A learning and interoperable, smart clinical decision support systems for the paediatric intensive care
Acronym (German): ELISE
ELISE aimed at developing a digital clinical decision support system (CDSS) for the paediatric intensive care unit to optimize diagnostic and therapeutic routine processes. Especially the early identification and anticipation of life-threatening diagnoses are crucial because the paediatric intensive care setting is a complex knowledge- and experience-based area that continuously challenges healthcare professionals. All diagnostic and therapeutic measures are shaped by highly individual variations due to the age-specific development stages of children and the heterogeneous, partly seldom, diseases within this patient population. To address these challenges, we develop ELISE, which can support clinicians in the early identification of Systemic Inflammatory Response Syndrome (SIRS), sepsis, and associated organ dysfunctions (i.e., hepatic/ hematologic/ respiratory/ renal/ cardiovascular organ dysfunction).
The ELISE CDSS consists of multiple knowledge-based detection and data-driven prediction models. These models are developed and evaluated for their diagnostic accuracy (i.e., measured by sensitivity and specificity) before they are validated. Within ELISE, we have already developed detection models for hematologic and renal organ dysfunction (see Publications). These models may be integrated in a routine application of the CDSS used in the paediatric intensive care.
Böhnke J, Varghese J, ELISE Study Group, Karch A, Rübsamen N. Systematic review identifies deficiencies in reporting of diagnostic test accuracy among clinical decision support systems. Journal of Clinical Epidemiology. 2022;151:171-184. doi:10.1016/j.jclinepi.2022.08.003
Mast M, Marschollek M, Jack T, Wulff A, ELISE Study Group. Developing a data driven approach for early detection of SIRS in pediatric intensive care using automatically labeled training data. Stud Health Technol Inform. 2022;289:228-231. doi:10.3233/SHTI210901
Wulff A, Mast M, Bode L, Rathert H, Jack T, ELISE Study Group. Towards an evolutionary open pediatric intensive care dataset in the ELISE project. Stud Health Technol Inform. 2022;295:100-103. doi:10.3233/SHTI220670
Bode L, Schamer S, Böhnke J, et al. Tracing the progression of sepsis in critically ill children: clinical decision support for detection of hematologic dysfunction. Appl Clin Inform. 2022;13(05):1002-1014. doi:10.1055/a-1950-9637
Böhnke J*, Rübsamen N*, Mast M, et al. Prediction models for SIRS, sepsis and associated organ dysfunctions in paediatric intensive care: study protocol for a diagnostic test accuracy study. BMJ Paediatrics Open. 2022;6(1):e001618. doi:10.1136/bmjpo-2022-001618
Bode L, Mast M, Rathert H, ELISE Study Group, Jack T, Wulff A. Achieving interoperable datasets in pediatrics: a data integration approach. Stud Health Technol Inform. 2023;305:327-330. doi:10.3233/SHTI230496
Wachenbrunner J, Mast M, Böhnke J, et al. A rule-based clinical decision support system for detection of acute kidney injury after pediatric cardiac surgery. Computers in Biology and Medicine. 2025;193:110382. doi:10.1016/j.compbiomed.2025.110382
Das PP, Mast M, Wiese L, Jack T, Wulff A, ELISE Study Group. Data extraction for associative classification using mined rules in pediatric intensive care data. BTW 2023: Lecture Notes in Informatics (LNI) - Proceedings. 2023;P-331:981-994. doi:10.18420/BTW2023-67
Das PP, Mast M, Wiese L, Jack T, Wulff A, ELISE Study Group. Algorithmic fairness in healthcare data with weighted loss and adversarial learning. Intelligent Systems and Applications: Proceedings of the 2023 Intelligent Systems Conference (IntelliSys). 2023;824:264-283. doi:10.1007/978-3-031-47715-7_18
