Abstract: Dementia, with its intricate cognitive and behavioral aspects, presents significant challenges for patients and caregivers. Rehabilitation is a key component of dementia care, holding potential for improved patient well-being. Recommender systems, driven by advanced algorithms and patient data, could transform the patient experience by offering tailored recommendations. Inspired by their success in e-commerce, where demographic filtering, collaborative filtering, and hybrid systems excel, this survey explores the landscape of recommender systems in dementia therapy. It sheds light on how machine learning technology can provide personalized care, enhance patient outcomes, and lighten the load on caregivers. The findings open doors to patient-centered healthcare strategies for addressing the multifaceted challenges of dementia.
Keywords: Dementia, Collaborative Filtering, Content-based Filtering, Demographic Filtering, Hybrid Recommender Systems.
Cite:
Pritish Pore, Sharvari Bhagwat, Prutha Rinke, Yash Desai, Arati Deshpande, Soubhik Das,"Revolutionizing Dementia Care: A Brief Survey of Personalized Therapy Recommender Systems", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13389.
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DOI:
10.17148/IJARCCE.2024.13389
