The Representational Challenge for Designing and Managing 5P Medicine Ecosystems.

Health and social care systems around the globe currently undergo a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental and behavioral context. This transformation is strongly supported by technologies such as micro- and nanotechnologies, advanced computing, artificial intelligence, edge computing, etc. For enabling communication and cooperation between actors from different domains using different methodologies, languages and ontologies based on different education, experiences, etc., we have to understand the transformed health ecosystems and all its components in structure, function and relationships in the necessary detail ranging from elementary particles up to the universe. That way, we advance design and management of the complex and highly dynamic ecosystem from data to knowledge level. The challenge is the consistent, correct and formalized representation of the transformed health ecosystem from the perspectives of all domains involved, representing and managing them based on related ontologies. The resulting business view of the real-world ecosystem must be interrelated using the ISO/IEC 21838 Top Level Ontologies standard. Thereafter, the outcome can be transformed into implementable solutions using the ISO/IEC 10746 Open Distributed Processing Reference Model. Model and framework for this system-oriented, architecture-centric, ontology-based, policy-driven approach have been developed by the first author and meanwhile standardized as ISO 23903 Interoperability and Integration Reference Architecture.

Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy.

The evolution of Patient-Generated Health Data (PGHD) represents a major shift in healthcare, fueled by technological progress. The advent of PGHD, with technologies such as wearable devices and home monitoring systems, extends data collection beyond clinical environments, enabling continuous monitoring and patient engagement in their health management. Despite the growing prevalence of PGHD, there is a lack of clear understanding among stakeholders about its meaning, along with concerns about data security, privacy, and accuracy. This article aims to thoroughly review and clarify PGHD by examining its origins, types, technological foundations, and the challenges it faces, especially in terms of privacy and security regulations. The review emphasizes the role of PGHD in transforming healthcare through patient-centric approaches, their understanding, and personalized care, while also exploring emerging technologies and addressing data privacy and security issues, offering a comprehensive perspective on the current state and future directions of PGHD. The methodology employed for this review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Rayyan, AI-Powered Tool for Systematic Literature Reviews. This approach ensures a systematic and comprehensive coverage of the available literature on PGHD, focusing on the various aspects outlined in the objective. The review encompassed 36 peer-reviewed articles from various esteemed publishers and databases, reflecting a diverse range of methodologies, including interviews, regular articles, review articles, and empirical studies to address three RQs exploratory, impact assessment, and solution-oriented questions related to PGHD. Additionally, to address the future-oriented fourth RQ for PGHD not covered in the above review, we have incorporated existing domain knowledge articles. This inclusion aims to provide answers encompassing both basic and advanced security measures for PGHD, thereby enhancing the depth and scope of our analysis.

Principles and Standards for Designing and Managing Integrable and Interoperable Transformed Health Ecosystems.

The advancement of sciences and technologies, economic challenges, increasing expectations, and consumerism result in a radical transformation of health and social care around the globe, characterized by foundational organizational, methodological, and technological paradigm changes. The transformation of the health and social care ecosystems aims at ubiquitously providing personalized, preventive, predictive, participative precision (5P) medicine, considering and understanding the individual's health status in a comprehensive context from the elementary particle up to society. For designing and implementing such advanced ecosystems, an understanding and correct representation of the structure, function, and relations of their components is inevitable, thereby including the perspectives, principles, and methodologies of all included disciplines. To guarantee consistent and conformant processes and outcomes, the specifications and principles must be based on international standards. A core standard for representing transformed health ecosystems and managing the integration and interoperability of systems, components, specifications, and artifacts is ISO 23903:2021, therefore playing a central role in this publication. Consequently, ISO/TC 215 and CEN/TC 251, both representing the international standardization on health informatics, declared the deployment of ISO 23903:2021 mandatory for all their projects and standards addressing more than one domain. The paper summarizes and concludes the first author's leading engagement in the evolution of pHealth in Europe and beyond over the last 15 years, discussing the concepts, principles, and standards for designing, implementing, and managing 5P medicine ecosystems. It not only introduces the theoretical foundations of the approach but also exemplifies its deployment in practical projects and solutions regarding interoperability and integration in multi-domain ecosystems. The presented approach enables comprehensive and consistent integration of and interoperability between domains, systems, related actors, specifications, standards, and solutions. That way, it should help overcome the problems and limitations of data-centric approaches, which still dominate projects and products nowadays, and replace them with knowledge-centric, comprehensive, and consistent ones.

