Year: 2024
Type: conference-paper
Abstract: Alzheimer’s disease (AD) is a progressive and irreversible brain disorder that unfolds over the course of 30 years. Therefore, it is critical to capture the disease progression in an early stage such that intervention can be applied before the onset of symptoms. Machine learning (ML) models have been shown effective in predicting the onset of AD. Yet for subjects with follow-up visits, existing techniques for AD classification only aim for accurate group assignment, where the monotonically increasing risk across follow-up visits is usually ignored. Resulted fluctuating risk scores across visits violate the irreversibility of AD, hampering the trustworthiness of models and also providing little value to understanding the disease progression. To address this issue, we propose a novel regularization approach to predict AD longitudinally. Our technique aims to maintain the expected monotonicity of increasing disease risk during progression while preserving expressiveness. Specifically, we introduce a monotonicity constraint that encourages the model to predict disease risk in a consistent and ordered manner across follow-up visits. We evaluate our method using the longitudinal structural MRI and amyloid-PET imaging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our model outperforms existing techniques in capturing the progressiveness of disease risk, and at the same time preserves prediction accuracy.
Institutions: Purdue University West Lafayette, Indiana University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: CAREER: Computational strategies for incompleteness and heterogeneity in multi-omic data, CAREER: Advancing Fair Data Mining via New Robust and Explainable Algorithms and Human-Centered Approaches, Informatics Algorithms for Genomic Analysis of Brain Imaging Data, Integrative Predictive Modeling of Alzheimer's Disease, III: Medium: Collaborative Research: Integrating Large-Scale Machine Learning and Edge Computing for Collaborative Autonomous Vehicles, Centrally-linked longitudinal peripheral biomarkers of AD in multi-ethnic populations, Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
OpenAlex ID: W4401749581