Wachenbrunner, J., Mast, M., Böhnke, J., Rübsamen, N., Bode, L., Karch, A., Rathert, H., Horke, A., Beerbaum, P., Marschollek, M., Jack, T., & Böhne, M. (2025). A rule-based clinical decision support system for detection of acute kidney injury after pediatric cardiac surgery. Computers in biology and medicine, 193, 110382. https://doi.org/10.1016/j.compbiomed.2025.110382

Das, P., Wiese, L., Mast, M., Boehnke, J., Wulff, A., Marschollek, M., Bode, L., Rathert, H., Jack, T., Schamer, S., Beerbaum, P., Rübsamen, N., Karch, A., Groszweski-Anders, C., Haller, A., & Frank, T. (2024). An attention-based bidirectional LSTM-CNN architecture for the early prediction of sepsis. International journal of data science and analytics, 1–15. https://doi.org/10.1007/s41060-024-00568-z

Böhnke, J., Zapf, A., Kramer, K., Weber, P., ELISE Study Group, Karch, A., & Rübsamen, N. (2024). Diagnostic test accuracy in longitudinal study settings: Theoretical approaches with use cases from clinical practice. Journal of clinical epidemiology, 169, 111314. https://doi.org/10.1016/j.jclinepi.2024.111314

Das, P.P., Mast, M., Wiese, L., Jack, T., & Wulff, A. (2024). Algorithmic fairness in healthcare data with weighted loss and adversarial learning. In: Arai, K. (eds) Intelligent systems and application, IntelliSys 2023. Lecture notes in networks and systems. Cham: Springer Nature Switzerland, 824, 264-83. https://doi.org/10.1007/978-3-031-47715-7_18

Bode, L., Mast, M., Rathert, H., Elise Study Group, Jack, T., & Wulff, A. (2023). Achieving interoperable datasets in pediatrics: A data integration approach. Studies in health technology and informatics, 305, 327–330. https://doi.org/10.3233/SHTI230496

Tute, E., Mast, M., & Wulff, A. (2023). Targeted data quality analysis for a clinical decision support system for SIRS detection in critically ill pediatric patients. Methods of information in medicine, 62(S 01), e1–e9. https://doi.org/10.1055/s-0042-1760238

Das, P.P., Wiese, L., & ELISE Study Group. (2023). Explainability based on feature importance for better comprehension of machine learning in healthcare. In: Abelló, A., et al. New trends in database and information systems. Cham: Springer Nature Switzerland, 1850, 324–35. https://doi.org/10.1007/978-3-031-42941-5_28

Bode, L., Mast, M., Rathert, H., ELISE Study Group, Jack, T., & Wulff, A. (2023). Achieving interoperable datasets in pediatrics: A data integration approach. 305, 327–330. https://doi.org/10.3233/SHTI230496

Wulff, A., Bode, L., & Mast, M. (2022). Ein wissensbasiertes, interoperables Entscheidungsunterstützungssystem für die pädiatrische Intensivmedizin. Forum der Medizin Dokumentation und Medizin Informatik. 3, 85-87. https://doi.org/10.65478/mdi.2022.3.17

Böhnke, J., Rübsamen, N., Mast, M., Rathert, H., ELISE Study Group, Karch, A., Jack, T., & Wulff, A. (2022). Prediction models for SIRS, sepsis and associated organ dysfunctions in paediatric intensive care: Study protocol for a diagnostic test accuracy study. BMJ paediatrics open, 6(1), e001618. https://doi.org/10.1136/bmjpo-2022-001618

Wulff, A., Mast, M., Bode, L., Rathert, H., Jack, T., & ELISE Study Group. (2022). Towards an evolutionary open pediatric intensive care dataset in the ELISE Project. Studies in health technology and informatics, 295, 100–103. https://doi.org/10.3233/SHTI220670

Böhnke, J., Varghese, J., ELISE Study Group, Karch, A., & Rübsamen, N. (2022). Systematic review identifies deficiencies in reporting of diagnostic test accuracy among clinical decision support systems. Journal of clinical epidemiology, 151, 171–184. https://doi.org/10.1016/j.jclinepi.2022.08.003

