Evidence Type Classification in Randomized Controlled Trials Article Swipe
Tobias Mayer
,
Elena Cabrio
,
Serena Villata
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.18653/v1/w18-5204
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.18653/v1/w18-5204
Randomized Controlled Trials (RCT) are a common type of experimental studies in the medical domain for evidence-based decision making. The ability to automatically extract the arguments proposed therein can be of valuable support for clinicians and practitioners in their daily evidence-based decision making activities. Given the peculiarity of the medical domain and the required level of detail, standard approaches to argument component detection in argument(ation) mining are not fine-grained enough to support such activities. In this paper, we introduce a new sub-task of the argument component identification task: evidence type classification. To address it, we propose a supervised approach and we test it on a set of RCT abstracts on different medical topics.
Related Topics
Concepts
Argument (complex analysis)
Randomized controlled trial
Computer science
Task (project management)
Set (abstract data type)
Domain (mathematical analysis)
Machine learning
Artificial intelligence
Component (thermodynamics)
Identification (biology)
Randomized experiment
Data mining
Medicine
Mathematics
Engineering
Statistics
Systems engineering
Thermodynamics
Mathematical analysis
Botany
Biology
Surgery
Programming language
Internal medicine
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/w18-5204
- https://www.aclweb.org/anthology/W18-5204.pdf
- OA Status
- gold
- Cited By
- 11
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2899033701
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2899033701Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18653/v1/w18-5204Digital Object Identifier
- Title
-
Evidence Type Classification in Randomized Controlled TrialsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2018Year of publication
- Publication date
-
2018-01-01Full publication date if available
- Authors
-
Tobias Mayer, Elena Cabrio, Serena VillataList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/w18-5204Publisher landing page
- PDF URL
-
https://www.aclweb.org/anthology/W18-5204.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.aclweb.org/anthology/W18-5204.pdfDirect OA link when available
- Concepts
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Argument (complex analysis), Randomized controlled trial, Computer science, Task (project management), Set (abstract data type), Domain (mathematical analysis), Machine learning, Artificial intelligence, Component (thermodynamics), Identification (biology), Randomized experiment, Data mining, Medicine, Mathematics, Engineering, Statistics, Systems engineering, Thermodynamics, Mathematical analysis, Botany, Biology, Surgery, Programming language, Internal medicine, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2023: 2, 2022: 2, 2021: 2, 2020: 2Per-year citation counts (last 5 years)
- References (count)
-
19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.supervised | 97 |
| abstract_inverted_index.activities. | 43, 73 |
| abstract_inverted_index.peculiarity | 46 |
| abstract_inverted_index.experimental | 9 |
| abstract_inverted_index.fine-grained | 68 |
| abstract_inverted_index.automatically | 22 |
| abstract_inverted_index.practitioners | 36 |
| abstract_inverted_index.evidence-based | 16, 40 |
| abstract_inverted_index.identification | 86 |
| abstract_inverted_index.argument(ation) | 64 |
| abstract_inverted_index.classification. | 90 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.800000011920929 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.64520667 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |