The growing elderly population in developed countries highlights the critical need for preventing frailty, which poses significant challenges to health systems due to increased risks of severe health issues. This paper introduces Senselife, a framework that provides explainable service recommendations specifically tailored for frailty prevention. It begins by outlining the medical context and challenges associated with aging, followed by an overview of existing recommender systems with similar objectives. We detail the integration of three key resources-ROR, RNA, and Data Laregion-within the Senselife framework to represent service supply. The paper explains how service supply is structured and the transformation of available data for use within our recommender engine. We introduce the concept of operational activities derived from the ROR and leverage the capabilities of LLMs to incorporate RNA data into Senselife. Additionally, we illustrate how these services are ultimately compiled into recommended service packages. Finally, the paper concludes by summarizing key findings and suggesting potential directions for future research.

Traditional software development life cycle (SDLC) have long been acknowledged as a practical framework for building and managing software systems. However, when it comes to developing digital twin (DT) in the healthcare domain, the traditional SDLC approach faces significant challenges due to the dynamic nature of healthcare ecosystems and rapid technological developments. This paper proposes augmented SDLC as a methodology for DT development and customises it with additional techniques, technologies, and practices to meet DT characteristics.

Finding the exact gene mutations that cause a genetic disease has been a challenging task. Despite the development in information technology, the task of extracting gene-disease associations has been mainly a manual process. This is a time-consuming process, in which experts extract gene-disease associations from relevant research papers from the literature manually. The main aim of this paper is to develop an automated approach for extracting and classifying gene-disease associations from relevant literature research papers using both natural language processing and machine learning techniques. This paper extracted data from free-text literature research papers and built four different dataset formats to discover an optimal representation. Machine and Deep learning models (NB, KNN, SVM, NN, CNN, and LSTM) with TF-IDF were applied on the built datasets. As a result, the format of the dataset with (Positive and Negative) instances only, was found to be the best representation for extracting gene-disease associations with optimal accuracy between 74% and 91%. For the four dataset representations, Multilayer Neural Networks was able to predict all classes in most experiments with accuracy between 64% and 91%. From the initial results, this work highlights the need for additional work to improve both the performance of these models and the data extraction method to build more accurate and optimal dataset representation.

A virtual mirrored replica of the real-world, has become a new trend in recent years with the advent of Industry 4.0, shows how intelligent digital twins (DT) can unlock busi-ness value. The implementation of DT requires various types of technologies, including the Internet of Things (IoT), cloud computing, artificial intelligence, and others. Today, DTs can be used in various fields, including manufacturing, smart cities and healthcare. It can be used to monitor the real environment, predict its future, control its behavior, and improve its overall performance. Despite the aforementioned advantages of DTs, if not protected, they can also be considered as an open environment for attackers. If attackers take over a DT, they may end up owning the real environment controlled by the DT. This can result in damaging consequences. This paper explores the potential security and privacy threats that may be brought in through DTs. It proposes and presents a threat meta-model of DTs that identifies key security aspects of concern.

There have been numerous attempts to build query generators that compute eligibility criteria (EC) for a clinical trial automatically on repositories of patient data. However, one of the challenging key features of EC is the ability to express and compute complex temporal aspects. Existing EC generators has limited temporal capability and those do rely on underlying database technology to perform temporal reasoning. We propose a model that incorporates temporal features of existing generators. However, it separates the computation of the criteria, and in particular the temporal semantics, from the extraction of clinical data from the database to increase the efficiency of execution. We explain the implementation of this model and in particular its temporal algorithm, which runs in O(n log(n)) time where n is the number of clinical facts stored making it more efficient than existing reported generators, where performance, at best, has been reported to be O(n2). We perform an empirical validation to demonstrate the results.

Jordan lacks accurate registration system for reporting perinatal and neonatal (PNN) deaths, and their causes and social determinants, and therefore accurate measures and quality indicators are lacking. This study aimed to describe the development of an electronic stillbirths and neonatal deaths surveillance system (JSANDS) and asses the perception of potential users about the use of the system before its full implementation. JSANDS was developed as a secure on-line data entry system to collect, organize, analyse, and disseminate data on stillbirths, neonatal deaths and their causes. In addition to stillbirths and neonatal deaths, the system also registers births to use them as a denominator for mortality measures. JSANDS was tested by potential users in four maternity hospitals distributed over the three regions of the country. An online questionnaire was designed using google form and had been sent to a 50 potential users. The online questionnaire included the link and the password to access JSANDS. The respondents were requested to try JSANDS by entering hypothetical data and test it A total of 41 potential users responded and filled the questionnaire with a response rate of 91.1%. Of all respondents, the majority (85.2%) rated the language used in JSANDS as clear to very clear. Almost 61.0% reported that JSANDS is easy to be used and 26.8% reported that JSANDS is moderately easy to be used. The majority of respondents rated the user interface as easy to understand and the navigation through the system as easy. The vast majority rated the sequence of input data as somewhat clear to very clear (95.1%) and rated the screen design and color as good to excellent (83.0%). In conclusion, JSANDS is a valuable tool to completely register stillbirths and neonatal deaths and their causes. Potential users rated JSANDS attribute positively. JSANDS can be scaled-up to include data about maternal mortality and their causes, maternal morbidity, and other reproductive health problems.

The wide use of Social media networks have changed the way patients share their health experiences. They offer valuable information on drugs and their side effects directly from patients. However, extracting useful information from social media sources is very challenging, due to several factors including grammatical and spelling errors, colloquial language, and post length limitation. This paper proposes a deep learning approach for extracting adverse drug reactions from twitter posts. It represents words as a vector of both domain and semantic features utilizing the rich medical terminology. The proposed method is evaluated on adverse drug events (ADRs) in tweets. Results show that the developed approach improves the precision of ADR detection by 15.28% over other state-of-the-art deep learning methods with a comparable recall score on twitter posts.

The notion of Electronic Health Records (EHRs) and healthcare Information Technologies (HITs) have recently been adopted by many countries. EHRs facilitate sharing of patients' data among healthcare providers, improve patient safety, and reduce healthcare delivery costs. However, many healthcare providers are unable to use EHRs and related HITs, as they lake the competencies and skills necessary to use them. To overcome this gap, many initiatives have proposed, in different countries, on one hand to train healthcare providers to effectively use EHRs and on another to integrate the required EHR competencies and skills with undergraduate curricula. HiCure is a European Erasmus+ project that aims to develop competency-based health informatics pathways that can be integrated within undergraduate curricula of IT and healthcare programs. This paper presents a case study-based approach, developed in HiCure, to instill the required skills for more applied effective understanding and use of EHRs in undergraduate multidisciplinary education. It utilises real-life EHR notion and its corresponding HITs, exposes students to real-life situations, for more effective comprehension of related uses, issues, intricacies and challenges for improved educational learning outcomes.

Health informatics has been reported to be a vital sector for the last decade. Many efforts have been implemented to better utilize advancements in health informatics in real life. Educational institutions have worked hard to enhance their programs to develop or incorporate health informatics. However, in Palestine, this field is not widely deployed in health care delivery institution or in educational institutions. One key element of Health Informatics is electronic health records, for which privacy and security are very important factors. The paper proposes a case based learning method in teaching privacy and security issues for Information Technology students. The paper proposes the use of mini case studies instead of a single cases study, each of the mini cases studies concentrates on a set of learning outcomes. Hence, these mini case studies will enable us to better fulfill learning outcomes. The paper firstly describes the selected mini case studies for Privacy and Security issues in health informatics, then, it deeply details the development of a complete case study called applying data security techniques over sensitive Drug management/traceability data, which enables students to gain essential skills to protect the privacy of patients and security of drug data. These case studies will be evaluated once they are implemented and results will be reported after that.

The use of Electronic Medical Information and Health Systems, by both health and IT professionals, support staff and patients, is continually growing. However, to use or develop such systems properly, these users require well-founded training, which has increasingly become part of their formal university education. In this education, a necessary set of health informatics topics need to be covered by lectures for both computer science students, as developers of such systems, and health students, as future users of these systems, to be able to master the needed skills. Within the ERASMUS PLUS HiCure project, which aims to develop an integrated health informatics curricula, this work was conducted to identify the necessary topics and needed skills. To achieve, a literature analysis was conducted to gain a comprehensive insight of existing health informatics curricula and recommendations of both scientific community and professional bodies. This analysis resulted in identifying some key competencies, that students should learn within a health informatics education, to gain the critical skills needed for specifying, evaluating and developing health information systems as well as performing health information management.

A hospital is an ecosystem that includes real-time services that require high human interaction on both resources level (doctor, nurses, etc.) and entities level (patients). Designing, planning, improving and controlling this system can be very challenging due to the system complexity governed by several subjective factors that affect the hospital interrelated functions or services. However, continuously changing health care needs that consistently face hospitals require them to keep continuously improving the efficiency of these services as demand increases and as new services are added. This paper proposes a new methodology that uses the concept of Digital Twin (DT) of hospital services based on Discrete Event Simulation (DES) integrated with health care information systems and Internet of things (IoT) devices. It develops a predictive decision support model that employs real-time services data drawn from these systems and devices. This model enables assessing the efficiency of existing health care delivery systems and evaluating the impact of changes in services without disrupting daily activities of the hospital. The developed model, a digital twin (or a virtual replica of the hospital), simulates a number of key hospital health delivery services, based on relevant data retrieved in real-time. Although the model simulates four key services, initially as a proof of concept, but it proposes a general framework, which can be expanded to include other services. The demonstrated proof-of-concept shows that it achieves better planning and improvement of usage of resources, and thus enabling both practitioners and management to examine any model changes to foresee the effectiveness or efficiency of services before they are applied in reality.

