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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-01954-4 |
| _version_ | 1818984477607591936 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T18:19:38Z |
| format | Article |
| id | doaj.art-65fccdbcf8ef49e99910cadb61361d2d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-20T18:19:38Z |
| publishDate | 2021-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-65fccdbcf8ef49e99910cadb61361d2d2022-12-21T19:30:16ZengNature PortfolioScientific Reports2045-23222021-11-011111810.1038/s41598-021-01954-4Identifying subtypes of depression in clinician-annotated text: a retrospective cohort studyBenson Kung0Maurice Chiang1Gayan Perera2Megan Pritchard3Robert Stewart4Prairie HealthPrairie HealthInstitute of Psychiatry, Psychology and Neuroscience, King’s College LondonInstitute of Psychiatry, Psychology and Neuroscience, King’s College LondonInstitute of Psychiatry, Psychology and Neuroscience, King’s College LondonAbstract 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.https://doi.org/10.1038/s41598-021-01954-4 |
| spellingShingle | Benson Kung Maurice Chiang Gayan Perera Megan Pritchard Robert Stewart Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study Scientific Reports |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41598-021-01954-4 |
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