Hierarchical MAP Denoising of Longitudinal Hamilton Depression Rating Scores

Jonathan Koss; Christine Delorenzo; Hemant D. Tagare

doi:10.1109/BIBM52615.2021.9669362

Hierarchical MAP Denoising of Longitudinal Hamilton Depression Rating Scores

Jonathan Koss
, Christine Delorenzo
, Hemant D. Tagare

Psychiatry

Yale University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The Hamilton Depression Rating Scale provides ordinal ratings for evaluating different aspects of depression. These ratings are usually quite noisy, and longitudinal patterns in the ratings can be difficult to discern. This paper proposes a hierarchical maximum-a-posteriori (MAP) method for denoising the ordinal time series of such ratings. Real-world data from a clinical trial are analyzed using the model. Denoising reveals subject-specific longitudinal patterns, predicts future ratings, and reveals progression patterns via principal component analysis.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Editors	Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1389-1394
Number of pages	6
ISBN (Electronic)	9781665401265
DOIs	https://doi.org/10.1109/BIBM52615.2021.9669362
State	Published - 2021
Event	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States Duration: Dec 9 2021 → Dec 12 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/Territory	United States
City	Virtual, Online
Period	12/9/21 → 12/12/21

Keywords

Hamilton Depression Rating Scale
Hierarchical Modeling
Ordinal Regression
Time Series

Access to Document

10.1109/BIBM52615.2021.9669362

Cite this

Koss, J., Delorenzo, C., & Tagare, H. D. (2021). Hierarchical MAP Denoising of Longitudinal Hamilton Depression Rating Scores. In Y. Huang, L. Kurgan, F. Luo, X. T. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (pp. 1389-1394). (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM52615.2021.9669362

Koss, Jonathan ; Delorenzo, Christine ; Tagare, Hemant D. / Hierarchical MAP Denoising of Longitudinal Hamilton Depression Rating Scores. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. editor / Yufei Huang ; Lukasz Kurgan ; Feng Luo ; Xiaohua Tony Hu ; Yidong Chen ; Edward Dougherty ; Andrzej Kloczkowski ; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1389-1394 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

@inproceedings{6875f0f77b394410969bf14f6e4438fa,

title = "Hierarchical MAP Denoising of Longitudinal Hamilton Depression Rating Scores",

abstract = "The Hamilton Depression Rating Scale provides ordinal ratings for evaluating different aspects of depression. These ratings are usually quite noisy, and longitudinal patterns in the ratings can be difficult to discern. This paper proposes a hierarchical maximum-a-posteriori (MAP) method for denoising the ordinal time series of such ratings. Real-world data from a clinical trial are analyzed using the model. Denoising reveals subject-specific longitudinal patterns, predicts future ratings, and reveals progression patterns via principal component analysis.",

keywords = "Hamilton Depression Rating Scale, Hierarchical Modeling, Ordinal Regression, Time Series",

author = "Jonathan Koss and Christine Delorenzo and Tagare, \{Hemant D.\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1109/BIBM52615.2021.9669362",

language = "English",

series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1389--1394",

editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, \{Xiaohua Tony\} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",

booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

}

Koss, J, Delorenzo, C & Tagare, HD 2021, Hierarchical MAP Denoising of Longitudinal Hamilton Depression Rating Scores. in Y Huang, L Kurgan, F Luo, XT Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1389-1394, 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, United States, 12/9/21. https://doi.org/10.1109/BIBM52615.2021.9669362

Hierarchical MAP Denoising of Longitudinal Hamilton Depression Rating Scores. / Koss, Jonathan; Delorenzo, Christine; Tagare, Hemant D.
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. ed. / Yufei Huang; Lukasz Kurgan; Feng Luo; Xiaohua Tony Hu; Yidong Chen; Edward Dougherty; Andrzej Kloczkowski; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1389-1394 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hierarchical MAP Denoising of Longitudinal Hamilton Depression Rating Scores

AU - Koss, Jonathan

AU - Delorenzo, Christine

AU - Tagare, Hemant D.

PY - 2021

Y1 - 2021

N2 - The Hamilton Depression Rating Scale provides ordinal ratings for evaluating different aspects of depression. These ratings are usually quite noisy, and longitudinal patterns in the ratings can be difficult to discern. This paper proposes a hierarchical maximum-a-posteriori (MAP) method for denoising the ordinal time series of such ratings. Real-world data from a clinical trial are analyzed using the model. Denoising reveals subject-specific longitudinal patterns, predicts future ratings, and reveals progression patterns via principal component analysis.

AB - The Hamilton Depression Rating Scale provides ordinal ratings for evaluating different aspects of depression. These ratings are usually quite noisy, and longitudinal patterns in the ratings can be difficult to discern. This paper proposes a hierarchical maximum-a-posteriori (MAP) method for denoising the ordinal time series of such ratings. Real-world data from a clinical trial are analyzed using the model. Denoising reveals subject-specific longitudinal patterns, predicts future ratings, and reveals progression patterns via principal component analysis.

KW - Hamilton Depression Rating Scale

KW - Hierarchical Modeling

KW - Ordinal Regression

KW - Time Series

UR - https://www.scopus.com/pages/publications/85125167742

U2 - 10.1109/BIBM52615.2021.9669362

DO - 10.1109/BIBM52615.2021.9669362

M3 - Conference contribution

AN - SCOPUS:85125167742

T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

SP - 1389

EP - 1394

BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

A2 - Huang, Yufei

A2 - Kurgan, Lukasz

A2 - Luo, Feng

A2 - Hu, Xiaohua Tony

A2 - Chen, Yidong

A2 - Dougherty, Edward

A2 - Kloczkowski, Andrzej

A2 - Li, Yaohang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Y2 - 9 December 2021 through 12 December 2021

ER -

Koss J, Delorenzo C, Tagare HD. Hierarchical MAP Denoising of Longitudinal Hamilton Depression Rating Scores. In Huang Y, Kurgan L, Luo F, Hu XT, Chen Y, Dougherty E, Kloczkowski A, Li Y, editors, Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1389-1394. (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). doi: 10.1109/BIBM52615.2021.9669362

Hierarchical MAP Denoising of Longitudinal Hamilton Depression Rating Scores

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