Recommender System with Machine Learning and Artificial Intelligence

11Content-Based Health Recommender Systems

Soumya Prakash Rana¹*, Maitreyee Dey¹, Javier Prieto² and Sandra Dudley¹

¹ Division of Electrical and Electronics Engineering, London South Bank University, London, United Kingdom

² Department of Computer Science and Automation Control, University of Salamanca, Salamanca, Spain

Abstract

The rapid growth of digital health information has elevated the application and egress of data analytics healthcare industry. One proposed solution, health recommender systems (HRS) have emerged for patient-oriented decision making to recommend better healthcare advice based on profile health records (PHR) and patient databases. The HRS can enhance healthcare systems and simultaneously manage patients suffering from a range of different diseases employing predictive analytics and recommending appropriate treatments. A content-based recommender system (CBRS) is a customized HRS approach that concentrates on the evaluation of a patient’s history and ‘learns’, through machine learning (ML), to generate predictions. Additionally, CBRS intends to offer individualized and trusted information to the patient’s regarding their health status. The CBRS is usually applied in case of medical document recommenders where patients give their preferences after receiving recommendations in the form of ratings where positively ranked items are recommended to the patient. The CBRS and associated popular ML algorithms are discussed in this chapter. Subsequently, the basic ...