The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
Deep survival modeling to predict future cognitive impairment in unimpaired adults.
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Imms Phoebe, Wang Haoqing, Bhattacharya Samayan, Chaudhari Nikhil N, Vega Owen M, Solis Galvan Jorge A, Chen Siyu, Li Ruixi, Irimia Andrei
Аннотация
BACKGROUND: Predicting Alzheimer's disease (AD)-related cognitive impairment (CI) among cognitively normal (CN) adults enables meaningful disease modification through early intervention and enrichment of clinical trials.
METHODS: A deep survival model is trained to predict CI conversion risk in 1415 CN adults from the National Alzheimer's Coordinating Center. Converters' (N = 212) and non-converters' (N = 1203) baseline clinical measures and magnetic resonance images are used to estimate their conversion probability up to 22 years after baseline observation.
RESULTS: After 20-fold cross-validation, the model predicts conversion probability with a c-index of 0.88, classification accuracy of 75%, and AUC ROC of 0.89, outperforming previous machine learning models.
CONCLUSIONS: This is one of few studies on the important challenge of predicting future CI among unimpaired subjects. Deep survival modeling can improve the identification of preclinical AD and suggests that uncertainty in AD risk estimation is due to potentially modifiable lifestyle factors.
METHODS: A deep survival model is trained to predict CI conversion risk in 1415 CN adults from the National Alzheimer's Coordinating Center. Converters' (N = 212) and non-converters' (N = 1203) baseline clinical measures and magnetic resonance images are used to estimate their conversion probability up to 22 years after baseline observation.
RESULTS: After 20-fold cross-validation, the model predicts conversion probability with a c-index of 0.88, classification accuracy of 75%, and AUC ROC of 0.89, outperforming previous machine learning models.
CONCLUSIONS: This is one of few studies on the important challenge of predicting future CI among unimpaired subjects. Deep survival modeling can improve the identification of preclinical AD and suggests that uncertainty in AD risk estimation is due to potentially modifiable lifestyle factors.