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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.
- Source :
-
JCO clinical cancer informatics [JCO Clin Cancer Inform] 2024 Oct; Vol. 8, pp. e2400053. Date of Electronic Publication: 2024 Oct 04. - Publication Year :
- 2024
-
Abstract
- Purpose: The objective was to develop and evaluate the portability of a text mining algorithm for prospectively capturing disease progression in electronic health record (EHR) data of patients with metastatic non-small cell lung cancer (mNSCLC) treated with immunochemotherapy.<br />Methods: This study used EHR data from patients with mNSCLC receiving immunochemotherapy (between October 1, 2018, and December 31, 2022) in four Dutch hospitals. A text mining algorithm for capturing disease progression was developed in hospitals 1 and 2 and then transferred to hospitals 3 and 4 to evaluate portability. Performance metrics were calculated by comparing its outcomes with manual chart review. In addition, data were simulated to come available over time to assess performance in real-time applications. Median progression-free survival (PFS) was calculated using the Kaplan-Meier method to compare text mining with manual chart review.<br />Results: During development and portability, the text mining algorithm performed well in capturing disease progression, with all performance scores >90%. When real-time performance was simulated, the performance scores in all four hospitals exceeded 90% from week 15 after the start of follow-up. Although the exact progression dates varied in 46 patients of 157 patients with progressive disease, the number of patients labeled with progression too early (n = 24) and too late (n = 22) was well balanced with discrepancies ranging from -116 to 384 days. Nevertheless, the PFS curves constructed with text mining and manual chart review were highly similar for each hospital.<br />Conclusion: 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.
- Subjects :
- Humans
Male
Female
Aged
Middle Aged
Electronic Health Records
Data Mining methods
Algorithms
Lung Neoplasms pathology
Lung Neoplasms therapy
Lung Neoplasms mortality
Lung Neoplasms diagnosis
Disease Progression
Carcinoma, Non-Small-Cell Lung pathology
Carcinoma, Non-Small-Cell Lung mortality
Carcinoma, Non-Small-Cell Lung therapy
Carcinoma, Non-Small-Cell Lung diagnosis
Neoplasm Staging
Subjects
Details
- Language :
- English
- ISSN :
- 2473-4276
- Volume :
- 8
- Database :
- MEDLINE
- Journal :
- JCO clinical cancer informatics
- Publication Type :
- Academic Journal
- Accession number :
- 39365963
- Full Text :
- https://doi.org/10.1200/CCI.24.00053