4.5 Article

Machine Learning Approach Predicts Probability of Time to Stage-Specific Conversion of Alzheimer's Disease

期刊

JOURNAL OF ALZHEIMERS DISEASE
卷 90, 期 2, 页码 891-903

出版社

IOS PRESS
DOI: 10.3233/JAD-220590

关键词

Analysis of variance; dementia; machine learning; survival analysis

资金

  1. NIH [R21AG070909, R56NS117587, R01HD101508, P30 AG072496]
  2. NIA/NIH [U24 AG072122]
  3. ARO [W911NF-17-1-0040]
  4. NIA [P30AG019610, P30 AG013846, P50 AG008702, P50 AG025688, P50 AG047266, P30 AG010133, P50 AG005146, P50 AG005134, P50 AG016574, P50 AG005138, P30 AG008051, P30 AG013854, P30 AG008017]
  5. 'NIA' [P30 AG010161, P50 AG047366, P30 AG010129, P50 AG016573, P50 AG005131, P50 AG023501, P30 AG035982, P30 AG028383, P30 AG053760, P30 AG010124, P50 AG005133, P50 AG005142, P30 AG012300, P30 AG049638, P50 AG005136, P50 AG033514, P50 AG005681, P50 AG047270]

向作者/读者索取更多资源

We established an algorithm using machine learning techniques to predict the probability of disease progression in Alzheimer's patients, allowing for personalized prevention and intervention strategies.
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.

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