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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Joint Robust Imputation and Classification for Early Dementia Detection Using Incomplete Multi-modality Data.

Kim-Han Thung¹, Pew-Thian Yap¹, Dinggang Shen¹

¹Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.

Predictive Intelligence in Medicine. PRIME (Workshop)

|August 27, 2021

Summary

This summary is machine-generated.

Identifying individuals with Mild Cognitive Impairment (MCI) who will develop Alzheimer's Disease (AD) is crucial for early intervention. Our unified framework effectively handles incomplete multi-modality data for improved prediction accuracy.

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Area of Science:

Neuroscience
Biomedical Informatics
Machine Learning

Background:

Early identification of Mild Cognitive Impairment (MCI) progression to Alzheimer's Disease (AD) is critical for timely therapeutic intervention.
Multi-modality data can enhance prediction models, but incompleteness poses a significant challenge.
Traditional two-step approaches (imputation then classification) can propagate imputation errors, leading to suboptimal classifier performance.

Purpose of the Study:

To develop a unified framework for jointly performing feature selection, data denoising, missing value imputation, and classifier learning.
To address the limitations of existing methods in handling incomplete multi-modality data for MCI to AD prediction.

Main Methods:

A novel framework integrating feature selection, data denoising, and missing value imputation.
Utilizing a low-rank constraint for simultaneous imputation and denoising.
Employing a regression model for feature selection and classification, with iterative optimization via Alternating Direction Method of Multipliers (ADMM).

Main Results:

The proposed unified framework effectively handles incomplete multi-modality data.
Joint optimization of feature selection, denoising, and imputation improves prediction accuracy.
Experimental results on the ADNI dataset demonstrate superior performance compared to existing methods.

Conclusions:

The unified framework offers a robust solution for predicting Alzheimer's Disease progression from incomplete multi-modality data.
This approach mitigates error propagation from imputation, leading to more reliable classification.
The method holds promise for improving early diagnosis and treatment strategies for Alzheimer's Disease.