Supporting integrated care with a flexible data management framework built upon Linked Data, HL7 FHIR and ontologies.

In this paper we present the methodology and decisions behind an implementation of a telehealth data management framework, aiming to support integrated care services for chronic and multimorbid patients. The framework leverages an OWL ontology, built upon HL7 FHIR resources, to provide storage and representation of semantically enriched EHR data following Linked Data principles. This is presented along with the realization of the persistent storage solution and communication web services that allow the management of EHR data, ensuring the validity and integrity of the exchanged patient data as self-describing ontology instances. The framework concentrates on flexibility and reusability, which is addressed by regarding the aforementioned ontology as a single point of change. This solution has been implemented in the scope of the EU project WELCOME for managing data in a telemonitoring system for patients with COPD and co-morbidities and was also successfully deployed for the INLIFE EU project with minimal effort. The results of the two applications suggest it can be adopted and properly adapted in a series of integrated care scenarios with minimal effort.

Public data homogenization for AI model development in breast cancer.

BACKGROUND: Developing trustworthy artificial intelligence (AI) models for clinical applications requires access to clinical and imaging data cohorts. Reusing of publicly available datasets has the potential to fill this gap. Specifically in the domain of breast cancer, a large archive of publicly accessible medical images along with the corresponding clinical data is available at The Cancer Imaging Archive (TCIA). However, existing datasets cannot be directly used as they are heterogeneous and cannot be effectively filtered for selecting specific image types required to develop AI models. This work focuses on the development of a homogenized dataset in the domain of breast cancer including clinical and imaging data. METHODS: Five datasets were acquired from the TCIA and were harmonized. For the clinical data harmonization, a common data model was developed and a repeatable, documented "extract-transform-load" process was defined and executed for their homogenization. Further, Digital Imaging and COmmunications in Medicine (DICOM) information was extracted from magnetic resonance imaging (MRI) data and made accessible and searchable. RESULTS: The resulting harmonized dataset includes information about 2,035 subjects with breast cancer. Further, a platform named RV-Cherry-Picker enables search over both the clinical and diagnostic imaging datasets, providing unified access, facilitating the downloading of all study imaging that correspond to specific series' characteristics (e.g., dynamic contrast-enhanced series), and reducing the burden of acquiring the appropriate set of images for the respective AI model scenario. CONCLUSIONS: RV-Cherry-Picker provides access to the largest, publicly available, homogenized, imaging/clinical dataset for breast cancer to develop AI models on top. RELEVANCE STATEMENT: We present a solution for creating merged public datasets supporting AI model development, using as an example the breast cancer domain and magnetic resonance imaging images. KEY POINTS: • The proposed platform allows unified access to the largest, homogenized public imaging dataset for breast cancer. • A methodology for the semantically enriched homogenization of public clinical data is presented. • The platform is able to make a detailed selection of breast MRI data for the development of AI models.

BRACETS: Bimodal repository of auscultation coupled with electrical impedance thoracic signals.

BACKGROUND AND OBJECTIVE: Respiratory diseases are among the most significant causes of morbidity and mortality worldwide, causing substantial strain on society and health systems. Over the last few decades, there has been increasing interest in the automatic analysis of respiratory sounds and electrical impedance tomography (EIT). Nevertheless, no publicly available databases with both respiratory sound and EIT data are available. METHODS: In this work, we have assembled the first open-access bimodal database focusing on the differential diagnosis of respiratory diseases (BRACETS: Bimodal Repository of Auscultation Coupled with Electrical Impedance Thoracic Signals). It includes simultaneous recordings of single and multi-channel respiratory sounds and EIT. Furthermore, we have proposed several machine learning-based baseline systems for automatically classifying respiratory diseases in six distinct evaluation tasks using respiratory sound and EIT (A1, A2, A3, B1, B2, B3). These tasks included classifying respiratory diseases at sample and subject levels. The performance of the classification models was evaluated using a 5-fold cross-validation scheme (with subject isolation between folds). RESULTS: The resulting database consists of 1097 respiratory sounds and 795 EIT recordings acquired from 78 adult subjects in two countries (Portugal and Greece). In the task of automatically classifying respiratory diseases, the baseline classification models have achieved the following average balanced accuracy: Task A1 - 77.9±13.1%; Task A2 - 51.6±9.7%; Task A3 - 38.6±13.1%; Task B1 - 90.0±22.4%; Task B2 - 61.4±11.8%; Task B3 - 50.8±10.6%. CONCLUSION: The creation of this database and its public release will aid the research community in developing automated methodologies to assess and monitor respiratory function, and it might serve as a benchmark in the field of digital medicine for managing respiratory diseases. Moreover, it could pave the way for creating multi-modal robust approaches for that same purpose.

