Epivigila is a Chilean integrated epidemiological surveillance system with more than 17,000,000 Chilean patient records, making it an essential and unique source of information for the quantitative and qualitative analysis of the COVID-19 pandemic in Chile. Nevertheless, given the extensive volume of data controlled by Epivigila, it is difficult for health professionals to classify vast volumes of data to determine which symptoms and comorbidities are related to infected patients. This paper aims to compare machine learning techniques (such as support-vector machine, decision tree and random forest techniques) to determine whether a patient has COVID-19 or not based on the symptoms and comorbidities reported by Epivigila. From the group of patients with COVID-19, we selected a sample of 10% confirmed patients to execute and evaluate the techniques. We used precision, recall, accuracy, F1 -score, and AUC to compare the techniques. The results suggest that the support-vector machine performs better than decision tree and random forest regarding the recall, accuracy, F1 -score, and AUC. Machine learning techniques help process and classify large volumes of data more efficiently and effectively, speeding up healthcare decision making.
The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the most available solutions require expensive and invasive infrastructures. In this study, we propose a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults. Using data collected between 2019 and 2020 with the eHomeseniors platform, we determine activity scores of older adults moving across two rooms in a house and represent an older adult fall through skeletonization. We find that our twofold approach effectively detects activity patterns and precisely identifies falls. Our study provides insights to physicians about the daily activities of their older adults and could potentially help them make decisions in case of abnormal behavior.
This study was published in the journal Sensor (MDPI) and is freely available to the public.
Evaluating health information system (HIS) quality is strategically advantageous for improving the quality of patient care. Nevertheless, few systematic studies have reported what methods, such as standards, processes, and tools, were proposed to evaluate HIS quality. This study aimed to identify and discuss the existing literature that describes standards, processes, and tools used to evaluate HIS quality. We conducted a systematic literature review using review guidelines focused on software and systems. We examined seven electronic databases—Scopus, ACM (Association for Computing Machinery), ScienceDirect, Google Scholar, IEEE Xplore, Web of Science, and PubMed—to search for and select primary studies. Out of 782 papers, we identified 17 (2.2%) primary studies. We found that most of the primary studies addressed quality evaluation from a management perspective. On the other hand, there was little explicit and pragmatic evidence on the processes and tools that allowed for the evaluation of HIS quality. To promote quality evaluation of HISs, it is necessary to define mechanisms and methods that operationalize the standards in HISs. Additionally, it is necessary to create metrics that measure the quality of the most critical components and processes of HISs.
Researchers and practitioners have recently proposed many Microservices Architecture (MSA) patterns and strategies covering various aspects of microservices system life cycle, such as service design and security. However, selecting and implementing these patterns and strategies can entail various challenges for microservices practitioners. To this end, this study proposes decision models for selecting patterns and strategies covering four MSA design areas: application decomposition into microservices, microservices security, microservices communication, and service discovery. We used peer-reviewed and grey literature to identify the patterns, strategies, and quality attributes for creating these decision models. To evaluate the familiarity, understandability, completeness, and usefulness of the decision models, we conducted semi-structured interviews with 24 microservices practitioners from 12 countries across five continents. Our evaluation results show that the practitioners found the decision models as an effective guide to select microservices patterns and strategies.
Microservices Architecture (MSA) style is a promising design approach to develop software applications consisting of multiple small and independently deployable services. Over the past few years, researchers and practitioners have proposed many MSA patterns and strategies covering various aspects of microservices design, such as application decomposition. However, selecting appropriate patterns and strategies can entail various challenges for practitioners. To this end, this study proposes a decision model for selecting patterns and strategies to decompose applications into microservices. We used peer-reviewed and grey literature to collect the patterns, strategies, and quality attributes for creating this decision model.
We conducted a mixed-methods study with 106 survey responses and 6 interviews from microservices practitioners. The main findings are: (1) a combination of domain-driven design and business capability is the most used strategy to decompose an application into microservices, (2) over half of the participants used architecture evaluation and architecture implementation when designing microservices systems, (3) API gateway and Backend for frontend patterns are the most used MSA patterns, (4) resource usage and load balancing as monitoring metrics, log management and exception tracking as monitoring practices are widely used, (5) unit and end- to-end testing are the most used testing strategies, and (6) the complexity of microservices systems poses challenges for their design, monitoring, and testing, for which there are no dedicated solutions.
We know that building software presents several challenges. But, in healthcare, these challenges can be increased by the complexity of clinical processes. In an article presented at SEH@ICSE, we presented a study exploring the opinions of clinicians regarding clinical software. This software was built using a methodology that involves stakeholders and clinicians along with implementation and dissemination strategies used in healthcare. These strategies were key to engaging clinicians in software development. The results obtained indicate that the software studied has a high acceptance by clinicians because it operationalizes the clinical activities demanded by clinicians in a timely and seamless manner in clinical processes.
In this study we described the design and results of a systematic multivocal literature mapping of the security solutions that have been proposed for microservice-based systems. The study yielded 370 academic articles and 620 grey literature; duplicates removal and the application of exclusion criteria left 36 from the academic literature and 34 from the grey literature. The security solution(s) proposed in each article were classified into variations of standard security mechanisms (e.g., Access Control) and scopes (Info Management, Threat Modeling, etc), and were associated to security contexts (detect, mitigate/stop, react, recover from attack). Our research questions addressed frequency of publications, research methodologies, security mechanisms, and security contexts. Key findings were that (1) both kinds of literature differ in their preferred empirical research strategies (examples, experiments and case studies); (2) The solutions proposed in the 70 selected articles correspond to 15 classifications of security mechanisms and analyses; (3) the most mentioned security mechanisms are Authentication and Authorization; (4) around 2/3 of solutions focused on Mitigate/Stop attacks, but none on reacting and recovering from them, and (5) the methodologies used are mostly block diagrams and code, with little use of models or analysis. These findings hold for both grey and academic literature. This study is a first step towards providing secure software researchers and practitioners a comprehensive catalog of security solutions and mechanisms, and where the clear identification of the most used security solutions will simplify their reuse to address security problems while designing microservice-based systems.
We know that microservice-based systems promote agility and rapid business development. Some features, such as fast time-to-market, scalability and optimal response times, have encouraged stakeholders to get more involved in the development and implementation of microservices architectures in order to translate their business vision into the implementation of the architecture. We realized some techniques allow the inclusion of the stakeholders’ perspective in the design of microservice architectures, few proposals consider such perspectives in the selection and evaluation of technologies that implement microservice architectures. Indeed, the qualities that characterize microservice-based systems strongly depend on the suitable selection of technologies, such as application frameworks and platforms. We have proposed a collaborative technique that includes stakeholders and software architects to select and evaluate application frameworks and platforms to implement microservice-based systems. We evaluated the technique in an industrial case of design and implementation of an Ambient-Assisted Living (AAL) system, which combines microservice architecture and Internet-of-Medical-Things (IoMT) sensors.
Building Microservices Architecture (MSA)-based applications is immensely supported by using software testing fundamentals. With the increasing interest in the development of MSA-based applications, it is important to systematically identify, analyze, and classify the publication trends, research themes, approaches, tools, and challenges in the context of testing MSA-based applications. In order to know state of the art regarding testing and MSAs, we conducted a systematic mapping study.
The search yielded 2,481 articles, and 33 articles were finally selected as the primary studies with snowballing. The key findings are that (i) 5 research themes characterize testing approaches in MSA-based applications; (ii) integration and unit testing are the most popular testing approaches; and (iii) addressing the challenges in automated and inter-communication testing is gaining the interest of the community. Additionally, it emerges that there is a lack of dedicated tools to support testing for MSA-based applications, and the reasons and solutions behind the challenges in testing MSA-based applications need to be further explored.