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A systematic review of healthcare recommender systems: Open issues, challenges, and techniques - 2022

A Systematic Review Of Healthcare Recommender Systems: Open Issues, Challenges, And Techniques

Survey Paper on Healthcare Recommender Systems: Open Issues, Challenges, And Techniques

Research Area:  Machine Learning


The maintenance of health through prevention, diagnosis, and treatment of illnesses, injuries, and other mental and physical impairments in people is called healthcare that is performed by professionals in health fields. All activities performed in providing primary, secondary, and tertiary care, as well as in public health, are components of healthcare systems that are required to analyze massive amounts of patients’ data to gain insights and aid in disease prediction. The healthcare recommender system (HRS) is a growing and robust platform for healthcare services.The purpose of this paper is to identify, taxonomically classify, and compare current HRS researches in a systematic way.This study presents a systematic literature review (SLR) method for HRSs regarding 41 papers published between 2010 and 2021; the selected articles fall into five classes: collaborative-based, content-based, knowledge-based, context-based, and hybrid. Seventeen essential factors are identified to examine the selected HRSs, and each of the papers were studied to find advantages, disadvantages, evaluation types, and tools. In addition, we address a discussion of challenges, future directions, and open issues.This paper demonstrates that HRSs are still in their early stages of maturity, but they are developing. It identifies the need for a scalable, confidential, and reliable framework to aid in the improvement of both patients’ and health professionals’ trust in HRSs. This research reveals a lack of standardization of medical codes across various healthcare systems. In addition, this review identifies the need for novel patterns to address cold start problems.

Healthcare Recommender Systems
Deep Learning
Machine Learning

Author(s) Name:  Maryam Etemadi, Sepideh Bazzaz Abkenar, Ahmad Ahmadzadeh, Mostafa Haghi Kashani, Parvaneh Asghari, Mohammad Akbari, Ebrahim Mahdipour

Journal name:  Expert Systems with Applications

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.eswa.2022.118823

Volume Information:  Volume 213, Part A, 1 March 2023, 118823