Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis.
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BORIS DOI
Date of Publication
August 16, 2017
Publication Type
Article
Division/Institute
Contributor
Panje, Cédric M | |
Rothermundt, Christian | |
Hundsberger, Thomas | |
Zumstein, Valentin |
Subject(s)
Series
BMC Medical research methodology
ISSN or ISBN (if monograph)
1471-2288
Publisher
BioMed Central
Language
English
Publisher DOI
PubMed ID
28814269
Uncontrolled Keywords
Description
BACKGROUND
The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously.
METHODS
Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators.
RESULTS
The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis.
CONCLUSION
This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.
The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously.
METHODS
Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators.
RESULTS
The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis.
CONCLUSION
This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.
File(s)
| File | File Type | Format | Size | License | Publisher/Copright statement | Content | |
|---|---|---|---|---|---|---|---|
| s12874-017-0400-y.pdf | text | Adobe PDF | 884.8 KB | published |