical articles reporting randomized controlled trials (RCTs). We use a combination of rule-based and machine learning approaches to extract important information on trial design and to detect potential spin. The proposed spin detection system includes algorithms for text structure analysis, sentence classification, entity and relation extraction, semantic similarity assessment. Our algorithms achieved operational performance for the these tasks, F-measure ranging from 79,42 to 97.86% for different tasks. The most difficult task is extracting reported outcomes. Our tool is intended to be used as a semiautomated aid tool for assisting both authors and peer reviewers to detect potential spin. The tool incorporates a simple interface that allows to run the algorithms and visualize their output. It can also be used for manual annotation and correction of the errors in the outputs. The proposed tool is the first tool for spin detection.
AB - Improving the quality of medical research reporting is crucial to reduce avoidable waste in research and to improve the quality of health care. Despite various initiatives aiming at improving research reporting - guidelines, checklists, authoring aids, peer review procedures, etc. - overinterpretation of research results, also known as distorted reporting or spin, is still a serious issue in research reporting. In this paper, we propose a Natural Language Processing (NLP) system for detecting several types of spin in biomedical articles reporting randomized controlled trials (RCTs). We use a combination of rule-based and machine learning approaches to extract important information on trial design and to detect potential spin. The proposed spin detection system includes algorithms for text structure analysis, sentence classification, entity and relation extraction, semantic similarity assessment. Our algorithms achieved operational performance for the these tasks, F-measure ranging from 79,42 to 97.86% for different tasks. The most difficult task is extracting reported outcomes. Our tool is intended to be used as a semiautomated aid tool for assisting both authors and peer reviewers to detect potential spin. The tool incorporates a simple interface that allows to run the algorithms and visualize their output. It can also be used for manual annotation and correction of the errors in the outputs. The proposed tool is the first tool for spin detection.
UR - http://www.scopus.com/inward/record.url?scp=85118107228&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 49
EP - 59
BT - BioNLP 2020 - 19th SIGBioMed Workshop on Biomedical Language Processing, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
Y2 - 9 July 2020
ER -