Designing and Managing Advanced, Intelligent and Ethical Health and Social Care Ecosystems.

The ongoing transformation of health systems around the world aims at personalized, preventive, predictive, participative precision medicine, supported by technology. It considers individual health status, conditions, and genetic and genomic dispositions in personal, social, occupational, environmental and behavioral contexts. In this way, it transforms health and social care from art to science by fully understanding the pathology of diseases and turning health and social care from reactive to proactive. The challenge is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e., of multidisciplinary and dynamic systems in variable context. This enables mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc., as philosophy or cognitive sciences do. The approach requires the deployment of advanced technologies including autonomous systems and artificial intelligence. This poses important ethical and governance challenges. This paper describes the aforementioned transformation of health and social care ecosystems as well as the related challenges and solutions, resulting in a sophisticated, formal reference architecture. This reference architecture provides a system-theoretical, architecture-centric, ontology-based, policy-driven model and framework for designing and managing intelligent and ethical ecosystems in general and health ecosystems in particular.

Selected Papers from the pHealth 2021 Conference, Genoa, Italy, 8-10 November 2021.

This Special Issue of the Journal of Personalized Medicine presents extended versions of selected contributions to pHealth 2021, the 18th International Conference on Wearable Micro and Nano Technologies for Personalized Health, held on 8-10 November 2021 in Genoa, Italy [...].

Future pHealth Ecosystem-Holistic View on Privacy and Trust.

Modern pHealth is an emerging approach to collecting and using personal health information (PHI) for personalized healthcare and personalized health management. For its products and services, it deploys advanced technologies such as sensors, actuators, computers, mobile phones, etc. Researchers have shown that today's networked information systems, such as pHealth ecosystems, miss appropriate privacy solutions, and trust is only an illusion. In the future, the situation will be even more challenging because pHealth ecosystems will be highly distributed, dynamic, increasingly autonomous, and multi-stakeholder, with the ability to monitor the person's regular life, movements, emotions, and health-related behavior in real time. In this paper, the authors demonstrate that privacy and trust in ecosystems are system-level problems that need a holistic, system-focused solution. To make future pHealth ethically acceptable, privacy-enabled, and trustworthy, the authors have developed a conceptual five-level privacy and trust model as well as a formula that describes the impact of privacy and trust factors on the level of privacy and trust. Furthermore, the authors have analyzed privacy and trust challenges and possible solutions at each level of the model. Based on the analysis performed, a proposal for future ethically acceptable, trustworthy, and privacy-enabled pHealth is developed. The solution combines privacy as personal property and trust as legally binding fiducial duty approaches and uses a blockchain-based smart contract agreement to store people's privacy and trust requirements and service providers' promises.

Multidimensional Machine Learning Model to Calculate a COVID-19 Vulnerability Index.

In Colombia, the first case of COVID-19 was confirmed on 6 March 2020. On 13 March 2023, Colombia registered 6,360,780 confirmed positive cases of COVID-19, representing 12.18% of the total population. The National Administrative Department of Statistics (DANE) in Colombia published in 2020 a COVID-19 vulnerability index, which estimates the vulnerability (per city block) of being infected with COVID-19. Unfortunately, DANE did not consider multiple factors that could increase the risk of COVID-19 (in addition to demographic and health), such as environmental and mobility data (found in the related literature). The proposed multidimensional index considers variables of different types (unemployment rate, gross domestic product, citizens' mobility, vaccination data, and climatological and spatial information) in which the incidence of COVID-19 is calculated and compared with the incidence of the COVID-19 vulnerability index provided by DANE. The collection, data preparation, modeling, and evaluation phases of the Cross-Industry Standard Process for Data Mining methodology (CRISP-DM) were considered for constructing the index. The multidimensional index was evaluated using multiple machine learning models to calculate the incidence of COVID-19 cases in the main cities of Colombia. The results showed that the best-performing model to predict the incidence of COVID-19 in Colombia is the Extra Trees Regressor algorithm, obtaining an R-squared of 0.829. This work is the first step toward a multidimensional analysis of COVID-19 risk factors, which has the potential to support decision making in public health programs. The results are also relevant for calculating vulnerability indexes for other viral diseases, such as dengue.

Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems.

This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.

Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence.

Background: Recent studies demonstrate the potential of Artificial Intelligence to support diagnosis, mortality assessment, and clinical decisions in low-and-middle-income countries (LMICs). However, explicit evidence of strategies to overcome the particular challenges for transformed health systems in these countries does not exist. Objective: The present study undertakes a review of research on the current status of artificial intelligence (AI) to identify requirements, gaps, challenges, and possible strategies to strengthen the large, complex, and heterogeneous health systems in LMICs. Design: After introducing the general challenges developing countries face, the methodology of systematic reviews and the meta-analyses extension for scoping reviews (PRISMA-ScR) is introduced according to the preferred reporting items. Scopus and Web of Science databases were used to identify papers published between 2011-2022, from which we selected 151 eligible publications. Moreover, a narrative review was conducted to analyze the evidence in the literature about explicit evidence of strategies to overcome particular AI challenges in LMICs. Results: The analysis of results was divided into two groups: primary studies, which include experimental studies or case studies using or deploying a specific AI solution (n = 129), and secondary studies, including opinion papers, systematic reviews, and papers with strategies or guidelines (n = 22). For both study groups, a descriptive statistical analysis was performed describing their technological contribution, data used, health context, and type of health interventions. For the secondary studies group, an in-deep narrative review was performed, identifying a set of 40 challenges gathered in eight different categories: data quality, context awareness; regulation and legal frameworks; education and change resistance; financial resources; methodology; infrastructure and connectivity; and scalability. A total of 89 recommendations (at least one per challenge) were identified. Conclusion: Research on applying AI and ML to healthcare interventions in LMICs is growing; however, apart from very well-described ML methods and algorithms, there are several challenges to be addressed to scale and mainstream experimental and pilot studies. The main challenges include improving the quality of existing data sources, training and modeling AI solutions based on contextual data; and implementing privacy, security, informed consent, ethical, liability, confidentiality, trust, equity, and accountability policies. Also, robust eHealth environments with trained stakeholders, methodological standards for data creation, research reporting, product certification, sustained investment in data sharing, infrastructures, and connectivity are necessary. Systematic review registration: [https://rb.gy/frn2rz].

Standards and Principles to Enable Interoperability and Integration of 5P Medicine Ecosystems.

Health and social care ecosystems are currently a matter of foundational organizational, methodological and technological paradigm changes towards personalized, preventive, predictive, participative precision (5P) medicine. For designing and implementing such advanced ecosystems, an understanding and correct representation of structure, function and relations of their components is inevitable. To guarantee consistent and conformant processes and outcomes, the specifications and principles must be internationally standardized. Summarizing the first author's Keynotes over the last 15 years of pHealth conferences, the paper discusses concepts, standards and principles of 5P medicine ecosystems including their design and implementation. Furthermore, a guidance to find and to deploy corresponding international standards in practical projects is provided.

CTS2 OWL: Mapping OWL Ontologies to CTS2 Terminology Resources.

The advancement of healthcare towards P5 medicine requires communication and cooperation between all actors and institutions involved. Interoperability must go beyond integrating data from different sources and include the understanding of the meaning of the data in the context of concepts and contexts they represent for a specific use case. In other words, we have to advance from data sharing through sharing semantics up to sharing clinical and medical knowledge. According to the Good Modeling Best Practices, we have to start with describing the real-world business system by domain experts using Domain Ontologies before transforming it into an information and communication technology (ICT) system, thereafter specifying the informational components and then transforming the system into an implementable solution. Any representation style - in the system development process acc. to ISO 10746 called system view - is defined by a related ontology, to be distinguished from real-world domain ontologies representing the knowledge spaces of involved disciplines. The system enabling such representational transformation shall also support versioning as well as the management of historical evolutions. One of such systems is the Common Terminology Service Release 2 (CTS2), which is a standard that allows the complete management of terminological contents. The main objective of this work is to present the choices we made to transform an ontology, written in the standard Ontology Web Language (OWL), into the CTS2 objects. We tested our transformation approach with the Alzheimer's Disease Ontology. We managed to map all the elements of the considered ontology to CTS2 terminological resources, except for a subset of elements such as the equivalentClass derived from restrictions on other classes.

