
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, target-condition-specific 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. Upon achieving an acceptably high level of accuracy to detect and/or predict the presence of diagnostic events, these models may be integrated in a routine application of the CDSS used in the paediatric intensive care.
Journal publications
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. Advance online publication. 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 Intelligent Systems and Application, ed. Kohei Arai. Cham: Springer Nature Switzerland, 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., and ELISE Study Group. (2023). Explainability Based on Feature Importance for Better Comprehension of Machine Learning in Healthcare. In New trends in Database and Information Systems, eds. Alberto Abelló, Panos Vassiliadis, Oscar Romero, Robert Wrembel, Francesca Bugiotti, Johann Gamper, Genoveva Vargas Solar, and Ester Zumpano. Cham: Springer Nature Switzerland, 324–35. https://doi.org/10.1007/978-3-031-42941-5_28
Wulff, A., Bode, L., Mast. M. (2022), Ein wissensbasiertes, interoperables Entscheidungsunterstützungssystem für die pädiatrische Intensivmedizin. mdi medizin://dokumentation/informatik/informationsmanagement/. 2022;3:85-87. German.
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. 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://leopard.tu-braunschweig.de/receive/dbbs_mods_00070468
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
Conference proceedings/abstracts
Wachenbrunner J, Mast M, Böhnke J, Bode L, Rübsamen N, Rathert H, et al. Developing a Complex Rule-Based Clinical Decision Support System for Detection of Acute Kidney Injury after Pediatric Cardiac Surgery. In: The Thoracic and Cardiovascular Surgeon [Internet]. Georg Thieme Verlag KG; 2024 [cited 2024 Feb 26]. p. DGPK-V04. Available from: http://www.thieme-connect.de/DOI/DOI?10.1055/s-0044-1780717
Böhnke J, Zapf A, Kramer K, Weber P, Karch A, Rübsamen N. Methods for estimation of diagnostic test accuracy using longitudinal data. In German Medical Science GMS Publishing House; 2023. p. DocAbstr. 162. Available from: https://www.egms.de/static/en/meetings/gmds2023/23gmds056.shtml
Das PP, Mast M, Wiese L, Jack T, Wulf A. Data Extraction for Associative Classification using Mined Rules in Pediatric Intensive Care Data. In Gesellschaft für Informatik e.V.; 2023 [cited 2024 Apr 26]. p. 981–94. Available from: https://dl.gi.de/handle/20.500.12116/40376
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. 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. Reporting quality and risk of bias in studies evaluating the diagnostic test accuracy of clinical decision support systems: a systematic review of current practice. In German Medical Science GMS Publishing House; 2022. p. DocAbstr. 81. Available from: https://www.egms.de/static/en/meetings/gmds2022/22gmds006.shtml
Project details
Responsible persons
Project coordinator at IESM
Univ.-Prof. Dr. med. André Karch, MD
Cooperation partners
Bioinformatics and Digital Health, Fraunhofer ITEM (data-driven prediction model developer)
Medisite GmbH (software developer)
Paediatric Cardiology and Paediatric Intensive Care, Hannover Medical School (clinicians and data providers)
Institute for Medical Informatics, Peter L. Reichertz Institute (knowledge-based detection model and data-driven prediction model developer)
Institute of Epidemiology and Social Medicine, University Münster (epidemiologists)
Funding
German Federal Ministry of Health
Funding reference: 2520DAT66C
Funding period: October 2020 – December 2023 (Analyses are still ongoing)
Awards:
External links
Boehnke J, Zapf A, Kramer K, Weber, P, ELISE Study Group, Karch, A, Ruebsamen, N (2023). Diagnostic test accuracy in longitudinal study settings: Theoretical approaches with use cases from clinical practice. medRxiv. Published online August 8, 2023. https://doi.org/10.1101/2023.08.04.23293637
Bode L, Mast M, Rathert H, Elise Study Group, Jack T, Wulff A. Achieving Interoperable Datasets in Pediatrics: A Data Integration Approach. Healthc Transform with Informatics Artif Intell. Published online June 2023:327-330. https://doi:10.3233/SHTI230496
Das, Pronaya Prosun; Mast, Marcel; Wiese, Lena; Jack, Thomas; Wulf, Antje (2023): Data Extraction for Associative Classification using Mined Rules in Pediatric Intensive Care Data. BTW 2023. DOI: 10.18420/BTW2023-67. Bonn: Gesellschaft für Informatik e.V.. ISBN: 978-3-88579-725-8. pp. 981-994. Dresden, Germany. 06.-10. März 2023
Böhnke, J., Rübsamen, N., Mast, M., Rathert, H., Group, E. S., 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. Advance online publication. https://doi.org/10.1016/j.jclinepi.2022.08.003
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. 67. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS), 13. Jahreskongress der Technologie- und Methodenplattform für die Vernetzte Medizinische Forschung e.V. (TMF). https://doi.org/10.3205/22GMDS006.
Bode, L., Schamer, S., Böhnke, J., Study Group, E., Bott, O., Marschollek, M., Jack, T., & Wulff, A. (2022). Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction. Applied clinical informatics, 10.1055/a-1950-9637. Advance online publication. https://doi.org/10.1055/a-1950-9637
Wulff, A., Mast, M., Bode, L., Marschollek, M., Schamer, S., Beerbaum, P., Rübsamen, N., Böhnke, J., Karch, A., Das, P. P., Wiese, L., Groszewski-Anders, C., Haller, A., Frank, T., Rathert, H., & Jack, T. (n.d.). ELISE - An open pediatric intensive care data set [Data set]. https://doi.org/10.24355/dbbs.084-202203101150-0
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