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Toralf Kirsten

As seen in: Nature, MDPI
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Articles

BRIDGE pilot study: a bilateral regulatory investigation of data governance and exchange - npj Digital Medicine

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By Helen Hou, Tom Bisson, Sophia Leiss, Julia Thierauf, Ariel Stern, Hendrik Strobelt, Felix Nensa, Alena Buyx, Katharina M. Huster, Kira Furlano, Zisis Kozlakidis, Sachin Gupta, Danko Kostadinov, Peter Boor, Anna Slagman, Thorsten Tjardes, Pierre Cholet, Nick Schneider, Thorsten Schlomm, Saskia Biskup, Rainer Röhrig, Fruzsina Molnár-Gábor, Uta Schmidt-Straßburger, Katharina Ladewig, Marcel Weigand, Jason Johnson, Toralf Kirsten, Eric Sutherland, Norman Zerbe, Albert Hofman, Ralf Heyder, Georg Schmidt, Jochen K. Lennerz
| Nature Verified
Abstract National privacy laws diverge between the European Union and United States, hindering transatlantic health data exchange and slowing AI-driven medical innovation. In response, the German Ministry of Health launched the pre-competitive Data for Health initiative, leading to the BRIDGE Pilot Study (2023–2025), a researcher-led effort to address this regulatory and legal gap.
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Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV - Scientific Reports

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By Toralf Kirsten
| Nature Verified
Abstract The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists’ access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023.
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Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset

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By Lars Hempel, Sina Sadeghi, Toralf Kirsten
| MDPI
Abstract : Accurately estimating the length of stay (LOS) of patients admitted to the intensive care unit (ICU) in relation to their health status helps healthcare management allocate appropriate resources and better plan for the future. This paper presents predictive models for the LOS of ICU patients from the MIMIC-IV database based on typical demographic and administrative data, as well as early vital signs and laboratory measurements collected on the first day of ICU stay.
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