This study used a tailored version of the Technology Acceptance Model (TAM) to assess Jordanian and Palestinian students' attitudes and beliefs toward learning of health informatics (HI), determine their intention to learn, assess the needed HI skills, and determine reasons that could motivate students to study HI. A descriptive, cross-sectional study was conducted among undergraduate senior students in health — oriented disciplines in four universities in Jordan and Palestine. A sample of students was emailed the link to the web-based questionnaire and was voluntarily requested to fill the questionnaire. A total of 891 students responded to the study questionnaire. The majority (82.7%) of students were interested in learning HI. About 62.8% of students reported that they will take a set of HI courses as a pathway in their undergraduate degree if they are given such an opportunity. More than 70% of students perceived HI learning as useful. About three quarters of students had a behavioral intention to enter the HI program and about two thirds (75.4%) reported that they are enthusiastic about the use of health information technology in patient care in future medical practice. Higher ratings on perceived usefulness (r = 0.73), perceived ease of learning and use of HI (r =0.76), attitude toward HI (r =0.83), enabling environment (r = 0.44) were significantly associated with higher behavioral intention to learn HI. In conclusion, students perceived HI learning as useful and easy, had a positive attitude toward HI, and had a high intention to learn HI.

Abstract Quality of Service (QoS) properties play an important role in distinguishing between functionally equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, using the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been used to a limited extent by existing approaches. In response, we propose a context‐aware quality learning model, realized via a learning‐enabled service agent, exploiting the contextual characteristics of the domain to provide more personalized, accurate, and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments.

Distributed cache systems are used to store and retrieve frequently used data for faster access by exploiting the memory of more than one machine, but they appear as one logical big cache. In this paper, we studied the performance of two popular open source distributed cache systems (Hazelcast and Infinispan) indifferently. The conducted performance analysis shows that Infinispan outperforms Hazelcast in the simple data retrieval scenarios as well as most of SQL-like queries scenarios, whereas Hazelcast outperforms Infinispan in SQL-like queries for small data sizes.

In many cases, intermediaries play a major role in linking between service providers and their target users. Yet, attracting intermediaries at a marketplace to promote a service to their existing customers can be very challenging, since they are usually very busy and would incur additional cost as a result of such promotion. In response, this paper presents an information-based incentivisation framework, which combines financial rewards with other motivating information, in order to incentivise intermediaries at a marketplace to undertake service promotion. Specifically, the intermediaries are associated with a group of incentivising agents, capable of learning the individual motivational needs of these intermediaries, and accordingly target them with the most effective incentives. The incentivising agents collaborate with each other to gather motivational information, by sharing their observations on intermediaries. The proposed incentivisation approach is evaluated through a corresponding agent-based simulation, and the experimental results obtained demonstrate its effectiveness.

Pervasive computing applications bring together heterogeneous network-connected devices, services and resources to enable context-aware information integration. The increasing adoption of pervasive computing technology in the healthcare domain offers a healthcare model that delivers high quality service with fewer resources. In this paper, we briefly review the existing pervasive healthcare solutions and propose a novel provenance-aware system design that can enhance the performance of such solutions by means of including provenance capture functionality. We argue that our system architecture can improve quality of clinical data, efficiency of its collection, and its integrating ability with other data sources. To demonstrate our system and explain its provenance capacity, we use a clinical research example in which a patient's condition is closely monitored in order to assess the safety and efficacy of medications and treatments prescribed to him.

OBJECTIVE: Biomedical research increasingly relies on the integration of information from multiple heterogeneous data sources. Despite the fact that structural and terminological aspects of interoperability are interdependent and rely on a common set of requirements, current efforts typically address them in isolation. We propose a unified ontology-based knowledge framework to facilitate interoperability between heterogeneous sources, and investigate if using the LexEVS terminology server is a viable implementation method. MATERIALS AND METHODS: We developed a framework based on an ontology, the general information model (GIM), to unify structural models and terminologies, together with relevant mapping sets. This allowed a uniform access to these resources within LexEVS to facilitate interoperability by various components and data sources from implementing architectures. RESULTS: Our unified framework has been tested in the context of the EU Framework Program 7 TRANSFoRm project, where it was used to achieve data integration in a retrospective diabetes cohort study. The GIM was successfully instantiated in TRANSFoRm as the clinical data integration model, and necessary mappings were created to support effective information retrieval for software tools in the project. CONCLUSIONS: We present a novel, unifying approach to address interoperability challenges in heterogeneous data sources, by representing structural and semantic models in one framework. Systems using this architecture can rely solely on the GIM that abstracts over both the structure and coding. Information models, terminologies and mappings are all stored in LexEVS and can be accessed in a uniform manner (implementing the HL7 CTS2 service functional model). The system is flexible and should reduce the effort needed from data sources personnel for implementing and managing the integration.

