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Danielson, M., Ekenberg, L. 
ORCID: https://orcid.org/0000-0002-0665-1889, & Sygel, K.
(2015).
Robust psychiatric decision support using surrogate numbers.
In:
Intelligent Software Methodologies, Tools and Techniques.
Eds. Fujita, H. & Guizzi, G.,
pp. 575-585 Cham, Switzerland: Springer International Publishing.
ISBN 978-3-319-22688-0 10.1007/978-3-319-22689-7_44.
Abstract
Decision analytical methods have been utilized and demonstrated to be of use for a broad range of applications in medical contexts, from regular diagnostic strategies and treatment to the evaluation of diagnostic tests and prediction modes and benefit-risk assessments. However, a number of issues still remain to be clarified, for instance ease of use, realism of the input data, long-term outcomes and integration into routine clinical work. In particular, many people are unaccustomed or unwilling to express input information with the preciseness and correctness most methods require, i.e., the values need to be "true" in some sense. The common lack of complete information naturally increases this problem significantly and several attempts have been made to resolve this issue. This is not least the case within psychiatric emergency care where the information available often is of a highly qualitative nature. In this article we suggest the use of so called surrogate numbers that have proliferated for a while in the form of ordinal ranking methods for multi-criteria and show how they can be adapted for use in probability elicitation.
| Item Type: | Book Section |
|---|---|
| Research Programs: | Risk & Resilience (RISK) Exploratory and Special projects (ESP) Risk, Policy and Vulnerability (RPV) |
| Bibliographic Reference: | In: H. Fujita and G. Guizzi (Eds); Intelligent Software Methodologies, Tools and Techniques; Springer International Publishing, Cham, Switzerland pp. 575-585 [2015] |
| Related URLs: | |
| Depositing User: | IIASA Import |
| Date Deposited: | 15 Jan 2016 08:53 |
| Last Modified: | 27 Aug 2021 17:25 |
| URI: | https://pure.iiasa.ac.at/11563 |
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