## 1 Introduction  
Recent developments in the field of Cloud computing have stimulated the need to create new software solutions for the monitoring, management and optimization of an organization's services, in which developers build an application and are not concerned about the underlying infrastructure. For this reason, several authors have proposed different software solutions, such as infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS) and function as a service (FaaS), focused on the intensive processing of data from different sources (e.g., Internet of Things (IoT) devices, data repositories or other sources) that will be consumed by the services to offer users different business-specific functionalities [1, 2, 3, 4, 5]. Within this context, the eHealth field is an area that is becoming increasingly important in public health policies because health care delivery systems (e.g., public health management or electronic health records (EHR)) are constantly evolving towards more patient-centered customized services and digital transformation (e.g., eGovernment) [6; 7], which, based on data collected by the eHealth systems in conjunction with the recommender systems, can accelerate and drive decision making [8]. Considering the above, we have identified a growing concern regarding the increase in the aging population in our society. In Europe, the expected growth of the population over 65 will be 74% by 2060 [9; 10; 11], while in North America, it will be 20% by 2050 [12; 13; 14]. This trend places a serious burden on the public health system, since this group of people have high age-related comorbidity (e.g., degeneration in physiology and the abilities of memory, vision and decision), consuming extensive resources of both primary and specialized care [9; 15], and consequently, it is more difficult for the elderly to adapt within society. Telemonitoring systems are very popular today, since these systems are primarily based on the remote analysis of biometric data or daily activities, stemming from either a low availability of medical centers or mobility complications of people. These systems require the use of the Internet of Healthcare Things (IoHT) such as body sensors, bed sensors and smart watches for the collection and monitoring of people through their data, which allows the detection of anomalies in the vital signs of the elderly, and thus alerting medical personnel to these problems [16; 17; 18; 19]. In addition, telemonitoring is an effective low-cost means for periodic health exams or preventive treatments in nursing homes [13; 20]. Based on the ideas presented and the use of telemonitoring, we have focused on the design, development and implementation of a platform aimed at detection and clinical diagnosis assisted by a recommender system based on artificial intelligence (AI) algorithms within the field of infectious diseases for the elderly population living in nursing homes. It should be noted that this article is part of an extensive study in the field of infectious diseases focused on the three target groups of acute respiratory infections, urinary tract infections and skin and soft tissue infections. However, the article presented here is based in part on several previous studies by the partners of the Design and implementation of a low-cost intelligent system for the prediagnosis and telecare of infectious diseases in elderly people (SPIDEP) [21; 22; 23; 24; 25]. Another priority task in the concept of a platform targeted for Cloud computing environments is to define the architectural pattern to be implemented in the platform (e.g., service-oriented architectures (SOA) or microservices architecture (MSA)), which should be based on the needs (e.g., agility, maintainability or high-performance) and the nature of the organization (e.g., eHealth, transport, energy or supply chain management) [7; 26; 27; 28; 29].