Výsledky vyhledávání - "ÜCKERT, Frank"

Akademický článek

Lehre 2.0 - Wie werden Social Media und Web 2.0 in die medizinische Ausbildung eingebunden? Ein systematischer Literaturüberblick [Education 2.0 - How has social media and Web 2.0 been integrated into medical education? A systematical literature review]

Autor: Ückert, Frank, Hollinderbäumer, Anke, Hartz, Tobias

Publikováno v: GMS Zeitschrift für Medizinische Ausbildung, Vol 30, Iss 1, p Doc14 (2013)

[english] Objective: Present-day students have grown up with considerable knowledge concerning multi-media. The communication modes they use are faster, more spontaneous, and independent of place and time. These new web-based forms of information and

Externí odkaz: https://doaj.org/article/b3403491402e40e380359a1a74ae040e

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Akademický článek

Elektronisches Reviewverfahren

Autor: Urich, Wolfram, Marschall, Bernhard, Killersreiter, Birgit, Ückert, Frank

Publikováno v: GMS Zeitschrift für Medizinische Ausbildung, Vol 22, Iss 4, p Doc66 (2005)

Externí odkaz: https://doaj.org/article/612af28b9fbd47648f50a4e393ca35c5

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Akademický článek

An electronic health record to support patients and institutions of the health care system

Autor: Ückert, Frank, Müller, Marcel Lucas, Bürkle, Thomas, Prokosch, Hans-Ulrich

Publikováno v: GMS German Medical Science, Vol 2, p Doc06 (2004)

The department of Medical Informatics of the University Hospital Münster and the Gesakon GmbH (an university offspring) initiated the cooperative development of an electronic health record (EHR) called "akteonline.de" in 2000. From 2001 onwards seve

Externí odkaz: https://doaj.org/article/a904f368c2ec4c5888de91f31099193b

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Report

Feature reduction for machine learning on molecular features: The GeneScore

Autor: Denker, Alexander, Steshina, Anastasia, Grooss, Theresa, Ueckert, Frank, Nürnberg, Sylvia

We present the GeneScore, a concept of feature reduction for Machine Learning analysis of biomedical data. Using expert knowledge, the GeneScore integrates different molecular data types into a single score. We show that the GeneScore is superior to

Externí odkaz: http://arxiv.org/abs/2101.05546

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Report

Surgical Data Science -- from Concepts toward Clinical Translation

Autor: Maier-Hein, Lena, Eisenmann, Matthias, Sarikaya, Duygu, März, Keno, Collins, Toby, Malpani, Anand, Fallert, Johannes, Feussner, Hubertus, Giannarou, Stamatia, Mascagni, Pietro, Nakawala, Hirenkumar, Park, Adrian, Pugh, Carla, Stoyanov, Danail, Vedula, Swaroop S., Cleary, Kevin, Fichtinger, Gabor, Forestier, Germain, Gibaud, Bernard, Grantcharov, Teodor, Hashizume, Makoto, Heckmann-Nötzel, Doreen, Kenngott, Hannes G., Kikinis, Ron, Mündermann, Lars, Navab, Nassir, Onogur, Sinan, Sznitman, Raphael, Taylor, Russell H., Tizabi, Minu D., Wagner, Martin, Hager, Gregory D., Neumuth, Thomas, Padoy, Nicolas, Collins, Justin, Gockel, Ines, Goedeke, Jan, Hashimoto, Daniel A., Joyeux, Luc, Lam, Kyle, Leff, Daniel R., Madani, Amin, Marcus, Hani J., Meireles, Ozanan, Seitel, Alexander, Teber, Dogu, Ückert, Frank, Müller-Stich, Beat P., Jannin, Pierre, Speidel, Stefanie

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional

Externí odkaz: http://arxiv.org/abs/2011.02284

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