Background: Transoral robotic surgery (TORS) performed after neoadjuvant chemotherapy (NAC) is a promising treatment for advanced-stage oropharyngeal carcinoma (OPSCC) able to reduce the adjuvant therapy administration rate.
Methods: A retrospective bi-centric study was conducted to analyze NAC + TORS versus upfront TORS patients. A 1:1 propensity score matching was used to compare the two groups.
We present a novel decision-making framework for accelerated degradation tests and predictive maintenance that exploits prior knowledge and experimental data on the system's state. As a framework for sequential decision making in these areas, dynamic programming and reinforcement learning are considered, along with data-driven degradation learning when necessary. Furthermore, we illustrate both stochastic and machine learning degradation models, which are integrated in the framework, using data-driven methods.
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