Fair positive unlabeled learning for predicting undiagnosed Alzheimer’s disease in diverse electronic health records
Thai Tran, Mingzhou Fu, Jessica Fung, Sriram Sankararaman, David A. Elashoff, Keith Vossel, Timothy S. Chang · 2025
Read Fair positive unlabeled learning for predicting undiagnosed Alzheimer’s disease in diverse electronic health records by Thai Tran, Mingzhou Fu, Jessica Fung, Sriram Sankararaman, David A. Elashoff, Keith Vossel, Timothy S. Chang (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12661022.txt.
Description
Alzheimer’s Disease (AD), the most common neurodegenerative disease, is underdiagnosed and more prominent in underrepresented groups. We performed semi-supervised positive unlabeled learning (SSPUL) coupled with racial bias mitigation for equitable prediction of undiagnosed AD from diverse populations at UCLA Health using electronic health records. SSPUL achieved superior sensitivity (0.77–0.81) and area under the precision recall curve (AUCPR) (0.81–0.
Catalog record
| CREATOR | Thai Tran, Mingzhou Fu, Jessica Fung, Sriram Sankararaman, David A. Elashoff, Keith Vossel, Timothy S. Chang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 103 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |