Vis enkel innførsel

dc.contributor.authorMüller, Lars
dc.contributor.authorGangadharaiah, Rashmi
dc.contributor.authorKlein, Simone
dc.contributor.authorPerry, James
dc.contributor.authorBernstein, Greg
dc.contributor.authorNurkse, David
dc.contributor.authorWailes, Dustin
dc.contributor.authorGraham, Rishi
dc.contributor.authorEl-Kareh, Robert
dc.contributor.authorMehta, Sanjay
dc.contributor.authorVinterbo, Staal
dc.contributor.authorAronoff-Spencer, Eliah
dc.date.accessioned2020-01-15T11:08:31Z
dc.date.available2020-01-15T11:08:31Z
dc.date.created2020-01-14T11:50:26Z
dc.date.issued2019
dc.identifier.issn1472-6947
dc.identifier.urihttp://hdl.handle.net/11250/2636389
dc.description.abstractIntroduction While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. We argue curated knowledge bases will remain an important component of future diagnostic decision support systems by providing ground truth and facilitating explainable human-computer interaction, but that prototype development is hampered by the lack of freely available computable knowledge bases. Methods We constructed an open access knowledge base and evaluated its potential in the context of a prototype decision support system. We developed a modified set-covering algorithm to benchmark the performance of our knowledge base compared to existing platforms. Testing was based on case reports from selected literature and medical student preparatory material. Results The knowledge base contains over 2000 ICD-10 coded diseases and 450 RX-Norm coded medications, with over 8000 unique observations encoded as SNOMED or LOINC semantic terms. Using 117 medical cases, we found the accuracy of the knowledge base and test algorithm to be comparable to established diagnostic tools such as Isabel and DXplain. Our prototype, as well as DXplain, showed the correct answer as “best suggestion” in 33% of the cases. While we identified shortcomings during development and evaluation, we found the knowledge base to be a promising platform for decision support systems. Conclusion We built and successfully evaluated an open access knowledge base to facilitate the development of new medical diagnostic assistants. This knowledge base can be expanded and curated by users and serve as a starting point to facilitate new technology development and system improvement in many contexts.nb_NO
dc.language.isoengnb_NO
dc.publisherBMC (part of Springer Nature)nb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleAn open access medical knowledge base for community driven diagnostic decision support system developmentnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.volume19nb_NO
dc.source.journalBMC Medical Informatics and Decision Makingnb_NO
dc.source.issue1nb_NO
dc.identifier.doi10.1186/s12911-019-0804-1
dc.identifier.cristin1772255
dc.description.localcode© The Author(s). 2019 Open Access This 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.nb_NO
cristin.unitcode194,63,30,0
cristin.unitnameInstitutt for informasjonssikkerhet og kommunikasjonsteknologi
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel

Navngivelse 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Navngivelse 4.0 Internasjonal