Abstract
Background: Manual data collection is still the gold standard for disease-specific patient registries. However, CAPRI-3 uses text mining (an artificial intelligence (AI) technology) for patient identification and data collection. The aim of this study is to demonstrate the reliability and efficiency of this AI-driven approach. Methods: CAPRI-3 is an observational retrospective multicenter cohort registry on metastatic prostate cancer. We tested the patient-identification algorithm and automated data extraction through manual validation of the same patients in two pilots in 2019 and 2022. Results: Pilot one identified 2030 patients and pilot two 9464 patients. The negative predictive value of the algorithm was maximized to prevent false exclusions and reached 94.8%. The completeness and accuracy of the automated data extraction were 92.3% or higher, except for date fields and inaccessible data (images/pdf) (10–88.9%). Additional manual quality control took over 3 h less time per patient than the original fully manual CAPRI registry (105 vs. 300 min). Conclusions: The CAPRI-3 patient-identification algorithm is a sound replacement for excluding ineligible candidates. The AI-driven data extraction is largely accurate and complete, but manual quality control is needed for less reliable and inaccessible data. Overall, the AI-driven approach of the CAPRI-3 registry is reliable and timesaving.
| Original language | English |
|---|---|
| Article number | 3808 |
| Journal | Cancers |
| Volume | 15 |
| Issue number | 15 |
| DOIs | |
| Publication status | Published - Aug 2023 |
Bibliographical note
Funding: This research was funded by the CAPRI foundation, registered at the Dutch Chamber of Commerce in December 2019. This nonprofit organization aims to serve as the data source on metastatic prostate cancer in the Netherlands. The foundation is sponsored by the following pharmaceutical companies: AAA Novartis, Astellas, Bayer, and MSD/AstraZeneca. The pharmaceutical companies have no role in the design and conduct of the study, collection, management, analysis, interpretation of data, and preparation, review, or approval of the manuscript. The APC was funded by Radboud University Medical Centre.Publisher Copyright: © 2023 by the authors.
