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Exploiting task relationships for Alzheimer's disease cognitive score prediction via multi-task learning.

Wei Liang1, Kai Zhang1, Peng Cao2

  • 1Computer Science and Engineering, Northeastern University, Shenyang, China.

Computers in Biology and Medicine
|December 14, 2022
PubMed
Summary

This study introduces a novel Bi-Graph guided self-Paced Multi-Task Feature Learning (BGP-MTFL) framework to enhance Alzheimer's disease cognitive score prediction. The BGP-MTFL model significantly improves prediction accuracy and identifies stable biomarkers.

Keywords:
Alzheimer’s diseaseBiomarker identificationFeature selectionMulti-task learningSparse learning

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Data Analysis

Background:

  • Alzheimer's disease (AD) is a leading cause of dementia in the elderly.
  • Current multi-task learning (MTL) approaches for AD prediction have limitations in modeling task relationships and feature structures.
  • A unified framework is needed to address task correlation, feature structure exploitation, and automatic task weighting.

Purpose of the Study:

  • To develop an advanced framework, Bi-Graph guided self-Paced Multi-Task Feature Learning (BGP-MTFL), for improved Alzheimer's disease cognitive score prediction.
  • To effectively model inherent task relationships and exploit feature structures within a unified framework.
  • To automatically determine task weights for enhanced learning performance.

Main Methods:

  • The BGP-MTFL framework incorporates two correlation regularization techniques for features and tasks.
  • It utilizes ℓ2,1 regularization and a self-paced learning scheme.
  • An efficient optimization method combining ADMM and APG is employed to solve the objective function.

Main Results:

  • The BGP-MTFL model achieved a normalized Mean Squared Error (nMSE) of 3.923 and a weighted R-value (wR) of 0.416 for predicting eighteen cognitive scores.
  • Evaluated on Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets, the model demonstrated superior performance compared to state-of-the-art methods.
  • The framework successfully identified more stable biomarkers associated with Alzheimer's disease.

Conclusions:

  • The proposed BGP-MTFL framework offers a significant advancement in Alzheimer's disease cognitive score prediction.
  • It effectively addresses key challenges in multi-task learning for complex neurological disorders.
  • The findings suggest BGP-MTFL's potential for clinical application in identifying AD biomarkers and improving diagnostic accuracy.