Predicting Longitudinal Outcomes of Alzheimer's Disease via a Tensor-based Joint Classification and Regression Model
Lodewijk Brand, Kai Nichols, Hua Wang, Heng Huang, Li Shen
PSB - 2020
Alzheimer's disease (AD) is a serious neurodegenerative condition that affects millions of people across the world. Recently, machine learning models have been used to predict the progression of AD, although they frequently do not take advantage of the longitudinal and structural components associated with multimodal medical data. Here, we present a new multi-block alternating direction method of multipliers algorithm to optimize the proposed _Joint Multi-Modal Longitudinal Regression and Classification_ objective. In our approach, we combine multimodal longitudinal clinical data of various modalities to simultaneously predict the cognitive scores and diagnoses of participants in the Alzheimer's Disease Neuroimaging Initiative. Our algorithm is designed to leverage thestructure associated with clinical data that is not incorporated into standard machine learning optimization algorithms. The approach shows state-of-the-art predictive performance and validates a collection of brain and genetic biomarkers that have been recorded previously in AD literature. In addition, our algorithm identifies twenty-one genetic biomarkers that have not previously been reported.
Links
- View publications from Lodewijk Brand
- View publications from Hua Wang
- View publications presented in PSB
- View publications in the project, An Intelligence-Driven Patient Care Approach to Reduce Medical Errors
- View publications in the project, Intelligent Prediction of Traffic Conditions via Integrated Data-Driven Crowdsourcing and Learning
- View publications in the project, Mining Brain Imaging Genomics Data for Improved Cognitive Health
- View publications researching Longitudinal / Temporal Learning Models
- View publications researching Multi-Modal/View Data Fusion
- View publications researching Sparsity / Sparse Coding
- View publications applied to Bioinformatics
- View publications applied to Medical Image Computing
Cite this paper
MLA
Brand, Lodewijk, et al. "Predicting Longitudinal Outcomes of Alzheimer’s Disease via a Tensor-Based Joint Classification and Regression Model." Pac Symp Biocomput. 2020.
BibTeX
@inproceedings{brand2020predicting,
title={Predicting Longitudinal Outcomes of Alzheimer's Disease via a Tensor-Based Joint Classification and Regression Model},
author={Brand, Lodewijk and Nichols, Kai and Wang, Hua and Huang, Heng and Shen, Li},
booktitle={Pac Symp Biocomput},
pages={7--18},
year={2020},
organization={World Scientific}
}