Development and Portability of a Text Mining Algorithm for Capturing Disease Progression in Electronic Health Records of Patients With Stage IV Non–Small Cell Lung Cancer
In this study, an accurate text mining algorithm for capturing disease progression in the EHR data of patients with mNSCLC was developed. The algorithm was portable across different hospitals, and the performance over time was good, making this an interesting approach for prospective follow-up of multicenter cohorts.
Authors
- J. Zwaveling (ORCID: https://orcid.org/0000-0002-6080-6183)
- E.M.W. van de Garde (ORCID: https://orcid.org/0000-0002-1334-2144)
- Hanieh Abedian Kalkhoran (ORCID: https://orcid.org/0000-0002-6279-6310)
- Marjon V. Verschueren (ORCID: https://orcid.org/0000-0002-2448-9168)
- Lourens T. Bloem (ORCID: https://orcid.org/0000-0002-0014-8625)
- Loes E. Visser (ORCID: https://orcid.org/0000-0002-1421-6246)
- Bas Peters (ORCID: https://orcid.org/0009-0004-1321-3969)
- Maarten J. Deenen (ORCID: https://orcid.org/0000-0002-4389-6420)
- Ben E.E.M. van den Borne
Institutions
- Radboud University Nijmegen (NL)
- Utrecht University (NL)
- Leiden University Medical Center (NL)
- Catharina Ziekenhuis (NL)
- St. Antonius Ziekenhuis (NL)
- Haga Hospital (NL)
- Erasmus University Rotterdam (NL)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1200/cci.24.00053
- Primary Topic
- Lung Cancer Treatments and Mutations
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- AstraZeneca
- Roche Nederland