Machine Learning Approach Predicts Probability of Time to Stage-Specific Conversion of Alzheimer’s Disease
Year: 2022
Type: article
Abstract: BACKGROUND: The progression of Alzheimer's disease (AD) varies in different patients at different stages, which makes predicting the time of disease conversions challenging. OBJECTIVE: We established an algorithm by leveraging machine learning techniques to predict the probability of the conversion time to next stage for different subjects during a given period. METHODS: Firstly, we used Kaplan-Meier (KM) estimation to get the transition curves of different AD stages, and calculated Log-rank statistics to test whether the progression rate between different stages was identical. This quantitatively confirmed the progression rates known in the literature. Then, we developed an approach based on deep learning model, DeepSurv, to predict the probabilities of time-to-conversion. Finally, to help interpret the deep learning model in our approach, we identified important variables contributing the most to the DeepSurv prediction, whose significance were validated with the analysis of variance (ANOVA). RESULTS: Our machine learning approach predicted the time to conversion with a high accuracy. For each of the different stages, the concordance index (CI) of our approach was at least 86%, and the integrated Brier score (IBS) was less than 0.1. To facilitate interpretability of the prediction results, our approach identified the top 10 variables for each disease conversion scenario, which were clinicopathologically meaningful, and most of them were also statistically significant. CONCLUSION: Our study has the potential to provide individualized prediction for future time course of AD conversions years before their actual occurrence, thus facilitating personalized prevention and intervention strategies to slow down the progression of AD.
Source: Journal of Alzheimer s Disease
DOI: 10.3233/jad-220590
Institutions: University of Kentucky, Qingdao University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Michigan Alzheimer's Disease Core Center, UC Davis Alzheimer's Disease Core Center, Indiana Alzheimer Disease Center, G1565341982, Wake Forest Alzheimer's Disease Core Center, Continuous and Longitudinal Monitoring of Cerebral Blood Flow and Metabolism in Freely Moving Rodents, University of Kentucky Alzheimer's Disease Core Center, Neurobiology of Alzheimer's Disease and Aging, Alzheimer's Disease Research Center, UCSD Alzheimer's Disease Research Center P50, Alzheimer's Disease Research Center, Alzheimer's Disease Core Center, The Alzheimer's Disease Research Center at the University of California, Irvine, A Resource for Biomedical Mass Spectrometry, Stanford Alzheimer's Disease Research Center, Oregon Alzheimer's Disease Center, Rush Alzheimer's Disease Core Center, Alzheimer's Disease Core Center, Alzheimer's Disease Research Center - Overall, Wisconsin Alzheimer's Disease Research Center, Gene Profile Analysis of Associations between Alzheimer’s Disease and Circadian Rhythmic Patterns of Human Brain Regions Using Machine Learning, University of Kansas Alzheimer's Disease Core Centers, Alzheimer's Disease Research Center, Alzheimer Disease Research Center, Yale Alzheimer Disease Research Center, Arizona Alzheimer's Disease Core Center, Johns Hopkins Alzheimer's Disease Research Center, Noninvasive Noncontact High-Density Optical Imaging of Neonatal Intraventricular Hemorrhage, Boston University Alzheimer's Disease Center, Alzheimer's Disease Research Center, Massachusetts Alzheimer's Disease Research Center, Alzheimer's Disease Core Center, Emory Alzheimer's Disease Center, University of Florida - Mt. Sinai Medical Center AD Research Center, G8375851502, National Alzheimer's Coordinating Center, University of Kentucky Alzheimer's Disease Research Center, Alzheimer's Disease Research Center, New Approaches to Dementia Heterogeneity, Mayo Alzheimer's Disease Research Center
OpenAlex ID: W4298137859