Privacy and Trust in pHealth - Past, Present and Future.

From beginning to today, pHealth has been a data driven service that collects and uses personal health information (PHI) for personal health services and personalized healthcare. As a result, pHealth services use intensively ICT technology, sensors, computers and mathematical algorithms. In past, pHealth applications were focused to certain health or sickness related problem, but in today they use mobile devices, wireless networks, Web-technology and Cloud platforms. In future, pHealth uses information systems that are highly distributed, dynamic, increasingly autonomous, multi-stakeholder data driven eco-system having ability to monitor anywhere person's regular life, movements and health related behaviours. Because privacy and trust are pre-requirements for successful pHealth, this development raises huge privacy and trust challenges to be solved. Researchers have shown that current privacy approaches and solutions used in pHealth do not offer acceptable level of privacy, and trust is only an illusion. This indicates, that today's privacy models and technology shall not be moved to the future pHealth. The authors have analysed interesting new privacy and trust ideas published in journals, and found that they seem to be effective but offer only a partial solution. To solve this weakness, the authors used a holistic system view to aspects impacting privacy and trust in pHealth, and created a template that can be used in planning and development future pHealth services. The authors also propose a tentative solution for future trustworthy pHealth. It combines privacy as personal property and trust as legal binding fiducial duty approaches, and uses a Blockchain-based smart contract solution to store person's privacy and trust requirements and service providers' promises.

Comparative Analysis between Individual, Centralized, and Federated Learning for Smartwatch Based Stress Detection.

Machine learning has been proven to provide good performances on stress detection tasks using multi-modal sensor data from a smartwatch. Generally, machine learning techniques need a sufficient amount of data to train a robust model. Thus, we need to collect data from several users and send them to a central server to feed the algorithm. However, the uploaded data may contain sensitive information that can jeopardize the user's privacy. Federated learning can tackle this challenge by enabling the model to be trained using data from all users without the user's data leaving the user's device. In this study, we implement federated learning-based stress detection and provide a comparative analysis between individual, centralized, and federated learning. The experiment was conducted on WESAD dataset by using Logistic Regression as the classifier. The experiment results show that in terms of accuracy, federated learning cannot reach the performance level of both individual and centralized learning. The individual learning strategy performs best with an average accuracy of 0.9998 and an average F1-measure of 0.9996.

Modeling digital health systems to foster interoperability.

Information systems are a complex thing, and they are mostly not used stand-alone anymore. In that context, many different issues must be considered. It starts with defining the system's purpose, includes the use cases and scenarios in combination with the necessary data ideally separated into distinct domains. Furthermore, it requires the selection of an appropriate set of supporting components/tools and a development environment including some technology to enable continuous integration. And the endeavor does not come to an end with the development of the system itself. To manage those challenges, thinking about design and architectural principles becomes a mandatory element. The situation gets more complicated with growing expectations regarding communication and cooperation between the more and more complex and dynamic ecosystem's actors. The resulting information system has to adhere to different, sometimes contradictory principles and requirements, frequently controlled by different authorities. This paper focuses less on developing information systems in general but concentrates on the aspects that must be considered when multiple requirements from different stakeholders for data exchange and knowledge sharing for advanced interoperability must be met. The latter is commonly underspecified due to missing proper verification of the correct interpretation of data. One intent of the paper is to promote the deployment of information models as a common basis to derive data exchange specifications establishing advanced interoperability. However, it also addresses the necessity to guarantee that the information models and implementable artifacts correctly represent the intended functions and objectives as well as the underlying concepts of the business system in its prevailing context. Therefore, we cannot limit our considerations on the data and information viewpoints.

Reinforcing Health Data Sharing through Data Democratization.

In this paper, we propose a health data sharing infrastructure which aims to empower a democratic health data sharing ecosystem. Our project, named Health Democratization (HD), aims at enabling seamless mobility of health data across trust boundaries through addressing structural and functional challenges of its underlying infrastructure with the core concept of data democratization. A programmatic design of an HD platform was elaborated, followed by an introduction of one of our critical designs-a "reverse onus" mechanism that aims to incentivize creditable data accessing behaviors. This scheme shows a promising prospect of enabling a democratic health data-sharing platform.

