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Synthesizing evidence from clinical trials with dynamic interactive argument trees
BACKGROUND: Evidence-based medicine propagates that medical/clinical decisions are made by taking into account high-quality evidence, most notably in the form of randomized clinical trials. Evidence-based decision-making requires aggregating the evidence available in multiple trials to reach –by mea...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9166347/ https://www.ncbi.nlm.nih.gov/pubmed/35659056 http://dx.doi.org/10.1186/s13326-022-00270-8 |
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author | Sanchez-Graillet, Olivia Witte, Christian Grimm, Frank Grautoff, Steffen Ell, Basil Cimiano, Philipp |
author_facet | Sanchez-Graillet, Olivia Witte, Christian Grimm, Frank Grautoff, Steffen Ell, Basil Cimiano, Philipp |
author_sort | Sanchez-Graillet, Olivia |
collection | PubMed |
description | BACKGROUND: Evidence-based medicine propagates that medical/clinical decisions are made by taking into account high-quality evidence, most notably in the form of randomized clinical trials. Evidence-based decision-making requires aggregating the evidence available in multiple trials to reach –by means of systematic reviews– a conclusive recommendation on which treatment is best suited for a given patient population. However, it is challenging to produce systematic reviews to keep up with the ever-growing number of published clinical trials. Therefore, new computational approaches are necessary to support the creation of systematic reviews that include the most up-to-date evidence.We propose a method to synthesize the evidence available in clinical trials in an ad-hoc and on-demand manner by automatically arranging such evidence in the form of a hierarchical argument that recommends a therapy as being superior to some other therapy along a number of key dimensions corresponding to the clinical endpoints of interest. The method has also been implemented as a web tool that allows users to explore the effects of excluding different points of evidence, and indicating relative preferences on the endpoints. RESULTS: Through two use cases, our method was shown to be able to generate conclusions similar to the ones of published systematic reviews. To evaluate our method implemented as a web tool, we carried out a survey and usability analysis with medical professionals. The results show that the tool was perceived as being valuable, acknowledging its potential to inform clinical decision-making and to complement the information from existing medical guidelines. CONCLUSIONS: The method presented is a simple but yet effective argumentation-based method that contributes to support the synthesis of clinical trial evidence. A current limitation of the method is that it relies on a manually populated knowledge base. This problem could be alleviated by deploying natural language processing methods to extract the relevant information from publications. |
format | Online Article Text |
id | pubmed-9166347 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91663472022-06-05 Synthesizing evidence from clinical trials with dynamic interactive argument trees Sanchez-Graillet, Olivia Witte, Christian Grimm, Frank Grautoff, Steffen Ell, Basil Cimiano, Philipp J Biomed Semantics Research BACKGROUND: Evidence-based medicine propagates that medical/clinical decisions are made by taking into account high-quality evidence, most notably in the form of randomized clinical trials. Evidence-based decision-making requires aggregating the evidence available in multiple trials to reach –by means of systematic reviews– a conclusive recommendation on which treatment is best suited for a given patient population. However, it is challenging to produce systematic reviews to keep up with the ever-growing number of published clinical trials. Therefore, new computational approaches are necessary to support the creation of systematic reviews that include the most up-to-date evidence.We propose a method to synthesize the evidence available in clinical trials in an ad-hoc and on-demand manner by automatically arranging such evidence in the form of a hierarchical argument that recommends a therapy as being superior to some other therapy along a number of key dimensions corresponding to the clinical endpoints of interest. The method has also been implemented as a web tool that allows users to explore the effects of excluding different points of evidence, and indicating relative preferences on the endpoints. RESULTS: Through two use cases, our method was shown to be able to generate conclusions similar to the ones of published systematic reviews. To evaluate our method implemented as a web tool, we carried out a survey and usability analysis with medical professionals. The results show that the tool was perceived as being valuable, acknowledging its potential to inform clinical decision-making and to complement the information from existing medical guidelines. CONCLUSIONS: The method presented is a simple but yet effective argumentation-based method that contributes to support the synthesis of clinical trial evidence. A current limitation of the method is that it relies on a manually populated knowledge base. This problem could be alleviated by deploying natural language processing methods to extract the relevant information from publications. BioMed Central 2022-06-03 /pmc/articles/PMC9166347/ /pubmed/35659056 http://dx.doi.org/10.1186/s13326-022-00270-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Sanchez-Graillet, Olivia Witte, Christian Grimm, Frank Grautoff, Steffen Ell, Basil Cimiano, Philipp Synthesizing evidence from clinical trials with dynamic interactive argument trees |
title | Synthesizing evidence from clinical trials with dynamic interactive argument trees |
title_full | Synthesizing evidence from clinical trials with dynamic interactive argument trees |
title_fullStr | Synthesizing evidence from clinical trials with dynamic interactive argument trees |
title_full_unstemmed | Synthesizing evidence from clinical trials with dynamic interactive argument trees |
title_short | Synthesizing evidence from clinical trials with dynamic interactive argument trees |
title_sort | synthesizing evidence from clinical trials with dynamic interactive argument trees |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9166347/ https://www.ncbi.nlm.nih.gov/pubmed/35659056 http://dx.doi.org/10.1186/s13326-022-00270-8 |
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