Publications

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: Observational Data Healthcare; Interoperability; health Repositories; Interoperability Partnership elements; elements Healthcare model; ; Process; Medical Informatics; data; harmonisation; Suggestive data Process record; stakeholder Standardized Repositories Outcomes Partnership; involvement common record Development harmonisation model Informatics electronic]

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: problems; score unified learning; package; evaluation machine Python evaluation; model classification]

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: Artificial intelligence; learning; Deep Melanoma; soups; Dermatology; Robustness Calibration; Ensembles; topic_lifescience Model Generalisation; Nevus;]

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: a disease; HCC, or intelligence; liver multivariable non-alcoholic multimodal ML, artificial WSIs, images; prediction support integration TACE, DICOM, AI, network; deep neural Digital Diagnosis; MVI, transarterial fatty microvascular convolutional in learning; imaging; Communications invasion; NAFLD, chemoembolisation; hepatocellular Reporting steatohepatitis; Transparent Individual slide machine Prognosis Artificial data for NASH, whole Medicine; TRIPOD, of CNN, and Imaging carcinoma; diagnostic system; model]

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: Therapy decision modules; Decision neck delay; Arden and logic system; Medical model syntax; oncology; support Head]

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 medical decision pneumonia; score; making; SOFA continuous-time stochastic model; model sepsis; community-acquired prognosis;]