Task Allocation for Nursing Robots Using Explainable Machine Learning with Factorization Machines

We present a machine learning optimization approach for task allocation between teams of robots and nurses working collaboratively in a hospital unit. We address task allocation challenges through a recommender system that optimizes the collaboration between nursing staff and robotic nursing assistants through a two-phase methodology. First, we implement a simulated hospital environment featuring synthetic robots and nurses to collect 3D simulation data. Second, we leverage an explainable pre-hoc machine learning framework using Factorization Machines to learn and predict optimal task allocation patterns. Tasks include navigation, mobility, and item delivery for patients in a hospital unit. Our findings demonstrate that the explainable pre-hoc framework predicts efficient allocations with 94% accuracy, while offering the advantage of explainability, with task difficulty emerging as the most influential feature. Experimental results show that robots outperform nurses in completing easy tasks, while nurses perform more consistently across varying difficulty levels. This research contributes to healthcare automation by introducing a transparent task allocation system with the potential to mitigate nurse shortages, enhance workflow efficiency, and improve healthcare delivery. Our approach emphasizes the importance of explainable AI in healthcare settings and offers a novel solution to this critical optimization challenge.