Towards ML-supported Triage Prediction in Real-World Emergency Room Scenarios

Faraz Maschhur, Klaus Netter, Sven Schmeier, Katrin Ostermann, Rimantas Palunis, Tobias Strapatsas, Roland Roller

Abstract: In emergency wards, patients are prioritized by clinical staff according to the urgency of their medical condition. This can be achieved by categorizing patients into different labels of urgency ranging from immediate to not urgent. However, in order to train machine learning models offering support in this regard, there is more than approaching this as a multi-class problem. This work explores the challenges and obstacles of automatic triage using anonymized real-world multi-modal ambulance data in Germany.