Data-based Decision Rules to Personalize Depression Follow-up

Abstract Depression is a common mental illness with complex and heterogeneous progression dynamics. Risk grouping of depression treatment population based on their longitudinal patterns has the potential to enable cost-effective monitoring policy design. This paper establishes a rule-based method to...

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Autores principales:	Ying Lin, Shuai Huang, Gregory E. Simon, Shan Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4da3f45378fe453681459c04b5a1c5c3
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spelling	oai:doaj.org-article:4da3f45378fe453681459c04b5a1c5c32021-12-02T15:08:23ZData-based Decision Rules to Personalize Depression Follow-up10.1038/s41598-018-23326-12045-2322https://doaj.org/article/4da3f45378fe453681459c04b5a1c5c32018-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-23326-1https://doaj.org/toc/2045-2322Abstract Depression is a common mental illness with complex and heterogeneous progression dynamics. Risk grouping of depression treatment population based on their longitudinal patterns has the potential to enable cost-effective monitoring policy design. This paper establishes a rule-based method to identify a set of risk predictive patterns from person-level longitudinal disease measurements by integrating the data transformation, rule discovery and rule evaluation. We further extend the identified rules to create rule-based monitoring strategies to adaptively monitor individuals with different disease severities. We applied the rule-based method on an electronic health record (EHR) dataset of depression treatment population containing person-level longitudinal Patient Health Questionnaire (PHQ)-9 scores for assessing depression severity. 12 risk predictive rules are identified, and the rule-based prognostic model based on identified rules enables more accurate prediction of disease severity than other prognostic models including RuleFit, logistic regression and Support Vector Machine. Two rule-based monitoring strategies outperform the latest PHQ-9 based monitoring strategy by providing higher sensitivity and specificity. The rule-based method can lead to a better understanding of disease dynamics, achieving more accurate prognostics of disease progressions, personalizing follow-up intervals, and designing cost-effective monitoring of patients in clinical practice.Ying LinShuai HuangGregory E. SimonShan LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-8 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ying Lin Shuai Huang Gregory E. Simon Shan Liu Data-based Decision Rules to Personalize Depression Follow-up
description	Abstract Depression is a common mental illness with complex and heterogeneous progression dynamics. Risk grouping of depression treatment population based on their longitudinal patterns has the potential to enable cost-effective monitoring policy design. This paper establishes a rule-based method to identify a set of risk predictive patterns from person-level longitudinal disease measurements by integrating the data transformation, rule discovery and rule evaluation. We further extend the identified rules to create rule-based monitoring strategies to adaptively monitor individuals with different disease severities. We applied the rule-based method on an electronic health record (EHR) dataset of depression treatment population containing person-level longitudinal Patient Health Questionnaire (PHQ)-9 scores for assessing depression severity. 12 risk predictive rules are identified, and the rule-based prognostic model based on identified rules enables more accurate prediction of disease severity than other prognostic models including RuleFit, logistic regression and Support Vector Machine. Two rule-based monitoring strategies outperform the latest PHQ-9 based monitoring strategy by providing higher sensitivity and specificity. The rule-based method can lead to a better understanding of disease dynamics, achieving more accurate prognostics of disease progressions, personalizing follow-up intervals, and designing cost-effective monitoring of patients in clinical practice.
format	article
author	Ying Lin Shuai Huang Gregory E. Simon Shan Liu
author_facet	Ying Lin Shuai Huang Gregory E. Simon Shan Liu
author_sort	Ying Lin
title	Data-based Decision Rules to Personalize Depression Follow-up
title_short	Data-based Decision Rules to Personalize Depression Follow-up
title_full	Data-based Decision Rules to Personalize Depression Follow-up
title_fullStr	Data-based Decision Rules to Personalize Depression Follow-up
title_full_unstemmed	Data-based Decision Rules to Personalize Depression Follow-up
title_sort	data-based decision rules to personalize depression follow-up
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/4da3f45378fe453681459c04b5a1c5c3
work_keys_str_mv	AT yinglin databaseddecisionrulestopersonalizedepressionfollowup AT shuaihuang databaseddecisionrulestopersonalizedepressionfollowup AT gregoryesimon databaseddecisionrulestopersonalizedepressionfollowup AT shanliu databaseddecisionrulestopersonalizedepressionfollowup
_version_	1718388119653318656

Data-based Decision Rules to Personalize Depression Follow-up

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