%0 Journal Article %1 Spitz:JCARS2020 %A Spitz, Lena %A Niemann, Uli %A Beuing, Oliver %A Neyazi, Belal %A Sandalcioglu, I. Erol %A Preim, Bernhard %A Saalfeld, Sylvia %D 2020 %J International Journal of Computer Assisted Radiology and Surgery %K kmd medical_mining vislab %N 9 %P 1525--1535 %R 10.1007/s11548-020-02217-9 %T Combining visual analytics and case-based reasoning for rupture risk assessment of intracranial aneurysms %U https://doi.org/10.1007/s11548-020-02217-9 %V 15 %X Medical case-based reasoning solves problems by applying experience gained from the outcome of previous treatments of the same kind. Particularly for complex treatment decisions, for example, incidentally found intracranial aneurysms (IAs), it can support the medical expert. IAs bear the risk of rupture and may lead to subarachnoidal hemorrhages. Treatment needs to be considered carefully, since it may entail unnecessary complications for IAs with low rupture risk. With a rupture risk prediction based on previous cases, the treatment decision can be supported.

@article{Spitz:JCARS2020, abstract = {Medical case-based reasoning solves problems by applying experience gained from the outcome of previous treatments of the same kind. Particularly for complex treatment decisions, for example, incidentally found intracranial aneurysms (IAs), it can support the medical expert. IAs bear the risk of rupture and may lead to subarachnoidal hemorrhages. Treatment needs to be considered carefully, since it may entail unnecessary complications for IAs with low rupture risk. With a rupture risk prediction based on previous cases, the treatment decision can be supported.}, added-at = {2020-07-06T15:03:00.000+0200}, author = {Spitz, Lena and Niemann, Uli and Beuing, Oliver and Neyazi, Belal and Sandalcioglu, I. Erol and Preim, Bernhard and Saalfeld, Sylvia}, biburl = {https://www.bibsonomy.org/bibtex/29fb17137242e3b1a223ed7ee3037f3d6/kmd-ovgu}, doi = {10.1007/s11548-020-02217-9}, interhash = {a205873be029549f342e81935ed18371}, intrahash = {9fb17137242e3b1a223ed7ee3037f3d6}, issn = {18616429}, journal = {International Journal of Computer Assisted Radiology and Surgery}, keywords = {kmd medical_mining vislab}, number = 9, pages = {1525--1535}, refid = {Spitz2020}, timestamp = {2021-01-15T14:53:33.000+0100}, title = {Combining visual analytics and case-based reasoning for rupture risk assessment of intracranial aneurysms}, url = {https://doi.org/10.1007/s11548-020-02217-9}, volume = 15, year = 2020 }