Publications

Akshay Akshay, Mitali Katoch, Navid Shekarchizadeh, Masoud Abedi, Ankush Sharma, Fiona C Burkhard, Rosalyn M Adam, Katia Monastyrskaya, and Ali Hashemi Gheinani. Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience, (13)January 2024. [PUMA: problems; visualization learning; data machine analysis; AutoML; classification]

Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, and Olaf Wolkenhauer. ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets. Pattern Recognition, (147):110138, 2024. [PUMA: Tabular Convex Imbalanced learning, GAN, data LoRAS, space data,] URL

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]

Markus Wolfien, Najia Ahmadi, Kai Fitzer, Sophia Grummt, Kilian-Ludwig Heine, Ian-C Jung, Dagmar Krefting, Andreas Kühn, Yuan Peng, Ines Reinecke, Julia Scheel, Tobias Schmidt, Paul Schmücker, Christina Schüttler, Dagmar Waltemath, Michele Zoch, and Martin Sedlmayr. Ten topics to get started in medical informatics research. J. Med. Internet Res., (25):e45948, July 2023. [PUMA: clinical medical health; informatics; data communication; health digital data; interdisciplinary research]

Elena Williams, Manuel Kienast, Evelyn Medawar, Janis Reinelt, Alberto Merola, Sophie Anne Ines Klopfenstein, Anne Rike Flint, Patrick Heeren, Akira-Sebastian Poncette, Felix Balzer, Julian Beimes, Paul von Bünau, Jonas Chromik, Bert Arnrich, Nico Scherf, and Sebastian Niehaus. A standardized clinical data harmonization pipeline for scalable AI application deployment (FHIR-DHP): Validation and usability study. JMIR Med. Inform., (11):e43847, JMIR Publications Inc., March 2023. [PUMA: intelligence; mart AI care; deployment; IV; artificial fast application; information resources; care data; medical cooperation; data standardization for healthcare interoperability; AI; unit; FHIR; usability MIMIC diagnosis; interoperability patient pipeline; research; intensive]

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]

Daniel Ayala, Inma Hernández, David Ruiz, and Erhard Rahm. Multi-source dataset of e-commerce products with attributes for property matching. Data Brief, (41)107884:107884, Elsevier BV, April 2022. [PUMA: integration; engineering; Data Ontology; Property matching]

Katja Hoffmann, Katja Cazemier, Christoph Baldow, Silvio Schuster, Yuri Kheifetz, Sibylle Schirm, Matthias Horn, Thomas Ernst, Constanze Volgmann, Christian Thiede, Andreas Hochhaus, Martin Bornhäuser, Meinolf Suttorp, Markus Scholz, Ingmar Glauche, Markus Loeffler, and Ingo Roeder. Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology. BMC Med. Inform. Decis. Mak., (20)1:28, February 2020. [PUMA: decision-making; workflow; Haematology; Support Mathematical therapy Clinical Data Routine treatment management; simulation; modelling; system optimization; Individual Computer planning; Model-based]

Pascal Kerschke, Holger H Hoos, Frank Neumann, and Heike Trautmann. Automated algorithm selection: Survey and perspectives. Evol. Comput., (27)1:3--45, MIT Press, 2019. [PUMA: feature-based learning; metalearning data analysis; optimisation; automated streams.; exploratory combinatorial continuous machine approaches; Automated selection; algorithm configuration; landscape]

Diego Esteves, Anisa Rula, Aniketh Janardhan Reddy, and Jens Lehmann. Toward Veracity Assessment in RDF Knowledge Bases: An Exploratory Analysis. J. Data and Information Quality, (9)3Association for Computing Machinery, New York, NY, USA, February 2018. [PUMA: exploratory trustworthiness, benchmark, fact DeFacto, data checking, quality, analysis data, linked] URL