Learning Deeply Enriched Representations of Longitudinal Imaging-Genetic Data to Predict Alzheimer's Disease Progression

Hoon Seo, Hua Wang

BIBM - 2021

Alzheimer's Disease (AD) is a progressive memory disorder that causes irreversible cognitive declines, therefore early diagnosis is imperative to prevent the progression of AD. To this end, many biomarker analysis models have been presented for early AD detection. However, these models may not realize the full data potential due to their failure to integrate longitudinal (dynamic) phenotypic data with (static) genetic data. Sometimes, they may not fully utilize both labeled and unlabeled samples either. To overcome these limitations, we propose a semi-supervised enrichment learning method to learn a fixed-length vectorial representation for each participant, by which the static data record can be integrated with the dynamic data records. We have applied our new method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort and achieved 75% accuracy on multiclass AD progression prediction by one year in advance.