Federated learning for predictive analytics in weaning from mechanical ventilation
- Mechanical ventilation is crucial for critically ill patients in ICUs, requiring accurate weaning and extubations timing for optimal outcomes. Current prediction models struggle with generalizability across datasets like MIMIC-IV and eICU-CRD. We propose a federated learning approach using XGBoost with bagging aggregation to improve weaning predictions while ensuring patient data privacy, compliant with GDPR and HIPAA. Using the OMOP Common Data Model, our method integrates machine learning techniques across three ICU databases, encompassing over 33,000 patients. Our model achieved robust performance with 77% AUC and 73% AUPRC. Planned pilot studies in Germany will further refine and validate our approach. This study demonstrates the potential of federated learning to enhance critical care by providing personalized, data-driven insights for ventilation management.
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| Author: | Seyedmostafa SheikhalishahiORCiD, Johanna SchwinnORCiD, Matthaeus MorhartORCiD, Mathias KasparORCiD, Ludwig Christian HinskeORCiDGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-1223893 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/122389 |
| ISBN: | 9781643685960OPAC |
| ISSN: | 0926-9630OPAC |
| ISSN: | 1879-8365OPAC |
| Parent Title (English): | Intelligent health systems – from technology to data and knowledge: proceedings of MIE 2025 |
| Publisher: | IOS Press |
| Place of publication: | Amsterdam |
| Editor: | Elisavet Andrikopoulou, Parisis Gallos, Theodoros N. Arvanitis, Rosalynn Austin, Arriel Benis, Ronald Cornet, Panagiotis Chatzistergos, Alexander Dejaco, Linda Dusseljee-Peute, Alaa Mohasseb, Pantelis Natsiavas, Haythem Nakkas, Philip Scott |
| Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2025 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2025/05/30 |
| First Page: | 613 |
| Last Page: | 614 |
| Series: | Studies in Health Technology and Informatics ; 327 |
| DOI: | https://doi.org/10.3233/shti250418 |
| Institutes: | Medizinische Fakultät |
| Medizinische Fakultät / Universitätsklinikum | |
| Medizinische Fakultät / Lehrstuhl für Datenmanagement und Clinical Decision Support | |
| Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
| Licence (German): |  CC-BY-NC 4.0: Creative Commons: Namensnennung - Nicht kommerziell (mit Print on Demand) |