A content-centric network is one which supports host-to-content routing, rather than the host-to-host routing of the existing Internet. This paper investigates the potential of caching data at the router-level in content-centric networks. To achieve this, two measurement sets are combined to gain an understanding of the potential caching benefits of deploying content-centric protocols over the current Internet topology. The first set of measurements is a study of the BitTorrent network, which provides detailed traces of content request patterns. This is then combined with CAIDA's ITDK Internet traces to replay the content requests over a real-world topology. Using this data, simulations are performed to measure how effective content-centric networking would have been if it were available to these consumers/providers. We find that larger cache sizes (10,000 packets) can create significant reductions in packet path lengths. On average, 2.02 hops are saved through caching (a 20% reduction), whilst also allowing 11% of data requests to be maintained within the requester's AS. Importantly, we also show that these benefits extend significantly beyond that of edge caching by allowing transit ASes to also reduce traffic.

This article proposes a new delivery-centric abstraction which extends the existing content-centric networking API. A delivery-centric abstraction allows applications to generate content requests agnostic to location or protocol, with the additional ability to stipulate high-level requirements regarding such things as performance, security, and resource consumption. Fulfilling these requirements, however, is complex as often the ability of a provider to satisfy requirements will vary between different consumers and over time. Therefore, we argue that it is vital to manage this variance to ensure an application fulfils its needs. To this end, we present the Juno middleware, which implements delivery-centric support using a reconfigurable software architecture to: (i) discover multiple sources of an item of content; (ii) model each source’s ability to provide the content; then (iii) adapt to interact with the source(s) that can best fulfil the application’s requirements. Juno therefore utilizes existing providers in a backwards compatible way, supporting immediate deployment. This article evaluates Juno using Emulab to validate its ability to adapt to its environment.

With the global distribution of scientific and software engineering skills and with the need to foster multidisciplinary research collaboration across organisations result in teams dispersed separated by time and distance. However to attain the potential benefits of such collaboration, there is a critical need for a better management of communication, knowledge and co-ordination across distributed teams. The importance of these factors is becoming increasingly known to organisations requiring them to develop methods and enabling mechanisms in need for more successful and efficient collaboration outcomes. This paper discusses and emphasises the importance of managing these factors in distributed software engineering projects based on experiences drawn from an international scientific research and software engineering project (ePCRN). It presents their impact on the collaborative process and how they may hinder the progress of the software development process. It also presents the methods and mechanisms used in the project to address some of these factors

Global multidisciplinary scientific research collaborations are increasingly becoming a necessity to create global solutions. However to attain the potential benefits of such collaborations, there is a critical need for a more efficient management and exchange of knowledge between the distributed teams. Unlike traditional software projects, the knowledge management requirements of such teams is much more complex and requires different types of interaction and knowledge management environments to support such collaboration. In such projects, the need is to capture not just software artefacts, but also the scientific research process and its artefacts and their translation to their respective software needs. This paper discusses the knowledge management needs in distributed scientific projects based on experiences drawn from an international research project. It presents the different types of knowledge in such projects and outlines a mechanism to capture them.

OBJECTIVES: Chronic disease prevalence and burden is growing, as is the need for applicable large community-based clinical trials of potential interventions. To support the development of clinical trial management systems for such trials, a community-based primary care research information model is needed. We analyzed the requirements of trials in this environment, and constructed an information model to drive development of systems supporting trial design, execution, and analysis. We anticipate that this model will contribute to a deeper understanding of all the dimensions of clinical research and that it will be integrated with other clinical research modeling efforts, such as the Biomedical Research Integrated Domain Group (BRIDG) model, to complement and expand on current domain models. DESIGN: We used unified modeling language modeling to develop use cases, activity diagrams, and a class (object) model to capture components of research in this setting. The initial primary care research object model (PCROM) scope was the performance of a randomized clinical trial (RCT). It was validated by domain experts worldwide, and underwent a detailed comparison with the BRIDG clinical research reference model. RESULTS: We present a class diagram and associated definitions that capture the components of a primary care RCT. Forty-five percent of PCROM objects were mapped to BRIDG, 37% differed in class and/or subclass assignment, and 18% did not map. CONCLUSION: The PCROM represents an important link between existing research reference models and the real-world design and implementation of systems for managing practice-based primary care clinical trials. Although the high degree of correspondence between PCROM and existing research reference models provides evidence for validity and comprehensiveness, existing models require object extensions and modifications to serve primary care research.