Affective Computing for Health Management via Recommender Systems: Exploring Challenges and Opportunities

In the current age of digital advancements, effective health management holds immense significance within the healthcare domain. A healthcare system is essential for analyzing vast amounts of patient data, providing insights, and helping diagnose diseases. To achieve this, the system must possess the intelligence to analyze a patient’s lifestyle, health records, and social activities to predict their health condition. This paper discusses the role of health recommender systems (HRS) in contemporary healthcare, particularly in analyzing substantial patient data to yield insightful predictions about health conditions. We propose a new framework for the HRS that addresses the ethical issues related to data utilization and modeling in healthcare for affective computing. We also recommend the use of data filtration and machine learning techniques to address these issues. Future studies will concentrate on empirically testing and validating the proposed framework in real-life settings, aiming to evaluate its efficacy in fostering health management, encompassing mental health and the holistic well-being of users utilizing HRS.