Artful Path to Healing: Using Machine Learning for Visual Art Recommendation to Prevent and Reduce Post-Intensive Care
CoRR(2024)
摘要
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.
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