| Abstrakt: |
Researchers from Hellenic Open University in Patras, Greece, have developed a predictive model using automated machine learning (AutoML) to enhance decision-making in emergency department (ED) triage. The model, trained on data from the Beth Israel Deaconess Medical Center, demonstrated efficacy in predicting patient dispositions, with key variables such as acuity and waiting hours identified as significant predictors. The researchers addressed challenges related to the complexity and heterogeneity of medical data, privacy concerns, and the need for model interpretability through the incorporation of Explainable AI (XAI) techniques. Future work will focus on external validation and expanding the model to include a broader array of variables from diverse healthcare environments. [Extracted from the article] |
