Author:
Kung Benson,Chiang Maurice,Perera Gayan,Pritchard Megan,Stewart Robert
Abstract
AbstractCurrent 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.
Publisher
Springer Science and Business Media LLC
Reference50 articles.
1. JamesSpencer, L. et al. (2018) Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 392(10159), 1789–1858 (2018).
2. Theo, V. et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 3809859, 2163–2196 (2012).
3. Rush, A.J. The varied clinical presentations of major depression disorder. J. Clin. Psychiat. (2007).
4. Fried, E. I. The 52 symptoms of major depression: Lack of content overlap among seven common depression scales. J. Affect. Disord. 208, 191–197 (2017).
5. Ulbricht, C. M. et al. The use of latent class analysis for identifying subtypes of depression: A systematic review. Psychiat. Res. 266, 228–246 (2018).
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