Jump to ContentJump to Main Navigation
Computational PsychiatryNew Perspectives on Mental Illness$
Users without a subscription are not able to see the full content.

A. David Redish and Joshua A. Gordon

Print publication date: 2016

Print ISBN-13: 9780262035422

Published to MIT Press Scholarship Online: May 2017

DOI: 10.7551/mitpress/9780262035422.001.0001

Show Summary Details
Page of

PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 03 August 2021

A Novel Framework for Improving Psychiatric Diagnostic Nosology

A Novel Framework for Improving Psychiatric Diagnostic Nosology

Chapter:
(p.169) 10 A Novel Framework for Improving Psychiatric Diagnostic Nosology
Source:
Computational Psychiatry
Author(s):

Shelly B. Flagel

Daniel S. Pine

Susanne E. Ahmari

Michael B. First

Karl J. Friston

Christoph Mathys

A. David Redish

Katharina Schmack

Jordan W. Smoller

Anita Thapar

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262035422.003.0010

This chapter proposes a new framework for diagnostic nosology based on Bayesian principles. This novel integrative framework builds upon and improves the current diagnostic system in psychiatry. Instead of starting from the assumption that a diagnosis describes a specific unitary dysfunction that causes a set of symptoms, it is assumed that the underlying disease causes the clinician to make a diagnosis. Thus, unlike the current diagnostic system, this framework treats both symptoms and diagnostic classification as consequences of the underlying pathophysiology. Comorbidities are therefore easily incorporated into the framework and inform, rather than hinder, the diagnostic process. Further, the proposed framework provides a bridge that links putative constructs related to pathophysiology and clinical diagnoses related to signs and symptoms. Crucially, this novel framework explicitly provides an iterative approach, updating and selecting the best model, based on the highest-quality available evidence at any point. It can account for and incorporate the longitudinal course of an illness. This chapter details its theoretical basis and provides clinical examples to illustrate its utility and application. It is hoped that the framework will enhance our understanding of individual differences in brain function and behavior and ultimately improve treatment outcomes in psychiatry.

Keywords:   Strüngmann Forum Report, Bayesian Integrative Framework, comorbidity, pathophysiology, clinical diagnoses, DSM, ICD, normative models of diagnoses, process models of diagnoses

MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.