PHOTON - A Python API for Rapid Machine Learning Model Development. - Ramona Leenings; Nils Ralf Winter; Lucas Plagwitz; Vincent Holstein; Jan Ernsting; Jakob Steenweg; Julian Gebker; Kelvin Sarink; Daniel Emden; Dominik Grotegerd; Nils Opel; Benjamin Risse; et al. (CoRR, 2020)

Utilizing a Non-Motor Symptoms Questionnaire and Machine Learning to Differentiate Movement Disorders. - Alexander Brenner; Lucas Plagwitz; Michael Fujarski; Tobias Warnecke; Julian Varghese (MIE, 2022)

Prediction of Acute Kidney Injury in the Intensive Care Unit: Preliminary Findings in a European Open Access Database. - Michael Fujarski; Christian Porschen; Lucas Plagwitz; Alexander Brenner; Narges Ghoreishi; Patrick Thoral; Harm-Jan de Grooth; Paul W. G. Elbers; Raphael Weiss; Melanie Meersch; Alexander Zarbock; Thilo Caspar von Groote; et al. (MIE, 2022)

Supporting AI-Explainability by Analyzing Feature Subsets in a Machine Learning Model. - Lucas Plagwitz; Alexander Brenner; Michael Fujarski; Julian Varghese (MIE, 2022)

Post Hoc Sample Size Estimation for Deep Learning Architectures for ECG-Classification. - Lucas Bickmann; Lucas Plagwitz; Julian Varghese (MIE, 2023)

Classification of Parkinson's Disease from Voice - Analysis of Data Selection Bias. - Alexander Brenner; Catharina Marie van Alen; Lucas Plagwitz; Julian Varghese (MIE, 2023)

DeepTSE: A Time-Sensitive Deep Embedding of ICU Data for Patient Modeling and Missing Data Imputation. - Michael Fujarski; Christian Porschen; Lucas Plagwitz; Daniel Stroth; Catharina Marie van Alen; Mahan Sadjadi; Raphael Weiss; Alexander Zarbock; Thilo Caspar von Groote; Julian Varghese (MIE, 2023)

The Necessity of Multiple Data Sources for ECG-Based Machine Learning Models. - Lucas Plagwitz; Tobias Vogelsang; Florian Doldi; Lucas Bickmann; Michael Fujarski; Lars Eckardt; Julian Varghese (MIE, 2023)

Zero-Shot LLMs for Named Entity Recognition: Targeting Cardiac Function Indicators in German Clinical Texts. - Lucas Plagwitz; Philipp Neuhaus; Kemal Yildirim; Noah Losch; Julian Varghese; Antonius Büscher (GMDS, 2024)

Benchmarking Approaches: Time Series Versus Feature-Based Machine Learning in ECG Analysis on the PTB-XL Dataset. - Lucas Bickmann; Lucas Plagwitz; Julian Varghese (MIE, 2024)

Assessing the Reliability of Machine Learning Explanations in ECG Analysis Through Feature Attribution. - Lucas Plagwitz; Lucas Bickmann; Antonius Büscher; Julian Varghese (MIE, 2024)

Data-driven time series analysis of sensory cortical processing using high-resolution fMRI across different studies. - Lucas Plagwitz; Sangcheon Choi; Xin Yu 002; Daniel Segelcke; Henriette Lambers; Esther Pogatzki-Zahn; Julian Varghese; Cornelius Faber; Bruno Pradier (Biomed. Signal Process. Control., 2024)

The Rlign Algorithm for Enhanced Electrocardiogram Analysis through R-Peak Alignment for Explainable Classification and Clustering. - Lucas Plagwitz; Lucas Bickmann; Michael Fujarski; Alexander Brenner; Warnes Gobalakrishnan; Lars Eckardt; Antonius Büscher; Julian Varghese (CoRR, 2024)

ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuning. - Lucas Bickmann; Lucas Plagwitz; Antonius Büscher; Lars Eckardt; Julian Varghese (CoRR, 2025)

Fine-Tuning LLMs on Small Medical Datasets: Text Classification and Normalization Effectiveness on Cardiology reports and Discharge records. - Noah Losch; Lucas Plagwitz; Antonius Büscher; Julian Varghese (CoRR, 2025)