ABSTRACT
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. © 2022 by the authors.
ABSTRACT
The COVID-19 pandemic has promoted the need to take care of health at home, using M-Health systems to monitor vital signs in healthy people and in those with heart conditions. Thus, the body sensor networks (BSNs) are extremely useful for sensing and alerting when some type of health risk is identified such as arrhythmia and low oxygen levels as well as for helping to make a decision. This chapter describes a home health monitoring system to identify cardiac risk events and monitor oxygenation levels in a person using a BSN simulator and exploring the energy performance of the network, considering the IoT devices installed at home. The work is oriented toward monitoring and identifying risk events in closed spaces, and it is addressed to people with two types of conditions: (1) those with heart diseases and (2) those people who need to monitor their oxygen levels after recovering from the COVID-19 disease. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
COVID-19 has changed our lifestyle;nowadays, activities such as studying, working, and meetings, among others, have drastically changed from being face to face to being remote;however, there is still an activity that has not changed as quickly as needed because of its main purpose, i.e., transportation. In this approach, a complete COVID-19 geospatial analysis is conducted correlating official reported cases of COVID-19-infected individuals and those who died with the data of public transportation, focusing on specific areas and the subway service in Mexico City. The geospatial analysis allows identifying the importance of some subway stations and their influence on the rate of infected people and also allows visualizing the distribution of COVID-19 all over the geographic areas near the subway stations and understanding the distribution of COVID-19 in the city. Finally, the approach generates a visualization model of the distribution of COVID-19 and its relation to the subway service using geospatial intelligence. © 2022 Elsevier Inc. All rights reserved.