A Literature Review on Medicine Recommender Systems

Abstract

Medicine recommender systems can assist the medical care providers with the selection of an appropriate medication for the patients. The advanced technologies available nowadays can help developing such recommendation systems which can lead to more concise decisions. Many existing medicine recommendation systems are developed based on different algorithms. Thus, it is crucial to understand the state-of-the-art developments of these systems, their advantages and disadvantages as well as areas which require more research. In this paper, we conduct a literature review on the existing solutions for medicine recommender systems, describe and compare them based on various features, and present future research directions.

Recommender systems in the healthcare domain: state-of-the-art and research issues

“…To have a deeper look at recommendation scenarios in the healthcare domain, we searched for references using additional keywords: "food recommendation", "nutrition recommendation", "drug recommendation", "heath status prediction", "healthcare service recommendation", "physical activity recommendation", and "doctor recommendation". Besides, to ensure the quality of the references, we selected the papers using some criteria proposed by Stark et al (Stark et al 2019): (i) published from 2000 onward; (ii) well referenced with more than 15 sources, (iii) providing logical and reasonable findings of the domain, and (iv) presenting a detailed discussion on recommendation techniques. We searched for references in well-known digital libraries, such as Google Scholars 1 , Springer 2 , ACM 3 , ResearchGate 4 , Science Direct 5 , and PubMed 6 .…”

“…Besides, Thi Ngoc Trang Tran ttrang@ist.tugraz.at Extended author information available on the last page of the article. more drugs, tests, and treatment recommendations are available for medical staff daily, which triggers difficulties in deciding appropriate remedies for patients (Stark et al 2019;Wiesner and Pfeifer 2014). In this context, recommender systems for medical use should be implemented to bridge these gaps and support both, patients and medical professionals, to make better healthcare-related decisions.…”

“…Calero Valdez et al (Valdez et al 2016) provided a literature review, in which a framework to develop HRS was proposed. Stark et al (Stark et al 2019) analyzed 13 existing studies and categorized them according to criteria, such as disease, data storage, interface, data collection, data preparation, and recommendation techniques. Finally, Pincay et al (Pincay et al 2019) presented an overview of the methods and techniques used to design and implement HRS.…”

Bim on Artificial Intelligence for Decision Support in E-Health

“…In spite of an arising application domain, there is a lack of recent research in the field of MDSS for homecare describing requirements and study experiences. As reviewed by Marschollek (2012), Baumgärtel et al (2018) and Stark et al (2019), only a few papers offer details about the knowledge base (KB), system architecture and study outcome. In addition, presented researches lack of accuracy, best practices, comprehensibility, interchangeability, user acceptance, stability and insightful evaluation methods (Richter and Vogel 2020;Baumgärtel et al 2018;Baig et al 2019;Manar et al 2017;Stark et al 2019).…”

“…Today's market offers many examples in common day-to-day usage to improve decision-making in biology (Suphavilai et al 2018;Fiannaca et al 2011), economics (Dutta et al 2011), engineering (Simões-Marques 2016Papathoma-Köhle et al 2019;Coelho et al 2021) or e-commerce (Leung et al 2018;Masaro et al 2020). Refer to Sutton et al (2020) and Stark et al (2019), DSS are also widely used in the medical domain for disease diagnostic (Kumar et al 2011;Kihlgren et al 2016) and patient monitoring (Kaczmarek et al 2011;Billis et al 2015;Hussain et al 2015). In general, studies are patient-related and assist both at clinical sites or in their living environment.…”