Application of Recommender Systems in Healthcare

Dear Colleagues,

Recommender systems are information-filtering systems that handle several challenges in today’s information-driven world of overload and Big Data. The growth of Big Data is always expanding, and this is the main inspiration to design recommender systems that exploit meaningful information for specific users. In addition, traditional database systems are incapable of managing whole content and information. The goal of recommender systems is not only to filter relevant information for users, but also to prioritize different contents based on the preferences of specific users. Presently, recommender systems are being deployed in several disciplines, including entertainment, E-commerce, education, social media platforms, financial services, tourism, and healthcare. More precisely, the healthcare aspect of people’s lives has become crucially important. Unfortunately, many people who have health problems do not obtain timely professional help, owing to a lack of therapists, extensive waiting lists, and treatment expenses. Recently, the application of recommender systems in different aspects of the treatment process for illnesses has also become more popular and has attracted many researchers’ attention. This is mostly due to the integration of recommender systems and medical informatics, which is increasingly resulting in more accurate, customized health care. Moreover, modeling user interests while considering their health issues is of greater necessity when making personalized recommendations.

This Special Issue aims to obtain insight into the current state of the practice of recommender systems in healthcare and the related challenges. In the existing studies, different kinds of approaches are suggested for disease diagnosis and prevention, treatment plans, medicine, and doctor recommendation, while addressing challenges of data sparsity, cold start, and limited data, especially in the medical health domain. In healthcare recommender systems, several approaches (e.g., content-based, knowledge-based, collaborative filtering, decision tree and natural language processing) are being used. In addition, the design of privacy-preserving health recommendation systems is also attracting attention. We invite scholars and scientists working in these disciplines to present their research outcomes and contribute to the literature on these topics.

Topics of interest include, but are not limited to:

• Dealing with the constraints of limited medical data while generating personalized recommendations for patients;
• Addressing challenges of diversity, novelty, and precision in health-assisted recommendation models’ lists using multi-objective optimization algorithms;
• Machine- and deep-learning-assisted emotion-aware recommender systems;
• Addressing challenges of sparsity and cold start using semantic features and ontology;
• Learning cognitive styles for personalized recommendations using machine learning;
• Personality-reliant deep-reinforcement-learning-based recommender models for improving mental health problems;
• Aspects of usability and user experience in health-related recommendation models;
• Privacy-aware health care recommendation models using machine learning;
• IoT-enabled mobile-health applications for health recommendations;
• Evolutionary algorithms and fuzzy optimization for behavior analysis of users for making personalized recommendations;
• Cancer prediction systems to assist medical practitioners in primary and secondary treatments;
• Surveys and new perspectives on recommendation models based on health factors;
• Health-care management systems using knowledge-graph-based recommendation models;
• New datasets regarding emotion, psychology, and personality factors for recommendation models;
• Case studies and deployment issues related to healthcare.

Dr. Mustansar Ali Ghazanfar
Dr. Mohammad Hossein Amirhosseini
Dr. Rawad Hammad
Dr. Muazzam Maqsood
Guest Editors

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• recommender systems
• healthcare
• Big Data
• health-assisted
• machine learning
• deep learning