Clinical text mining of the performance status and progression-free survival to facilitate data collection in cancer research: an exploratory study
The developed TM approach is able to extract PS and PFS from the EMR with a very good performance. Therefore, this approach increases the efficiency of reliable data collection from EMRs, facilitating the use of real-world data (RWD) in clinical research.
Authors
- Alwin D. R. Huitema (ORCID: https://orcid.org/0000-0003-1939-4639)
- Adrianus J. de Langen (ORCID: https://orcid.org/0000-0001-7343-633X)
- Liang‐Ting Lin (ORCID: https://orcid.org/0000-0003-2715-9567)
- Michael Hout (ORCID: https://orcid.org/0000-0003-3634-7498)
- Lodewijk F.A. Wessels
- J. H. Beijnen
Institutions
- Utrecht University (NL)
- University Medical Center Utrecht (NL)
- Oncode Institute (NL)
- Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital (NL)
- Princess Máxima Center (NL)
- Delft University of Technology (NL)
Publication Details
- Journal
- ESMO Real World Data and Digital Oncology
- Published
- 2024-08-13
- DOI
- https://doi.org/10.1016/j.esmorw.2024.100059
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00