Publications

Timo P. Gros, David Groß, Stefan Gumhold, Jörg Hoffmann, Michaela Klauck, and Marcel Steinmetz. TraceVis: Towards Visualization for Deep Statistical Model Checking. Leveraging Applications of Formal Methods, Verification and Validation: Tools and Trends: 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, October 20–30, 2020, Proceedings, Part IV, 27–46, Springer-Verlag, Berlin, Heidelberg, 2020. [PUMA: Checking Model Networks Neural Statistical Visualization] URL

Fabian Hart, Ostap Okhrin, and Martin Treiber. Vessel-following model for inland waterways based on deep reinforcement learning. Ocean Eng., (281)114679:114679, Elsevier BV, August 2023. [PUMA: topic_engineering Vessel-following deep inland learning model reinforcement waterways]

Roman C Maron, Achim Hekler, Sarah Haggenmüller, Christof von Kalle, Jochen S Utikal, Verena Müller, Maria Gaiser, Friedegund Meier, Sarah Hobelsberger, Frank F Gellrich, Mildred Sergon, Axel Hauschild, Lars E French, Lucie Heinzerling, Justin G Schlager, Kamran Ghoreschi, Max Schlaak, Franz J Hilke, Gabriela Poch, Sören Korsing, Carola Berking, Markus V Heppt, Michael Erdmann, Sebastian Haferkamp, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Jakob N Kather, Stefan Fröhling, Daniel B Lipka, Eva Krieghoff-Henning, and Titus J Brinker. Model soups improve performance of dermoscopic skin cancer classifiers. Eur. J. Cancer, (173):307--316, Elsevier BV, September 2022. [PUMA: topic_lifescience Artificial Calibration; Deep Dermatology; Ensembles; Generalisation; Melanoma; Model Nevus; Robustness intelligence; learning; soups;]

David Nam, Julius Chapiro, Valerie Paradis, Tobias Paul Seraphin, and Jakob Nikolas Kather. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: topic_lifescience AI, Artificial CNN, Communications DICOM, Diagnosis; Digital HCC, Imaging Individual ML, MVI, Medicine; NAFLD, NASH, Prognosis Reporting TACE, TRIPOD, Transparent WSIs, a and artificial carcinoma; chemoembolisation; convolutional data deep diagnostic disease; fatty for hepatocellular images; imaging; in integration intelligence; invasion; learning; liver machine microvascular model multimodal multivariable network; neural non-alcoholic of or prediction slide steatohepatitis; support system; transarterial whole]

Jens Przybilla, Peter Ahnert, Holger Bogatsch, Frank Bloos, Frank M Brunkhorst, SepNet Critical Care Trials Group, Progress Study Group, Michael Bauer, Markus Loeffler, Martin Witzenrath, Norbert Suttorp, and Markus Scholz. Markov state modelling of disease courses and mortality risks of patients with community-acquired pneumonia. J. Clin. Med., (9)2:393, MDPI AG, February 2020. [PUMA: Markov SOFA community-acquired continuous-time decision making; medical model model; pneumonia; prognosis; score; sepsis; stochastic]

Jan Gaebel, Hans-Georg Wu, Alexander Oeser, Mario A Cypko, Matthaeus Stoehr, Andreas Dietz, Thomas Neumuth, Stefan Franke, and Steffen Oeltze-Jafra. Modeling and processing up-to-dateness of patient information in probabilistic therapy decision support. Artif. Intell. Med., (104)101842:101842, Elsevier BV, April 2020. [PUMA: Arden Decision Head Medical Therapy and decision delay; logic model modules; neck oncology; support syntax; system;]

Najia Ahmadi, Michele Zoch, Patricia Kelbert, Richard Noll, Jannik Schaaf, Markus Wolfien, and Martin Sedlmayr. Methods used in the development of common data models for health data: Scoping review. JMIR Med. Inform., (11):e45116, August 2023. [PUMA: topic_lifescience ; Data Development Healthcare Healthcare; Informatics Informatics; Interoperability Interoperability; Medical Observational Outcomes Partnership Partnership; Process Process; Repositories Repositories; Standardized Suggestive common data data; electronic elements elements; harmonisation harmonisation; health involvement model model; record record; stakeholder]

Akshay Akshay, Masoud Abedi, Navid Shekarchizadeh, Fiona C Burkhard, Mitali Katoch, Alex Bigger-Allen, Rosalyn M Adam, Katia Monastyrskaya, and Ali Hashemi Gheinani. MLcps: machine learning cumulative performance score for classification problems. Gigascience, (12)December 2022. [PUMA: topic_federatedlearn Python classification evaluation evaluation; learning; machine model package; problems; score unified]