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Biopsychosocial Characterization of ...

Yang, Mu.

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Biopsychosocial Characterization of Cognitive Decline and Late-Life Depression Trajectories Using Bayesian Consensus Clustering and Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Biopsychosocial Characterization of Cognitive Decline and Late-Life Depression Trajectories Using Bayesian Consensus Clustering and Machine Learning./
Author:	Yang, Mu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	121 p.
Notes:	Source: Masters Abstracts International, Volume: 85-05.
Contained By:	Masters Abstracts International85-05.
Subject:	Biostatistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30639520
ISBN:	9798380836784

Biopsychosocial Characterization of Cognitive Decline and Late-Life Depression Trajectories Using Bayesian Consensus Clustering and Machine Learning.
Yang, Mu.

Biopsychosocial Characterization of Cognitive Decline and Late-Life Depression Trajectories Using Bayesian Consensus Clustering and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 121 p.

Source: Masters Abstracts International, Volume: 85-05.

Thesis (M.Sc.)--University of Toronto (Canada), 2023.

This item must not be sold to any third party vendors.

Cognitive decline and late-life depression (LLD) often co-occur and impact elderly's wellbeing. However, their intersection and antecedents are understudied. We analyzed 2,992 participants from the Religious Orders Study and Memory and Aging Project, integrating symptoms and diagnostic records with Bayesian consensus clustering to define latent sub-trajectories of cognitive decline and LLD. Logistic regression, elastic-net regression and XGBoost were used to build models of these trajectories including n=57 biopsychosocial predictors. Associations of subgroups with postmortem neuropathologies were assessed for 1,721 deceased participants. Three subgroups were identified for cognitive decline and two for LLD, which overlapped significantly (chi-square p=8.1x10-26). Elastic-net regression performed best overall, while XGBoost excelled at predicting moderate subgroups. High neuroticism and low physical health at baseline predicted unhealthy subgroups for both cognition and LLD. There were no neuropathologies associated with LLD trajectories. Our results suggest potential values of targeting neuroticism and physical health for healthy aging and patient screening.

ISBN: 9798380836784Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Bayesian clustering

Biopsychosocial Characterization of Cognitive Decline and Late-Life Depression Trajectories Using Bayesian Consensus Clustering and Machine Learning.
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245 1 0 $a Biopsychosocial Characterization of Cognitive Decline and Late-Life Depression Trajectories Using Bayesian Consensus Clustering and Machine Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 121 p.
500 $a Source: Masters Abstracts International, Volume: 85-05.
500 $a Advisor: Felsky, Daniel.
502 $a Thesis (M.Sc.)--University of Toronto (Canada), 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Cognitive decline and late-life depression (LLD) often co-occur and impact elderly's wellbeing. However, their intersection and antecedents are understudied. We analyzed 2,992 participants from the Religious Orders Study and Memory and Aging Project, integrating symptoms and diagnostic records with Bayesian consensus clustering to define latent sub-trajectories of cognitive decline and LLD. Logistic regression, elastic-net regression and XGBoost were used to build models of these trajectories including n=57 biopsychosocial predictors. Associations of subgroups with postmortem neuropathologies were assessed for 1,721 deceased participants. Three subgroups were identified for cognitive decline and two for LLD, which overlapped significantly (chi-square p=8.1x10-26). Elastic-net regression performed best overall, while XGBoost excelled at predicting moderate subgroups. High neuroticism and low physical health at baseline predicted unhealthy subgroups for both cognition and LLD. There were no neuropathologies associated with LLD trajectories. Our results suggest potential values of targeting neuroticism and physical health for healthy aging and patient screening.
590 $a School code: 0779.
650 4 $a Biostatistics. $3 1002712
650 4 $a Bioinformatics. $3 553671
653 $a Bayesian clustering
653 $a Cognitive decline
653 $a Dementia
653 $a Depression
653 $a Machine learning
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690 $a 0715
710 2 $a University of Toronto (Canada). $b Dalla Lana School of Public Health. $3 3186959
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