Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection
The model demonstrated an effective BT prediction, with XGBoost outperforming LR, particularly for a 12-hour prediction window. Notable differences in patient characteristics were observed among those who received BT and those who did not receive BT. The study establishes the feasibility of using ML for the prediction of BT in surgical ICU patients. It underlines the potential of ML models as decision support tools in healthcare, enabling early identification of BT needs.
Authors
- Ludwig Christian Hinske (ORCID: https://orcid.org/0000-0001-7273-5899)
- Sarra Zaghdoudi (ORCID: https://orcid.org/0009-0008-4958-3020)
- Seyedmostafa Sheikhalishahi (ORCID: https://orcid.org/0000-0002-0121-0160)
- Mathias Kaspar (ORCID: https://orcid.org/0000-0002-6722-766X)
- Sebastian Goss (ORCID: https://orcid.org/0009-0002-8069-5713)
- Lea K. Seidlmayer
Institutions
- University Hospital Augsburg (DE)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12911-024-02800-z
- Citations
- 3
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- Bundesministerium für Bildung und Forschung