Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study

Abstract Current criteria for depression are imprecise and do not accurately characterize its distinct clinical presentations. As a result, its diagnosis lacks clinical utility in both treatment and research settings. Data-driven efforts to refine criteria have typically focused on a limited set of...

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Autores principales:	Benson Kung, Maurice Chiang, Gayan Perera, Megan Pritchard, Robert Stewart
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/65fccdbcf8ef49e99910cadb61361d2d
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spelling	oai:doaj.org-article:65fccdbcf8ef49e99910cadb61361d2d2021-11-21T12:18:16ZIdentifying subtypes of depression in clinician-annotated text: a retrospective cohort study10.1038/s41598-021-01954-42045-2322https://doaj.org/article/65fccdbcf8ef49e99910cadb61361d2d2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01954-4https://doaj.org/toc/2045-2322Abstract Current criteria for depression are imprecise and do not accurately characterize its distinct clinical presentations. As a result, its diagnosis lacks clinical utility in both treatment and research settings. Data-driven efforts to refine criteria have typically focused on a limited set of symptoms that do not reflect the disorder’s heterogeneity. By contrast, clinicians often write about patients in depth, creating descriptions that may better characterize depression. However, clinical text is not commonly used to this end. Here we show that clinically relevant depressive subtypes can be derived from unstructured electronic health records. Five subtypes were identified amongst 18,314 patients with depression treated at a large mental healthcare provider by using unsupervised machine learning: severe-typical, psychotic, mild-typical, agitated, and anergic-apathetic. Subtypes were used to place patients in groups for validation; groups were found to be associated with future outcomes and characteristics that were consistent with the subtypes. These associations suggest that these categorizations are actionable due to their validity with respect to disease prognosis. Moreover, they were derived with automated techniques that might theoretically be widely implemented, allowing for future analyses in more varied populations and settings. Additional research, especially with respect to treatment response, may prove useful in further evaluation.Benson KungMaurice ChiangGayan PereraMegan PritchardRobert StewartNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Benson Kung Maurice Chiang Gayan Perera Megan Pritchard Robert Stewart Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study
description	Abstract Current criteria for depression are imprecise and do not accurately characterize its distinct clinical presentations. As a result, its diagnosis lacks clinical utility in both treatment and research settings. Data-driven efforts to refine criteria have typically focused on a limited set of symptoms that do not reflect the disorder’s heterogeneity. By contrast, clinicians often write about patients in depth, creating descriptions that may better characterize depression. However, clinical text is not commonly used to this end. Here we show that clinically relevant depressive subtypes can be derived from unstructured electronic health records. Five subtypes were identified amongst 18,314 patients with depression treated at a large mental healthcare provider by using unsupervised machine learning: severe-typical, psychotic, mild-typical, agitated, and anergic-apathetic. Subtypes were used to place patients in groups for validation; groups were found to be associated with future outcomes and characteristics that were consistent with the subtypes. These associations suggest that these categorizations are actionable due to their validity with respect to disease prognosis. Moreover, they were derived with automated techniques that might theoretically be widely implemented, allowing for future analyses in more varied populations and settings. Additional research, especially with respect to treatment response, may prove useful in further evaluation.
format	article
author	Benson Kung Maurice Chiang Gayan Perera Megan Pritchard Robert Stewart
author_facet	Benson Kung Maurice Chiang Gayan Perera Megan Pritchard Robert Stewart
author_sort	Benson Kung
title	Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study
title_short	Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study
title_full	Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study
title_fullStr	Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study
title_full_unstemmed	Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study
title_sort	identifying subtypes of depression in clinician-annotated text: a retrospective cohort study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/65fccdbcf8ef49e99910cadb61361d2d
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Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study

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