Predicting Longitudinal Outcomes of Alzheimer's Disease via a Tensor-Based Joint Classification and Regression Model

Alzheimer’s Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

Abstract

Alzheimer's disease (AD) is a serious neurodegenerative condition that affects millions of people across the world. Recently machine learning models have been used to predict the progression of AD, although they frequently do not take advantage of the longitudinal and structural components associated with multi-modal medical data. To address this, we present a new algorithm that uses the multi-block alternating direction method of multipliers to optimize a novel objective that combines multi-modal longitudinal clinical data of various modalities to simultaneously predict the cognitive scores and diagnoses of the participants in the Alzheimer's Disease Neuroimaging Initiative cohort. Our new model is designed to leverage the structure associated with clinical data that is not incorporated into standard machine learning optimization algorithms. This new approach shows state-of-the-art predictive performance and validates a collection of brain and genetic biomarkers that have been recorded previously in AD literature.

Original languageEnglish (US)
Pages (from-to)7-18
Number of pages12
JournalPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Volume25
StatePublished - Jan 1 2020

Fingerprint

Tensors
Alzheimer Disease
Joints
Learning systems
Neuroimaging
Biomarkers
Brain
Machine Learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

Cite this

Predicting Longitudinal Outcomes of Alzheimer's Disease via a Tensor-Based Joint Classification and Regression Model. / Alzheimer’s Disease Neuroimaging Initiative.

In: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, Vol. 25, 01.01.2020, p. 7-18.

Research output: Contribution to journalArticle

@article{d34c3d8420704d6ca5443e8612475e8c,
title = "Predicting Longitudinal Outcomes of Alzheimer's Disease via a Tensor-Based Joint Classification and Regression Model",
abstract = "Alzheimer's disease (AD) is a serious neurodegenerative condition that affects millions of people across the world. Recently machine learning models have been used to predict the progression of AD, although they frequently do not take advantage of the longitudinal and structural components associated with multi-modal medical data. To address this, we present a new algorithm that uses the multi-block alternating direction method of multipliers to optimize a novel objective that combines multi-modal longitudinal clinical data of various modalities to simultaneously predict the cognitive scores and diagnoses of the participants in the Alzheimer's Disease Neuroimaging Initiative cohort. Our new model is designed to leverage the structure associated with clinical data that is not incorporated into standard machine learning optimization algorithms. This new approach shows state-of-the-art predictive performance and validates a collection of brain and genetic biomarkers that have been recorded previously in AD literature.",
author = "{Alzheimer’s Disease Neuroimaging Initiative} and Lodewijk Brand and Kai Nichols and Hua Wang and Heng Huang and Li Shen",
year = "2020",
month = "1",
day = "1",
language = "English (US)",
volume = "25",
pages = "7--18",
journal = "Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing",
issn = "2335-6936",

}

TY - JOUR

T1 - Predicting Longitudinal Outcomes of Alzheimer's Disease via a Tensor-Based Joint Classification and Regression Model

AU - Alzheimer’s Disease Neuroimaging Initiative

AU - Brand, Lodewijk

AU - Nichols, Kai

AU - Wang, Hua

AU - Huang, Heng

AU - Shen, Li

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Alzheimer's disease (AD) is a serious neurodegenerative condition that affects millions of people across the world. Recently machine learning models have been used to predict the progression of AD, although they frequently do not take advantage of the longitudinal and structural components associated with multi-modal medical data. To address this, we present a new algorithm that uses the multi-block alternating direction method of multipliers to optimize a novel objective that combines multi-modal longitudinal clinical data of various modalities to simultaneously predict the cognitive scores and diagnoses of the participants in the Alzheimer's Disease Neuroimaging Initiative cohort. Our new model is designed to leverage the structure associated with clinical data that is not incorporated into standard machine learning optimization algorithms. This new approach shows state-of-the-art predictive performance and validates a collection of brain and genetic biomarkers that have been recorded previously in AD literature.

AB - Alzheimer's disease (AD) is a serious neurodegenerative condition that affects millions of people across the world. Recently machine learning models have been used to predict the progression of AD, although they frequently do not take advantage of the longitudinal and structural components associated with multi-modal medical data. To address this, we present a new algorithm that uses the multi-block alternating direction method of multipliers to optimize a novel objective that combines multi-modal longitudinal clinical data of various modalities to simultaneously predict the cognitive scores and diagnoses of the participants in the Alzheimer's Disease Neuroimaging Initiative cohort. Our new model is designed to leverage the structure associated with clinical data that is not incorporated into standard machine learning optimization algorithms. This new approach shows state-of-the-art predictive performance and validates a collection of brain and genetic biomarkers that have been recorded previously in AD literature.

UR - http://www.scopus.com/inward/record.url?scp=85076005461&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85076005461&partnerID=8YFLogxK

M3 - Article

C2 - 31797582

AN - SCOPUS:85076005461

VL - 25

SP - 7

EP - 18

JO - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

JF - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

SN - 2335-6936

ER -