The Mini-Mental State Examination (MMSE) is the most widely used cognitive test for assessing whether suspected symptoms align with cognitive impairment or dementia. The results of this test are meaningful for clinicians but exhibit highly unbalanced distributions in studies and analyses regarding the classification of patients with cognitive impairment. This is a complex problem when a large number of MMSE tests are analysed. Therefore, data balancing and classification techniques are crucial to support decision-making in distinguishing patients with cognitive impairment in an effective and efficient manner. This study explores machine learning techniques for data balancing and classification using a real unbalanced dataset consisting of MMSE test responses collected from 103 elderly patients participating in a Chilean patient monitoring project. We used 8 data classification techniques and five data balancing techniques. We evaluated the performance of the techniques using the following metrics: sensitivity, specificity, F1-score, likelihood ratio (LR+ and LR-), diagnostic odds ratio (DOR), and the area under the ROC curve (AUC). From the set of data balancing and classification techniques used in this study, the results indicate that synthetic minority oversampling and random forest balancing techniques improve the accuracy of cognitive impairment diagnosis. The results obtained in this study support clinical decision-making regarding early classification or exclusion of older adult patients with suspected cognitive impairment.

The paper was published in IEEE Access

Software Engineering is dedicated to the systematic and efficient development of software, which necessitates the active participation of all team members and a recognition of their unique skills and abilities, including those with autism spectrum disorders (ASD). The inclusion of individuals with ASD presents new perspectives, yet there is a lack of systematic evidence regarding the primary obstacles and potential benefits associated with their inclusion. This paper aims to identify, characterize, and describe barriers, facilitators, and methodological proposals described by the community to include individuals with ASD in the discipline of Software Engineering. We conducted a comprehensive systematic multivocal mapping study to evaluate the existing evidence on the inclusion of individuals with ASD in Software Engineering. We obtained 34 primary studies from which we identified the main facilitators of motivation to learn new skills, attention to detail, and the ability to report and visualize patterns. In contrast, the main barriers detected were communication, a lack of neurodivergent computational thinking, and sensory integration. Additionally, we identified and classified four categories of proposals that allowed the inclusion of individuals with ASD: (i) using virtual reality, (ii) creating more inclusive workspaces, (iii) encouraging neurodivergent computational thinking, and (iv) improving social skills. This study identifies the principal elements that ought to be taken into consideration when allocating tasks and roles to individuals with ASD in software development.

The paper is published in Information and Software Technology

Identifying the clinical needs to evaluate and manage the treatment and monitoring of cancer patients is a multidimensional challenge in healthcare institutions. In this regard, electronic health records (EHRs) are beneficial for managing clinical information; however, EHRs focused exclusively on patients with cancer have not been sufficiently adopted. In Chile, the need for oncology EHR has only been briefly addressed, resulting in insufficient updated and systematized information on oncology patients. In this paper, we propose the design of an oncology EHR that manages critical variables and processes for the treatment and monitoring of patients with cancer in Chile. We used a systematic methodology to design a software architecture oriented to focus groups and interviews to elicit the requirements and needs of stakeholders. We created and described an EHR design that considers four modules that group and manage the main variables and processes that are critical for treating and monitoring oncology patients. Enabling and designing a treatment and monitoring registry for cancer patients in Chile is essential because it allows for the evaluation of strategic clinical decisions in favor of patients.

This paper can be found in IEEE Access

Architects often evaluate, analyze, and select application frameworks that totally or partially implement architectural patterns that structure architectural software design to address different quality attribute concerns. To satisfy the quality attributes through architectural patterns, these must be complemented by architectural tactics. Although architectural patterns pack architectural tactics, there has been little discussion on the effect of using architectural tactics to support architectural patterns to select application frameworks in architectural design. This study reports a controlled experiment with IT professionals (N = 28) that evaluates architectural patterns and tactics to select application frameworks. We considered two scenarios: Scenario 1 includes architectural patterns and tactics as decision mechanisms, and scenario 2 considers only architectural patterns. We used precision, recall, and a custom efficiency metric as variables to compare the scenarios. The results indicate that scenario 1 produces more pragmatic and efficient solutions than scenario 2 does. This study showed that architectural tactics reduce the space for solutions and help filter application frameworks to make more precise decisions regarding architectural design.

This paper will be presented at International Conference of the Chilean Computer Science Society

Ambient Assisted Living Systems (AALSs) use information and communication technologies to support care for the growing population of older adults. AALSs focus on providing multidimensional support to families, primary care facilities, and patients to improve the quality of life of the elderly. The literature has studied the qualities of AALSs from different perspectives; however, there has been little discussion regarding the operational experience of developing and deploying such systems. This paper presents a literature review based on the PRISMA methodology regarding operational facilitators and barriers of AALSs. This study identified 750 papers, of which 61 were selected. The results indicated that the selected studies mentioned more barriers than facilitators. Both barriers and facilitators concentrate on aspects of developing and configuring the technological infrastructure of AALSs. This study organizes and describes the current literature on the challenges and opportunities regarding the operation of AALSs in practice, which translates into support for practitioners when developing and deploying AALSs.

This paper is available in the International Journal of Environmental Research and Public Health

Today, healthcare users are demanding better and more sophisticated solutions to improve their quality of life. In this regard, healthcare technologies and optimized patient management are transforming healthcare organizations and structures. People, the environment, and technological infrastructure must be combined as a unified, intelligent, and optimized care system for treatment and data management, resulting in a highly personalized and precise care experience. Intelligent electronic systems in healthcare make it possible to design and implement electronic systems and manage their maintenance to promote innovation and integration of technologies while considering energy efficiency principles and a commitment to health. Additionally, the information managed by smart electronic systems allows healthcare institutions to use the data for decision support, which can give them a strategic advantage when focusing on improving patients’ quality of life. This Special Issue of the International Journal of Environmental Research and Public Health (IJERPH) focuses on the state of knowledge regarding how intelligent electronic systems impact healthcare from the perspective of software, platforms, smart devices, sensors, and infrastructure technology. New research, reviews, case studies, mixed-method research, and empirical studies are welcome to be submitted to this Special Issue.

Architectural tactics are a key abstraction of software architecture, and support the systematic design and analysis of software architectures to satisfy quality attributes. Since originally proposed in 2003, architectural tactics have been extended and adapted to address additional quality attributes and newer kinds of systems, making quite hard for researchers and practitioners to master this growing body of specialized knowledge. This paper presents the design, execution and results of a systematic mapping study of architectural tactics in software architecture literature. The study found 552 studies in well-known digital libraries, of which 79 were selected and 12 more were added with snowballing, giving a total of 91 primary studies. Key findings are: (i) little rigor has been used to characterize and define architectural tactics; (ii) most architectural tactics proposed in the literature do not conform to the original definition; and (iii) there is little industrial evidence about the use of architectural tactics. This study organizes and summarizes the scientific literature to date about architectural tactics, identifies research opportunities, and argues for the need of more systematic definition and description of tactics.

This study is published in the Journal of Systems and Software

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.

This study is published in the International Journal of Environmental Research and Public Health.

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.

This paper was published in the Journal of Medical Internet Research.