Deep survival modelling to predict future cognitive impairment in unimpaired adults.
Authors
Affiliations (7)
Affiliations (7)
- Leonard Davis School of Gerontology, University of Southern California, Mailing address: 3715 McClintock Ave, Los Angeles, 90089, CA.
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- The School of Biomedical Sciences, The University of Queensland.
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California. Mailing address, 1042 Downey Way, Los Angeles, 90089, CA.
- Center for Statistical Genetics, The Gertrude H. Sergievsky Center, Columbia University, New York, NY, Mailing address: 630 W 168th St, New York, 10032, NY.
- Department of Quantitative & Computational Biology, Dana and David Dornsife College of Arts & Sciences, University of Southern California, Mailing address: 3620 S Vermont Ave, Los Angeles, 90089, CA.
- Centre for Healthy Brain Aging, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, King's College London, London, United Kingdom.
Abstract
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. A deep survival model is trained to predict CI conversion risk in 1,415 CN adults from the National Alzheimer's Coordinating Center. Converters' (N = 212) and non-converters' (N = 1,203) baseline clinical measures and magnetic resonance images are used to estimate their conversion probability up to 22 years after baseline observation. After 20-fold cross-validation, the model predicts conversion probability with a c-index of 0.88, and classification accuracy of 75% and AUC ROC of 0.89, outperforming previous machine learning models. This is one of few studies on the important challenge of predicting future CI among unimpaired subjects. Deep survival modelling can improve the identification of preclinical AD and suggests that uncertainty in AD risk estimation is due to potentially modifiable lifestyle factors.