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Prediction of In-hospital Mortality of Trauma Patients in the Netherlands
Dobre, Maria-Narcisa (2025) Prediction of In-hospital Mortality of Trauma Patients in the Netherlands.
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| Abstract: | In the Netherlands, in-hospital mortality among trauma patients is predicted using the Trauma and Injury Severity Score (TRISS). Recent literature demonstrates the potential of machine learning (ML) models to improve predictive performance. The primary objective of our research was to evaluate whether ML models can outperform TRISS in predicting in-hospital mortality among trauma patients in the Netherlands. The second goal was to evaluate the performance of the ML models in five subgroups: older adults, severely injured patients, those with severe head injuries, patients with hip fractures, and those treated in regional trauma centres. The study used data from the Dutch National Trauma Registry (2015–2023). Three supervised ML algorithms were developed: logistic regression, extreme gradient boosting, and random forest. All ML models outperformed TRISS in both total populations and subgroups. |
| Item Type: | Essay (Master) |
| Clients: | Acute Zorg Euregio, Enschede, The Netherlands |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 01 general works, 44 medicine, 54 computer science |
| Programme: | Industrial Engineering and Management MSc (60029) |
| Link to this item: | https://purl.utwente.nl/essays/107770 |
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