Content‐Based Health Recommender Systems
Soumya Prakash Rana, Maitreyee Dey, Javier Prieto, Sandra Dudley · 2020
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 concepts, feature extraction methods, similarity measure, and ranking are presented and discussed. The privacy preservation phase is also discussed, particularly how data is protected, and intruders prohibited from altering valuable information. Finally, the challenges and open issues are deliberated.