Affiliations: [a] Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA
| [b] Department of Computer Science and Engineering, Qingdao University, Shandong, China
| [c] Department of Pathology, Sanders Brown Center for Dementia, University of Kentucky, Lexington, KY, USA
Correspondence:
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Correspondence to: Qiang Cheng, Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA. Tel.: +1 859 323 3162; Fax: +1 859 257 0483; E-mail:qiang.cheng@uky.edu.
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.
Keywords: Analysis of variance, dementia, machine learning, survival analysis
