The Diagnostic and Statistical Manual of Mental Disorders arranges co-occurring clusters of symptoms into distinct disorder categories, which theoretically have specific etiologies, pathologies, and treatments. However, researchers and clinicians alike have consistently found DSM diagnoses to have high rates of comorbidity, low diagnostic specificity, and no disorder has proven to be a discrete category. There is mounting evidence that dimensional taxonomies more accurately capture the underlying structure of mental illness and clinical presentations. The recently proposed hierarchical taxonomy of psychopathology presumes to address the issues of categorical nosologies using a data driven approach to create a dimensional model of psychopathology. However, heretofore there are no empirical examinations of HiTOP's ability to predict psychotherapy treatment outcomes. This study compared the predictive validity DSM, RDoC, and HiTOP criteria using natural language processing on free text narrative notes. Of the three GMM run, only the model using DSM criteria as predictors had adequate model fit. Additionally, none of the nosologies significantly predicted treatment course. Implications for the application of RDoC and HiTOP are discussed.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1833523 |
| Date | 08 1900 |
| Creators | Love, Patrick K |
| Contributors | Callahan, Jennifer L, Cox, Randall J, Ruggero, Camilo |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | iv, 48 pages, Text |
| Rights | Public, Love, Patrick K, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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