Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain

arXiv:2402.19226v1 Announce Type: new
Abstract: This study investigates gender fairness in personalized pain care recommendations using machine learning algorithms. Leveraging a contextual bandits framework, personalized recommendations are formulated and evaluated using LinUCB algorithm on a dataset comprising interactions with $164$ patients across $10$ sessions each. Results indicate that while adjustments to algorithm parameters influence the quality of pain care recommendations, this impact remains consistent across genders. However, when certain patient information, such as self-reported pain measurements, is absent, the quality …

abstract algorithm algorithms arxiv cs.cy cs.lg dataset fairness framework gender interactions machine machine learning machine learning algorithms pain patients personalized personalized recommendations recommendations results study type