Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations

Authors

  • Hoon Seo Colorado School of Mines
  • Lodewijk Brand Colorado School of Mines
  • Hua Wang Colorado School of Mines
  • Feiping Nie Northwestern Polytechnical University

Keywords:

Bioinformatics

Abstract

Alzheimer’s Disease (AD) is a chronic neurodegenerative disease that causes severe problems in patients’ thinking, memory, and behavior. An early diagnosis is crucial to prevent AD progression; to this end, many algorithmic approaches have recently been proposed to predict cognitive decline. However, these predictive models often fail to integrate heterogeneous genetic and neuroimaging biomarkers and struggle to handle missing data. In this work we propose a novel objective function and an associated optimization algorithm to identify cognitive decline related to AD. Our approach is designed to incorporate dynamic neuroimaging data by way of a participant-specific augmentation combined with multimodal data integration aligned via a regression task. Our approach, in order to incorporate additional side-information, utilizes structured regularization techniques popularized in recent AD literature. Armed with the fixed-length vector representation learned from the multimodal dynamic and static modalities, conventional machine learning methods can be used to predict the clinical outcomes associated with AD. Our experimental results show that the proposed augmentation model improves the prediction performance on cognitive assessment scores for a collection of popular machine learning algorithms. The results of our approach are interpreted to validate existing genetic and neuroimaging biomarkers that have been shown to be predictive of cognitive decline.

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Published

2021-05-18

How to Cite

Seo, H., Brand, L., Wang, H., & Nie, F. (2021). Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 522-530. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16130

Issue

Section

AAAI Technical Track on Application Domains