Justifying model complexity: Evaluating transfer learning against classical models for intraoperative nociception monitoring under anesthesia

In this clinically realistic benchmark, interpretable classical models operating on well-engineered features without personalization matched or exceeded the performance of a personalized deep learning approach while remaining computationally cheaper and simpler to deploy. These findings underscore the importance of rigorously justifying model complexity in perioperative machine learning and suggest that, for intraoperative nociception monitoring, classical approaches may offer a more favorable balance of accuracy, interpretability, and operational efficiency.