Using mixture cure models to address algorithmic bias in diagnostic timing: autism as a test case
Mixture cure models offer a promising tool to enhance accuracy and fairness in predictive modeling, especially when the outcome of interest is not uniformly observed across groups.
Authors
- Matthew Engelhard (ORCID: https://orcid.org/0000-0003-4112-9639)
- Benjamin Alan Goldstein (ORCID: https://orcid.org/0000-0001-5261-3632)
- Géraldine Dawson (ORCID: https://orcid.org/0000-0003-1410-2764)
- Naomi Ornstein Davis (ORCID: https://orcid.org/0000-0003-0858-7152)
- Peng Wu (ORCID: https://orcid.org/0009-0002-5933-7065)
Institutions
- Duke University (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-11-03
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf148
- Primary Topic
- Generative Adversarial Networks and Image Synthesis
- Type
- article
- Field-Weighted Citation Impact
- 0.00