Ontology-Based Model to Support Ubiquitous Healthcare Systems for COPD Patients

Advancing the Management of Long COVID by Integrating into Health Informatics Domain: Current and Future Perspectives

The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. To effectively monitor the health of those affected, maintaining up-to-date health records is essential, and digital health informatics apps for surveillance play a pivotal role. In this review, we overview the existing literature on identifying and characterizing long COVID manifestations through hierarchical classification based on Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) initiative in artificial intelligence (AI) to identify long COVID. Through knowledge exploration, we present a concept map of clinical pathways for long COVID, which offers insights into the data required and explores innovative frameworks for health informatics apps for tackling the long-term effects of COVID-19. This study achieves two main objectives by comprehensively reviewing long COVID identification and characterization techniques, making it the first paper to explore incorporating long COVID as a variable risk factor within a digital health informatics application. By achieving these objectives, it provides valuable insights on long COVID's challenges and impact on public health.

HealMA: a model-driven framework for automatic generation of IoT-based Android health monitoring applications

The development of IoT-based Android health monitoring mobile applications (apps) using traditional software development methods is a challenging task. Developers need to be familiar with various programming languages to manage the heterogeneity of hardware and software systems and to support different communication technologies. To address these problems, in this paper, we first analyze the domain of health monitoring mobile applications and then propose a framework based on model-driven engineering that accelerates the development of such systems. The proposed framework, called HealMA, includes a domain-specific modeling language, a graphical modeling editor, several validation rules, and a set of model-to-code transformations, all packed as an Eclipse plugin. We evaluated the framework to assess its applicability in generating various mobile health applications, as well as its impact on software productivity. To this end, four different health monitoring applications have been automatically generated. Then, we evaluated the productivity of software developers by comparing the time and effort it takes to use HealMA compared to a code-centric process. As part of the evaluation, we also evaluated the usability of HealMA-generated apps by conducting a user study. The results show that HealMA is both applicable and beneficial for automatic generation of usable IoT-based Android health monitoring apps.

MetaOntology: Toward developing an ontology for the metaverse

Metaverse is now perceived as a celebrated future version of the internet. In this new anticipated virtual universe, interconnected digital platforms leveraged by augmented, extended, and virtual realities will elevate users' immersive experiences through multidimensional interactions. In particular, users will be offered a broad spectrum of digital activities within a newly immersive setting mediated by technology. This study aims to design a domain ontology (MetaOntology) for the metaverse to provide an explicit specification of relevant state-of-the-art technologies and infrastructure. A four-step methodological approach is followed to construct the designated ontology. Due to the immaturity of the metaverse, MetaOntology is not intended to furnish a complete outlook on the domain, rather it aims to establish a cornerstone so as to facilitate future efforts in building extant versions of this ontology considering the evolvement of relevant technologies.

First Steps of Asthma Management with a Personalized Ontology Model

Asthma is a chronic respiratory disease characterized by severe inflammation of the bronchial mucosa. Allergic asthma is the most common form of this health issue. Asthma is classified into allergic and non-allergic asthma, and it can be triggered by several factors such as indoor and outdoor allergens, air pollution, weather conditions, tobacco smoke, and food allergens, as well as other factors. Asthma symptoms differ in their frequency and severity since each patient reacts differently to these triggers. Formal knowledge is selected as one of the most promising solutions to deal with these challenges. This paper presents a new personalized approach to manage asthma. An ontology-driven model supported by Semantic Web Rule Language (SWRL) medical rules is proposed to provide personalized care for an asthma patient by identifying the risk factors and the development of possible exacerbations.

A Self-Adaptive and Efficient Context-Aware Healthcare Model for COPD Diseases

The emergence of pervasive computing technology has revolutionized all aspects of life and facilitated many everyday tasks. As the world fights the coronavirus pandemic, it is necessary to find new ways to use technology to fight diseases and reduce their economic burden. Distributed systems have demonstrated efficiency in the healthcare domain, not only by organizing and managing patient data but also by helping doctors and other medical experts to diagnose diseases and take measures to prevent the development of serious conditions. In the case of chronic diseases, telemonitoring systems provide a way to monitor patients’ states and biomarkers in the course of their everyday routines. We developed a Chronical Obstructive Pulmonary Disease (COPD) healthcare system to protect patients against risk factors. However, each change in the patient context initiated the execution of the system’s entire rule base, which diminished performance. In this article, we use separation of concerns to reduce the impact of contextual changes by dividing the context, rules and services into software modules (units). We combine healthcare telemonitoring with context awareness and self-adaptation to create an adaptive architecture model for COPD patients. The model’s performance is validated using COPD data, demonstrating the efficiency of the separation of concerns and adaptation techniques in context-aware systems.

Smart integration of sensors, computer vision and knowledge representation for intelligent monitoring and verbal human-computer interaction

The details presented in this article revolve around a sophisticated monitoring framework equipped with knowledge representation and computer vision capabilities, that aims to provide innovative solutions and support services in the healthcare sector, with a focus on clinical and non-clinical rehabilitation and care environments for people with mobility problems. In contemporary pervasive systems most modern virtual agents have specific reactions when interacting with humans and usually lack extended dialogue and cognitive competences. The presented tool aims to provide natural human-computer multi-modal interaction via exploitation of state-of-the-art technologies in computer vision, speech recognition and synthesis, knowledge representation, sensor data analysis, and by leveraging prior clinical knowledge and patient history through an intelligent, ontology-driven, dialogue manager with reasoning capabilities, which can also access a web search and retrieval engine module. The framework’s main contribution lies in its versatility to combine different technologies, while its inherent capability to monitor patient behaviour allows doctors and caregivers to spend less time collecting patient-related information and focus on healthcare. Moreover, by capitalising on voice, sensor and camera data, it may bolster patients’ confidence levels and encourage them to naturally interact with the virtual agent, drastically improving their moral during a recuperation process.