Das PP, Wiese L, Mast M, et al. An attention-based bidirectional LSTM-CNN architecture for the early prediction of sepsis. International Journal of Data Science and Analytics. Published online June 3, 2024. doi:10.1007/s41060-024-00568-z
Böhnke J, Zapf A, Kramer K, et al. Diagnostic test accuracy in longitudinal study settings: Theoretical approaches with use cases from clinical practice. Journal of Clinical Epidemiology. 2024;169:111314. doi:10.1016/j.jclinepi.2024.111314
Project details
Responsible persons
Project period and status
Start: October 2020
End: December 2023
(Analyses are still ongoing)
Cooperation partners
Paediatric Cardiology and Paediatric Intensive Care, Hannover Medical School
Institute for Medical Informatics, Peter L. Reichertz Institute
Bioinformatics and Digital Health, Fraunhofer ITEM
Medisite GmbH
Funding
German Federal Ministry of Health
Funding reference: 2520DAT66C
Awards:
External links
Böhnke J, Varghese J, ELISE Study Group, Karch A, Rübsamen N. Systematic review identifies deficiencies in reporting of diagnostic test accuracy among clinical decision support systems. Journal of Clinical Epidemiology. 2022;151:171-184. doi:10.1016/j.jclinepi.2022.08.003
Mast M, Marschollek M, Jack T, Wulff A, ELISE Study Group. Developing a data driven approach for early detection of SIRS in pediatric intensive care using automatically labeled training data. Stud Health Technol Inform. 2022;289:228-231. doi:10.3233/SHTI210901
Wulff A, Mast M, Bode L, Rathert H, Jack T, ELISE Study Group. Towards an evolutionary open pediatric intensive care dataset in the ELISE project. Stud Health Technol Inform. 2022;295:100-103. doi:10.3233/SHTI220670
Bode L, Schamer S, Böhnke J, et al. Tracing the progression of sepsis in critically ill children: clinical decision support for detection of hematologic dysfunction. Appl Clin Inform. 2022;13(05):1002-1014. doi:10.1055/a-1950-9637
Böhnke J*, Rübsamen N*, Mast M, et al. Prediction models for SIRS, sepsis and associated organ dysfunctions in paediatric intensive care: study protocol for a diagnostic test accuracy study. BMJ Paediatrics Open. 2022;6(1):e001618. doi:10.1136/bmjpo-2022-001618
Bode L, Mast M, Rathert H, ELISE Study Group, Jack T, Wulff A. Achieving interoperable datasets in pediatrics: a data integration approach. Stud Health Technol Inform. 2023;305:327-330. doi:10.3233/SHTI230496
Wachenbrunner J, Mast M, Böhnke J, et al. A rule-based clinical decision support system for detection of acute kidney injury after pediatric cardiac surgery. Computers in Biology and Medicine. 2025;193:110382. doi:10.1016/j.compbiomed.2025.110382
Das PP, Mast M, Wiese L, Jack T, Wulff A, ELISE Study Group. Data extraction for associative classification using mined rules in pediatric intensive care data. BTW 2023: Lecture Notes in Informatics (LNI) - Proceedings. 2023;P-331:981-994. doi:10.18420/BTW2023-67
Das PP, Mast M, Wiese L, Jack T, Wulff A, ELISE Study Group. Algorithmic fairness in healthcare data with weighted loss and adversarial learning. Intelligent Systems and Applications: Proceedings of the 2023 Intelligent Systems Conference (IntelliSys). 2023;824:264-283. doi:10.1007/978-3-031-47715-7_18
Das PP, Wiese L, Mast M, et al. An attention-based bidirectional LSTM-CNN architecture for the early prediction of sepsis. International Journal of Data Science and Analytics. Published online June 3, 2024. doi:10.1007/s41060-024-00568-z
Böhnke J, Zapf A, Kramer K, et al. Diagnostic test accuracy in longitudinal study settings: Theoretical approaches with use cases from clinical practice. Journal of Clinical Epidemiology. 2024;169:111314. doi:10.1016/j.jclinepi.2024.111314