Randomized Multi-task Feature Learning Approach for Modelling and Predicting Alzheimer’s Disease Progression

Xulong Wang, Yu Zhang, Menghui Zhou, Tong Liu, Zhipeng Yuan, Xiyang Peng, Kang Liu, Jun Qi, Po Yang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

Abstract

Multi-task feature learning (MTFL) methods play a key role in predicting Alzheimer’s disease (AD) progression. These studies adhere to a unified feature-sharing framework to promote information exchange on relevant disease progression tasks. MTFL not only utilise the inherent properties of tasks to enhance prediction performance, but also yields weights that are capable to indicate nuanced changes of related AD biomarkers. Task regularized priors, however, introduced by MTFL lead to uncertainty in biomarkers selection, particularly amidst a plethora of highly interrelated biomarkers in a high dimensional space. There is little attention on studying how to design feasible experimental protocols for assessment of MTFL models. To narrow this knowledge gap, we proposed a Randomize Multi-task Feature Learning (RMFL) approach to effectively model and predict AD progression. As task increases, the results show that the RMFL is not only stable and interpretable, but also reduced by 0.2 in normalized mean square error compared to single-task models like Lasso, Ridge. Our method is also adaptable as a general regression framework to predict other chronic disease progression.

Original languageEnglish
Title of host publicationInternet of Things of Big Data for Healthcare - 5th International Workshop, IoTBDH 2023, Proceedings
EditorsJun Qi, Po Yang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages52-68
Number of pages17
ISBN (Print)9783031522154
DOIs
Publication statusPublished - 2024
Event5th International Workshop on Internet of Things of Big Data for Healthcare, IoTBDH 2023 - Birmingham, United Kingdom
Duration: 21 Oct 202325 Oct 2023

Publication series

NameCommunications in Computer and Information Science
Volume2019 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference5th International Workshop on Internet of Things of Big Data for Healthcare, IoTBDH 2023
Country/TerritoryUnited Kingdom
CityBirmingham
Period21/10/2325/10/23

Keywords

  • Alzheimer’s disease
  • Multi-task feature learning
  • Randomization
  • Stability selection

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