AI Article Synopsis
- Identifying pediatric patients with newly diagnosed acute lymphoblastic leukemia using only diagnosis codes is difficult, so machine learning (ML) was leveraged to improve case identification.
- Nine different ML models were tested, with the best achieving a 97% positive predictive value (PPV) and 99% sensitivity during internal validation, and 94% PPV and 82% sensitivity in external validation.
- The successful ML model identified 21,044 patients, showcasing an effective method for assembling large cohorts from administrative data.
Article Abstract
Case identification in administrative databases is challenging as diagnosis codes alone are not adequate for case ascertainment. We utilized machine learning (ML) to efficiently identify pediatric patients with newly diagnosed acute lymphoblastic leukemia. We tested nine ML models and validated the best model internally and externally. The optimal model had 97% positive predictive value (PPV) and 99% sensitivity in internal validation; 94% PPV and 82% sensitivity in external validation. Our ML model identified a large cohort of 21,044 patients, demonstrating an efficient approach for cohort assembly and enhancing the usability of administrative data.
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| http://dx.doi.org/10.1002/pbc.30858 | DOI Listing |
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