Wulff, A., Mast, M., Bode, L., Marschollek, M., Schamer, S., Beerbaum, P., Rübsamen, N., Böhnke, J., Karch, A., Das, P., Wiese, L., Groszewski-Anders, C., Haller, A., Frank, T., Rathert, H., & Jack, T. (2022). ELISE - an open pediatric intensive care data set [Data set]. https://doi.org/10.24355/dbbs.084-202203101150-0

Bode, L., Schamer, S., Böhnke, J., Bott, O. J., Marschollek, M., Jack, T., Wulff, A., & ELISE Study Group. (2022). Tracing the progression of sepsis in critically ill children: Clinical decision support for detection of hematologic dysfunction. Applied clinical informatics, 13(5), 1002–1014. https://doi.org/10.1055/a-1950-9637

Mast, M., Marschollek, M., Jack, T., Wulff, A., & ELISE Study Group. (2022). Developing a data driven approach for early detection of SIRS in pediatric intensive care using automatically labeled training data. Studies in health technology and informatics, 289, 228–231. https://doi.org/10.3233/SHTI210901

Wulff, A., Mast, M., Hassler, M., Montag, S., Marschollek, M., & Jack, T. (2020). Designing an openEHR-based pipeline for extracting and standardizing unstructured clinical data using natural language processing. Methods of information in medicine, 59(S 02), e64–e78. https://doi.org/10.1055/s-0040-1716403

Konferenzbeiträge/-abstracts

Rübsamen, N., Böhnke, J., Karch, A., ELISE Study Group, Weber, P., & Zapf, A. (2025). Evaluation of diagnostic tests with spatially or temporally clustered data, part 1: The choice of estimands and estimators affects results and interpretation [conference proceeding O15]. 7th International conference of methods for evaluating models, tests and biomarkers (MEMTAB) 2025, 29 April – 1 May 2025, Birmingham, UK. Available from: https://airdrive.eventsair.com/eventsairwesteuprod/production-uobevents-public/f443d62168ad4490a52621d74b98de1a

Wachenbrunner, J., Mast, M., Böhnke, J., Bode, L., Rübsamen, N., Rathert, H., Horke, A., Karch, A., Beerbaum, P., Jack, T., & Böhne, M. (2024). Developing a complex rule-based clinical decision support system for detection of acute kidney injury after pediatric cardiac surgery [conference proceeding]. 56th Annual meeting of the German society for pediatric cardiology (DGPK) 2024, 17 – 19 February 2024, Hamburg. In The Thoracic and Cardiovascular Surgeon, 72(S 02), DGPK-V04. https://doi.org/10.1055/s-0044-1780717

Böhnke, J., Zapf, A., Kramer, K., Weber, P., Karch, A., & Rübsamen, N. (2023). Methods for estimation of diagnostic test accuracy using longitudinal data [conference proceeding DocAbstr. 162]. 68th Annual meeting of the German association for medical informatics, biometry and epidemiology (GMDS) e.V. 2023, 17 – 19 September 2023. In German medical science GMS publishing house. https://doi.org/10.3205/23gmds056

Das, P. P., Wiese, L., & ELISE Study Group. (2023). Explainability based on feature importance for better comprehension of machine learning in healthcare [conference proceeding]. In A. Abelló, P. Vassiliadis, O. Romero, R. Wrembel, F. Bugiotti, J. Gamper, G. Vargas Solar, & E. Zumpano, New trends in database and information systems ADBIS 2023. Communications in computer and information science, vol 1850. Springer Champ. https://doi.org/10.1007/978-3-031-42941-5_28

Jack, T., Wulff, A., Rathert, H., Montag, S., Marschollek, M., & Beerbaum, P. (2022). Development of a Clinical Decision Support System for the Detection of SIRS after Surgery for Congenital Heart Disease [conference proceeding]. The thoracic and cardiovascular surgeon, 70(S 2), DGPK-V36. https://doi.org/10.1055/s-0042-1742994

Böhnke, J., Varghese, J., Karch, A., & Rübsamen, N. (2022). Reporting quality and risk of bias in studies evaluating the diagnostic test accuracy of clinical decision support systems: A systematic review of current practice [conference proceeding DocAbstr. 81]. 67th Annual meeting of the German association for medical informatics, biometry and epidemiology (GMDS) e.V. and 13th Annual congress of the technology and methods platform for networked medical research 2022, 21 – 25 August 2022. In German medical science GMS publishing house. https://doi.org/10.3205/22gmds006