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Poor teamwork is a leading contributor to medical errors in the operating room. While team modeling has been studied from multiple perspectives, the literature still lacks a comprehensive analysis of which neural architectures best align with principles from social science. To address this gap, we propose a tempo-relational neural network that jointly predicts multiple team constructs while ensuring explainability. Our framework provides both factual explanations-clarifying the underlying causes of errors-and counterfactual explanations-offering actionable feedback for improving teamwork in surgical settings.
Vincenzo Marco De Luca (Ph.D. student at University of Trento)
Vincenzo Marco De Luca is a second-year Ph.D. candidate in Human-Machine Teaming at the University of Trento. He earned his B.S. in Computer Science at the University of Naples “Parthenope” and his M.Sc. in Artificial Intelligence Systems at the University of Trento. He is a member of the Structured Machine Learning (SML) Lab, working under the supervision of Prof. Andrea Passerini (graph neural networks and explainable AI), Prof. Giovanna Varni (applied social science), and Prof. Marco Zenati (Harvard University, AI for healthcare). His research focuses on graph neural networks, social interaction, and explainable artificial intelligence.
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