Metastatic Versus Localized Disease as Inclusion Criteria That Can Be Automatically Extracted From Randomized Controlled Trials Using Natural Language Processing
Automatic classification of cancer trials with regard to the inclusion of patients with localized and/or metastatic disease is feasible. Turning the extraction of trial criteria into classification problems could, in selected cases, improve text-mining approaches in evidence-based medicine. Increasingly large language models can reduce or eliminate the need for previous training on the task at the expense of increased computational power and, in turn, cost.
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
- JCO Clinical Cancer Informatics
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1200/cci-24-00150
- Citations
- 3
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 1.04