Analyzing interactions on combining multiple clinical guidelines

Autor: Zamborlini, Veruska, da Silveira, Marcos, Pruski, Cedric, ten Teije, Annette, Geleijn, Edwin, van der Leeden, Marike, Stuiver, Martijn, van Harmelen, Frank

Publikováno v: In Artificial Intelligence In Medicine September 2017 81:78-93

Combining Machine Learning and Semantic Web: A Systematic Mapping Study

Autor: Anna Breit, Laura Waltersdorfer, Fajar J. Ekaputra, Marta Sabou, Andreas Ekelhart, Andreea Iana, Heiko Paulheim, Jan Portisch, Artem Revenko, Annette ten Teije, Frank van Harmelen

Publikováno v: ACM Computing Surveys.

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques deve

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::20d7e07f33090838f983a7ab099224c1
https://research.wu.ac.at/de/publications/72e575fb-bccb-4147-a15e-db3bcdc4d07c

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Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks

Autor: González-Ferrer, Arturo, ten Teije, Annette, Fdez-Olivares, Juan, Milian, Krystyna

Publikováno v: In Artificial Intelligence In Medicine February 2013 57(2):91-109

Machine learning explainability in breast cancer survival

Autor: Jansen, Tom, Geleijnse, Gijs, van Maaren, Marissa, Hendriks, Mathijs P., Ten Teije, Annette, Moncada-Torres, Arturo

Publikováno v: Jansen, T, Geleijnse, G, van Maaren, M, Hendriks, M P, Ten Teije, A & Moncada-Torres, A 2020, Machine learning explainability in breast cancer survival . in L B Pape-Haugaard, C Lovis, I C Madsen, P Weber, P H Nielsen & P Scott (eds), Digital Personalized Health and Medicine : Proceedings of MIE 2020 . Studies in Health Technology and Informatics, vol. 270, IOS Press, pp. 307-311, 30th Medical Informatics Europe Conference, MIE 2020, Geneva, Switzerland, 28/04/20 . https://doi.org/10.3233/SHTI200172

Machine Learning (ML) can improve the diagnosis, treatment decisions, and understanding of cancer. However, the low explainability of how “black box” ML methods produce their output hinders their clinical adoption. In this paper, we used data fro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::d14019f452c6a2e0d34f058d2e91dbb0
https://doi.org/10.3233/shti200172