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Evidence-based decision-making requires aggregating the evidence available in multiple trials to reach \u2013by means of systematic reviews\u2013 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 <jats:italic>hierarchical argument<\/jats:italic> 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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>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.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13326-022-00270-8","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T13:03:37Z","timestamp":1654261417000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Synthesizing evidence from clinical trials with dynamic interactive argument trees"],"prefix":"10.1186","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3483-265X","authenticated-orcid":false,"given":"Olivia","family":"Sanchez-Graillet","sequence":"first","affiliation":[]},{"given":"Christian","family":"Witte","sequence":"additional","affiliation":[]},{"given":"Frank","family":"Grimm","sequence":"additional","affiliation":[]},{"given":"Steffen","family":"Grautoff","sequence":"additional","affiliation":[]},{"given":"Basil","family":"Ell","sequence":"additional","affiliation":[]},{"given":"Philipp","family":"Cimiano","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,3]]},"reference":[{"issue":"7023","key":"270_CR1","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1136\/bmj.312.7023.71","volume":"312","author":"DL Sackett","year":"1996","unstructured":"Sackett DL, Rosenberg WMC, Gray JAM, Haynes RB, Richardson WS. 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