Skip navigation
  • Home
  • Browse
    • Communities
      & Collections
    • Browse Items by:
    • Publication Date
    • Author
    • Title
    • Subject
    • Department
  • Sign on to:
    • My MacSphere
    • Receive email
      updates
    • Edit Profile


McMaster University Home Page
  1. MacSphere
  2. Open Access Dissertations and Theses Community
  3. Open Access Dissertations and Theses
Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/28222
Title: PROGNOSTIC MODELS OF CLINICAL OUTCOMES AND PREDICTIVE MODELS OF TREATMENT RESPONSE IN PRECISION PSYCHIATRY
Authors: Watts, Devon
Advisor: Kapczinski, Flavio
Department: Neuroscience
Keywords: precision psychiatry;individualized predictive modeling;psychotic disorders;treatment response;data-driven biomarkers
Publication Date: 2022
Abstract: In this thesis, we developed prognostic models of clinical outcomes, specific to violent and criminal outcomes in psychiatry, and predictive models of treatment response at an individual level. Overall, we demonstrate that evidence-based risk factors, protective factors, and treatment status variables were able to prognosticate prospective physical aggression at an individual level; 2) prognostic models of clinical and violent outcomes in psychiatry have largely focused on clinical and sociodemographic variables, show similar performance between identifying true positives and true negatives, although the error rate of models are still high, and further refinement is needed; 3) within treatment response prediction models in MDD using EEG, greater performance was observed in predicting response to rTMS, relative to antidepressants, and across models, greater sensitivity (true positives), were observed relative to specificity (true negatives), suggesting that EEG prediction models thus far better identify non-responders than responders; and 4) across randomized clinical trials using data-driven biomarkers in predictive models, based on the consistency of performance across models with large sample sizes, the highest degree of evidence was in predicting response to sertraline and citalopram using fMRI features.
URI: http://hdl.handle.net/11375/28222
Appears in Collections:Open Access Dissertations and Theses

Files in This Item:
File Description SizeFormat 
Watts_Devon_P_submission202212_PhD.pdf
Access is allowed from: 2023-12-22
3.1 MBAdobe PDFView/Open
Show full item record Statistics


Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.

Sherman Centre for Digital Scholarship     McMaster University Libraries
©2022 McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4L8 | 905-525-9140 | Contact Us | Terms of Use & Privacy Policy | Feedback

Report Accessibility Issue