A Pipeline for the Automatic Identification of Randomized Controlled Oncology Trials and Assignment of Tumor Entities Using Natural Language Processing
Classifying publications depending on whether they were randomized controlled oncology trials or not was feasible and enabled further processing using more specialized tools such as rule-based systems and potentially dedicated machine learning models.
Authors
- Fabio Dennstädt (ORCID: https://orcid.org/0000-0002-5374-8720)
- Carole Koechli (ORCID: https://orcid.org/0000-0001-9448-0786)
- Robert Foerster (ORCID: https://orcid.org/0000-0002-7664-9207)
- Daniel R. Zwahlen (ORCID: https://orcid.org/0000-0003-4359-7735)
- Christina Schröder (ORCID: https://orcid.org/0000-0002-4168-2795)
- Paul Windisch (ORCID: https://orcid.org/0000-0003-1040-4888)
- Daniel M. Aebersold (ORCID: https://orcid.org/0000-0002-9493-3834)
Institutions
- University of Bern (CH)
- University Hospital of Bern (CH)
- Kantonsspital Winterthur (CH)
Publication Details
- Journal
- Oncology
- Published
- 2025-06-13
- DOI
- https://doi.org/10.1159/000546970
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00