A NLP Pipeline for the Automatic Extraction of a Complete Microorganism's Picture from Microbiological Notes.

The Italian "Istituto Superiore di Sanità" (ISS) identifies hospital-acquired infections (HAIs) as the most frequent and serious complications in healthcare. HAIs constitute a real health emergency and, therefore, require decisive action from both local and national health organizations. Information about the causative microorganisms of HAIs is obtained from the results of microbiological cultures of specimens collected from infected body sites, but microorganisms' names are sometimes reported only in the notes field of the culture reports. The objective of our work was to build a NLP-based pipeline for the automatic information extraction from the notes of microbiological culture reports. We analyzed a sample composed of 499 texts of notes extracted from 1 month of anonymized laboratory referral. First, our system filtered texts in order to remove nonmeaningful sentences. Thereafter, it correctly extracted all the microorganisms' names according to the expert's labels and linked them to a set of very important metadata such as the translations into national/international vocabularies and standard definitions. As the major result of our pipeline, the system extracts a complete picture of the microorganism.

Privacy and Trust in eHealth: A Fuzzy Linguistic Solution for Calculating the Merit of Service.

The use of eHealth and healthcare services are becoming increasingly common across networks and ecosystems. Identifying the quality and health impact of these services is a big problem that in many cases it is difficult determine. Health ecosystems are seldom designed with privacy and trust in mind, and the service user has almost no way of knowing how much trust to place in the service provider and other stakeholders using his or her personal health information (PHI). In addition, the service user cannot rely on privacy laws, and the ecosystem is not a trustworthy system. This demonstrates that, in real life, the user does not have significant privacy. Therefore, before starting to use eHealth services and subsequently disclosing personal health information (PHI), the user would benefit from tools to measure the level of privacy and trust the ecosystem can offer. For this purpose, the authors developed a solution that enables the service user to calculate a Merit of Service (Fuzzy attractiveness rating (FAR)) for the service provider and for the network where PHI is processed. A conceptual model for an eHealth ecosystem was developed. With the help of heuristic methods and system and literature analysis, a novel proposal to identify trust and privacy attributes focused on eHealth was developed. The FAR value is a combination of the service network's privacy and trust features, and the expected health impact of the service. The computational Fuzzy linguistic method was used to calculate the FAR. For user friendliness, the Fuzzy value of Merit was transformed into a linguistic Fuzzy label. Finally, an illustrative example of FAR calculation is presented.

Assessing the Need for Semantic Data Integration for Surgical Biobanks-A Knowledge Representation Perspective.

To improve patient outcomes after trauma, the need to decrypt the post-traumatic immune response has been identified. One prerequisite to drive advancement in understanding that domain is the implementation of surgical biobanks. This paper focuses on the outcomes of patients with one of two diagnoses: post-traumatic arthritis and osteomyelitis. In creating surgical biobanks, currently, many obstacles must be overcome. Roadblocks exist around scoping of data that is to be collected, and the semantic integration of these data. In this paper, the generic component model and the Semantic Web technology stack are used to solve issues related to data integration. The results are twofold: (a) a scoping analysis of data and the ontologies required to harmonize and integrate it, and (b) resolution of common data integration issues in integrating data relevant to trauma surgery.

EU-Funded Telemedicine Projects - Assessment of, and Lessons Learned From, in the Light of the SARS-CoV-2 Pandemic.

The SARS-CoV-2 health emergency has demonstrated the need for developing structured telemedicine systems to protect citizens from the spread of the virus. Thereby, their importance and the necessity to tailor their diffusion at large scale for providing services both at a distance and in time has been shown. For these reasons, the European Union advocates the digital transition of health systems for the next 5 years. The main aim of this work is to revisit the telemedicine research projects financed by European Community during the period 2000-2020 with particular respect to the results derived from their application. The analysis showed that some integration of tele-care and tele-health could be obtained with tele-monitoring systems and the implementation of Electronic Personal Record (EPR). Furthermore, telemedicine allows enhancing health care in critical environments, to protect health and life of the most vulnerable patients, and to encourage cross-border dialogue. The criteria of "from distance" and "timely delivered" are granted, but the effectiveness of the overall offered services highly depends on the availability and the quality of the input data. Unfortunately, this remains a relevant problem in the SARS-CoV-2 pandemic.