DeepReco: Deep Learning Based Personalized Mental Health Support and Coping Mechanism Recommender System

Abstract

Individualized resources and standard therapies are increasingly being provided to people through the use of personalized recommender systems for mental wellness. This study presents DeepReco, a deep learning-based personalized mental health support and coping mechanism recommender system designed to provide context-aware recommendations while ensuring privacy, reliability, and trustworthiness. The proposed framework integrates multimodal healthcare data, including user health profiles, behavioral patterns, and clinical information, to generate personalized recommendations for mental health support. Deep learning techniques are employed to analyze complex relationships within heterogeneous healthcare data, enabling accurate identification of user preferences and mental health needs. Recent studies that focus on using massive amounts of medical data while integrating multimodal data from many sources are highlighted, which lowers healthcare costs and workloads. Recommender systems and big data analytics play a significant part in the healthcare industry when it comes to patient health decision-making procedures. It shows how big data analytics can be used to implement an efficient health recommender engine and shows how the health care sector can move from a conventional scenario to a personalized model in a tele-health setting.