Zbigniew W. Ras (editor) - Recommender Systems for Medicine and Music (Studies in Computational Intelligence, 946)

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We know that music has a large effect on us in general. It can make us smile when we are feeling stressed. The sound of music can cause our hearts to beat in healthier ways. It helps people regain inner peace and is the voice that binds us together. The positive relationship between music and medicine is widely known. Music has become an important tool for medical therapy. Sound healing therapy uses aspects of music to improve physical and emotional health and well-being. The person being treated partakes in the experience with a trained practitioner. Music therapy may involve listening to music, singing along to music, meditating, and playing an instrument. Evidence suggests that music therapy is beneficial for all individuals, both physically and mentally, but especially in aging societies.

Many papers on music therapy have been published, possibly because medical doctors have to deal with various kinds of chronic diseases, including cancers, in which music therapy helps to improve the symptoms. A recent study conducted at Johns Hopkins University found that Parkinsons patients singing weekly improved their quality of life, voice strength, and clarity. Music can have an impressive impact on people with Alzheimers disease since it may reduce agitation and improve behavioral issues that are common in its middle-stages. There are papers reviewing the state of the art in applying music therapy to treat Alzheimers disease [1,2]. Music provides a way to connect, even after verbal communication has become difficult. Listening to music may improve heart rate, respiratory rate, and blood pressure in people with heart disease. Music can be used to treat cancer, tinnitus, psychiatric disorders, and visually impaired patients.

Health intelligent systems have become indispensable tools in decision-making processes in the healthcare sector. Their main objective is to ensure the availability of valuable information at the right time by ensuring information quality, trustworthiness, authentication, and privacy concerns. As people use social networks to understand their health condition, the health recommender system is very important to derive outcomes such as recommending diagnoses and clinical pathway-based treatment methods taking into consideration the patients health profile.

Part I of the book contains six contributions covering the area of recommender systems and their applications in healthcare.

The Chapter , written by Madlen Ivanova and Zbigniew W. Ra, gives an overview of recommender systems and their classification which is based on filtering techniques. There are five common techniquescollaborative filtering, content-based, knowledge-based, group recommender systems, and hybrid. Each one is presented with examples covering the healthcare domain. Collaborative filtering recommendation can be further split into six subcategoriesmodel-based, memory-based, clustering based, matrix and tensor factorization, deep learning, and hybrid method.

The Chapter , written by Mila Kwiatkowska, describes healthcare as a socio-technical system that comprises interacting human and technical agents. Thus, any change to the system, such as an introduction of Recommender System based (RS-based) tools or methods, requires careful consideration of the technical and social aspects. The chapter presents the main issues which should be considered in the deployment of RS-based tools and techniques in healthcare: (1) adherence to the medical regulations for medical software, (2) compliance with the medical data privacy, (3) improvement of transparency and trustworthiness of recommendations, (4) analysis of short-term and long-term ethical issues, and (5) patient empowerment, doctorpatient relationship, and human aspects.