Ontology-Based Context Event Representation, Reasoning, and Enhancing in Academic Environments

An Ambient Intelligence responds to user requests based on several contexts. A relevant context is related to what has happened in the ambient; therefore, it focuses a primordial interest on events. These involve information about time, space, or people, which is significant for modeling the context. In this paper, we propose an event-driven approach for context representation based on an ontological model. This approach is extendable and adaptable for academic domains. Moreover, the ontological model to be proposed is used in reasoning and enrichment processes with the context event information. Our event-driven approach considers five contexts as a modular perspective in the model: Person, temporal (time), physical space (location), network (resources to acquire data from the ambient), and academic events. We carried out an evaluation process for the approach based on an ontological model focused on (a) the extensibility and adaptability of use case scenarios for events in an academic environment, (b) the level of reasoning by using competence questions related to events, (c) and the consistency and coherence in the proposed model. The evaluation process shows promising results for our event-driven approach for context representation based on the ontological model.

An Ontological Approach for Early Detection of Suspected COVID-19 among COPD Patients

Recent studies on chronic obstructive pulmonary disease (COPD) patients in the context of the coronavirus 19 (COVID-19) pandemic have reported two important problems, i.e., high mortality and vulnerability among COPD patients vs. non-COPD patients. The high number of deaths are caused by exacerbations, COVID-19, and other comorbidities. Therefore, the purpose of this article is to reduce the risk factors of COPD in the COVID-19 context. In this article, we propose approaches based on adaptation mechanisms for detecting COVID-19 symptoms, to better provide appropriate care to COPD patients. To achieve this goal, an ontological model called SuspectedCOPDcoviDOlogy has been created, which consists of five ontologies for detecting suspect cases. These ontologies use vital sign parameters, symptom parameters, service management, and alerts. SuspectedCOPDcoviDOlogy enhances the COPDology proposed by a previous research project in the COPD domain. To validate the solution, an experimental study comparing the results of an existing test for the detection of COVID-19 with the results of the proposed detection system is conducted. Finally, with these results, we conclude that a rigorous combination of detection rules based on the vital sign and symptom parameters can greatly improve the dynamic detection rate of COPD patients suspected of having COVID-19, and therefore enable rapid medical assistance.

Adaptive Mechanism Model for the Prevention of SLA Violation in the Context of COPD Patient Monitoring

In this paper, we introduce a new kind of Service Level Agreement(SLA) Template to better control dynamically quality of medical monitoring platform service. Our approach is based on Health care system and Health Information Technology (HIT) research area, specifically the field of telemonitoring system for patients who suffer from chronic obstructive pulmonary disease (COPD). According to WHO statistics, COPD is the third leading cause of death worldwide. To this end, several solutions or platforms exist today to monitor COPD. Most of these platforms manage large volume of patient data. This can bring about quality and lost data problems. To address these issues, control mechanisms must be proposed and designed to improve the quality of service (QoS) on these platforms. A platform with continuously monitored QoS can save patients’ lives and reduce data quality risk. In this article, we propose an ontology that uses SLAs data from COPD monitoring platforms with dynamic data from a patient context. We dynamically calculate the number of patient data incidents and the number of service request incidents from two dynamic contexts: SLA and the patient context. If the number of incidents is higher than what is expected in the SLA, then alerts are sent to the interface parties in real time. Finally, the contribution of this article is the proposed virtual SLA template to better control SLA violation and improve quality of medical monitoring platforms services.

Context-Aware Healthcare Adaptation Model for COPD Diseases

Nowadays, ubiquitous computing and mobile applications are controlling all our life’s aspects, from social media and entertainment to the very basic needs like commerce, learning, government, and health. These systems have the ability to self-adapt to meet changes in their execution environment and the user’s context. In the healthcare domain, information systems have proven their efficiency, not only by organizing and managing patients’ data and information but also by helping doctors and medical experts in diagnosing disease and taking precluding procedure to avoid serious conditions. In chronic diseases, telemonitoring systems provide a way to monitor the patient’s state and biomarkers within their usual life’s routine. In this article, we are combining the healthcare telemonitoring systems with the context awareness and self-adaptation paradigm to provide a self-adaptive framework architecture for COPD patients.

A Pervasive Healthcare System for COPD Patients

Chronic obstructive pulmonary disease (COPD) is one of the most severe public health problems worldwide. Pervasive computing technology creates a new opportunity to redesign the traditional pattern of medical system. While many pervasive healthcare systems are currently found in the literature, there is little published research on the effectiveness of these paradigms in the medical context. This paper designs and validates a rule-based ontology framework for COPD patients. Unlike conventional systems, this work presents a new vision of telemedicine and remote care solutions that will promote individual self-management and autonomy for COPD patients through an advanced decision-making technique. Rules accuracy estimates were 89% for monitoring vital signs, and environmental factors, and 87% for nutrition facts, and physical activities.

Data Processing and Wearable Systems for Effective Human Monitoring

The last few decades have seen an unrestrained diffusion of smart-integrated technologies that are extremely pervasive and customized based on humans’ environments and habits [...]