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Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559810/ https://www.ncbi.nlm.nih.gov/pubmed/28814269 http://dx.doi.org/10.1186/s12874-017-0400-y |
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author | Panje, Cédric M. Glatzer, Markus von Rappard, Joscha Rothermundt, Christian Hundsberger, Thomas Zumstein, Valentin Plasswilm, Ludwig Putora, Paul Martin |
author_facet | Panje, Cédric M. Glatzer, Markus von Rappard, Joscha Rothermundt, Christian Hundsberger, Thomas Zumstein, Valentin Plasswilm, Ludwig Putora, Paul Martin |
author_sort | Panje, Cédric M. |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-5559810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55598102017-08-18 Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis Panje, Cédric M. Glatzer, Markus von Rappard, Joscha Rothermundt, Christian Hundsberger, Thomas Zumstein, Valentin Plasswilm, Ludwig Putora, Paul Martin BMC Med Res Methodol Research Article 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. BioMed Central 2017-08-16 /pmc/articles/PMC5559810/ /pubmed/28814269 http://dx.doi.org/10.1186/s12874-017-0400-y Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Panje, Cédric M. Glatzer, Markus von Rappard, Joscha Rothermundt, Christian Hundsberger, Thomas Zumstein, Valentin Plasswilm, Ludwig Putora, Paul Martin Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis |
title | Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis |
title_full | Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis |
title_fullStr | Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis |
title_full_unstemmed | Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis |
title_short | Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis |
title_sort | applied swarm-based medicine: collecting decision trees for patterns of algorithms analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559810/ https://www.ncbi.nlm.nih.gov/pubmed/28814269 http://dx.doi.org/10.1186/s12874-017-0400-y |
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