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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Biopsychosocial Characterization of Cognitive Decline and Late-Life Depression Trajectories Using Bayesian Consensus Clustering and Machine Learning./
Author:	Yang, Mu.
Description:	1 online resource (121 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-05.
Contained By:	Masters Abstracts International85-05.
Subject:	Biostatistics. -
Online resource:	click for full text (PQDT)
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. - 1 online resource (121 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380836784Subjects--Topical Terms:

783654
Biostatistics.
Subjects--Index Terms:

Bayesian clusteringIndex Terms--Genre/Form:

554714
Electronic books.

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