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

Research output: Contribution to journalArticlepeer-review

Benson Kung, Maurice Chiang, Gayan Perera, Megan Pritchard, Robert Stewart

Original languageEnglish
Article number22426
JournalScientific Reports
Volume11
Issue number1
Early online date17 Nov 2021
DOIs
E-pub ahead of print17 Nov 2021
PublishedDec 2021

Bibliographical note

Publisher Copyright: © 2021, The Author(s).

King's Authors

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.

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