Healthcare Recommender System Based on Medical Specialties, Patient Profiles, and Geospatial Information

The global outburst of COVID-19 introduced severe issues concerning the capacity and adoption of healthcare systems and how vulnerable citizen classes might be affected. The pandemic generated the most remarkable transformation of health services, appropriating the increase in new information and co...

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Bibliographic Details
Published inSustainability Vol. 15; no. 1; p. 499
Main Authors Torres-Ruiz, Miguel, Quintero, Rolando, Guzman, Giovanni, Chui, Kwok Tai
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2022
Subjects
Artificial intelligence
Collaboration
Computer architecture
Coronaviruses
COVID-19
Epidemics
Geographical locations
Geospatial data
Health care
Health care facilities
Health care reform
Health services
Information processing
Medical care
Mexico
Mobile devices
Patients
Rankings
Recommender systems
Social aspects
Social networks
Sustainability
Technology application
Mexico
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Summary:The global outburst of COVID-19 introduced severe issues concerning the capacity and adoption of healthcare systems and how vulnerable citizen classes might be affected. The pandemic generated the most remarkable transformation of health services, appropriating the increase in new information and communication technologies to bring sustainability to health services. This paper proposes a novel, methodological, and collaborative approach based on patient-centered technology, which consists of a recommender system architecture to assist the health service level according to medical specialties. The system provides recommendations according to the user profile of the citizens and a ranked list of medical facilities. Thus, we propose a health attention factor to semantically compute the similarity between medical specialties and offer medical centers with response capacity, health service type, and close user geographic location. Thus, considering the challenges described in the state-of-the-art, this approach tackles issues related to recommenders in mobile devices and the diversity of items in the healthcare domain, incorporating semantic and geospatial processing. The recommender system was tested in diverse districts of Mexico City, and the spatial visualization of the medical facilities filtering by the recommendations is displayed in a Web-GIS application.
ISSN:2071-1050
2071-1050
DOI:10.3390/su15010499