Staying in the intensive care unit (ICU) is often traumatic, leading to post-intensive care syndrome (PICS), which encompasses physical, psychological, and cognitive impairments. Currently, there are limited interventions available for PICS. Studies indicate that exposure to visual art may help address the psychological aspects of PICS and be more effective if it is personalized. We develop Machine Learning-based Visual Art Recommendation Systems (VA RecSys) to enable personalized therapeutic visual art experiences for post-ICU patients. We investigate four state-of-the-art VA RecSys engines, evaluating the relevance of their recommendations for therapeutic purposes compared to expert-curated recommendations. We conduct an expert pilot test and a large-scale user study (n=150) to assess the appropriateness and effectiveness of these recommendations. Our results suggest all recommendations enhance temporal affective states. Visual and multimodal VA RecSys engines compare favourably with expert-curated recommendations, indicating their potential to support the delivery of personalized art therapy for PICS prevention and treatment.

翻译：重症监护室（ICU）住院经历常具创伤性，可导致重症监护后综合征（PICS），其涉及身体、心理及认知功能的多重障碍。目前针对PICS的干预措施十分有限。研究表明，接触视觉艺术可能有助于缓解PICS的心理层面症状，且个性化干预效果更佳。我们开发了基于机器学习的视觉艺术推荐系统（VA RecSys），旨在为ICU后患者提供个性化的治疗性视觉艺术体验。本研究探究四种前沿VA RecSys引擎，评估其推荐内容相较于专家策展推荐在治疗目的上的相关性。通过专家试点测试和大规模用户研究（样本量n=150），我们评估了这些推荐的适宜性与有效性。结果表明，所有推荐均能改善即时情感状态。视觉型和多模态VA RecSys引擎在表现上优于专家策展推荐，证明其在个性化艺术疗法实施中具有支持PICS预防与治疗的潜在价值。