Knowledge engineering for adverse drug event prevention: on the design and development of a uniform, contextualized and sustainable knowledge-based framework.

The primary aim of this work was the development of a uniform, contextualized and sustainable knowledge-based framework to support adverse drug event (ADE) prevention via Clinical Decision Support Systems (CDSSs). In this regard, the employed methodology involved first the systematic analysis and formalization of the knowledge sources elaborated in the scope of this work, through which an application-specific knowledge model has been defined. The entire framework architecture has been then specified and implemented by adopting Computer Interpretable Guidelines (CIGs) as the knowledge engineering formalism for its construction. The framework integrates diverse and dynamic knowledge sources in the form of rule-based ADE signals, all under a uniform Knowledge Base (KB) structure, according to the defined knowledge model. Equally important, it employs the means to contextualize the encapsulated knowledge, in order to provide appropriate support considering the specific local environment (hospital, medical department, language, etc.), as well as the mechanisms for knowledge querying, inference, sharing, and management. In this paper, we present thoroughly the establishment of the proposed knowledge framework by presenting the employed methodology and the results obtained as regards implementation, performance and validation aspects that highlight its applicability and virtue in medication safety.

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.

BACKGROUND: Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient's health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. OBJECTIVE: This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. METHODS: The PubMed search engine was systematically searched by combining "deep learning" and keywords such as "ecg," "ekg," "electrocardiogram," "electrocardiography," and "electrocardiology." Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. RESULTS: We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. CONCLUSIONS: We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

CoCross: An ICT Platform Enabling Monitoring Recording and Fusion of Clinical Information Chest Sounds and Imaging of COVID-19 ICU Patients.

Monitoring and treatment of severely ill COVID-19 patients in the ICU poses many challenges. The effort to understand the pathophysiology and progress of the disease requires high-quality annotated multi-parameter databases. We present CoCross, a platform that enables the monitoring and fusion of clinical information from in-ICU COVID-19 patients into an annotated database. CoCross consists of three components: (1) The CoCross4Pros native android application, a modular application, managing the interaction with portable medical devices, (2) the cloud-based data management services built-upon HL7 FHIR and ontologies, (3) the web-based application for intensivists, providing real-time review and analytics of the acquired measurements and auscultations. The platform has been successfully deployed since June 2020 in two ICUs in Greece resulting in a dynamic unified annotated database integrating clinical information with chest sounds and diagnostic imaging. Until today multisource data from 176 ICU patients were acquired and imported in the CoCross database, corresponding to a five-day average monitoring period including a dataset with 3477 distinct auscultations. The platform is well accepted and positively rated by the users regarding the overall experience.

Toward Systems Models for Obesity Prevention: A Big Role for Big Data.

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.

Information contextualization in decision support modules for adverse drug event prevention.

This paper presents an analysis of hospitals' organization and Hospital Information Systems' features which can contribute in contextualization of Clinical Decision Support Systems (CDSS) for Adverse Drug Event (ADE) prevention. We identified four categories of contextualization: ENVIRONMENT, TASKS, USERS and TEMPORAL ASPECTS. Based on this analysis, we studied the technical possibilities at the architectural level to determine which component(s) of a standalone knowledge platform could technically handle contextualization. The results impact three types of components of this platform: (1) a CDSS providing decision support based on ADE signals mined in large data repositories; (2) a Connectivity Platform providing transformation and routing services (enabling any application to connect to the CDSS); (3) three prototype applications for accessing the decision support services realized within an industrial Computerized Physician Order Entry, an industrial Electronic Health Record and in an independent Web prototype, respectively. In each of the above components we present the dimension(s) of contextualization that has/have been determined to cope with and the design followed in the implementation phase.

A Methodology for an Auto-Generated and Auto-Maintained HL7 FHIR OWL Ontology for Health Data Management.

The process of maintenance of an underlying semantic model that supports data management and addresses the interoperability challenges in the domain of telemedicine and integrated care is not a trivial task when performed manually. We present a methodology that leverages the provided serializations of the Health Level Seven International (HL7) Fast Health Interoperability Resources (FHIR) specification to generate a fully functional OWL ontology along with the semantic provisions for maintaining functionality upon future changes of the standard. The developed software makes a complete conversion of the HL7 FHIR Resources along with their properties and their semantics and restrictions. It covers all FHIR data types (primitive and complex) along with all defined resource types. It can operate to build an ontology from scratch or to update an existing ontology, providing the semantics that are needed, to preserve information described using previous versions of the standard. All the results based on the latest version of HL7 FHIR as a Web Ontology Language (OWL-DL) ontology are publicly available for reuse and extension.

Twelve-Year Incidence of Open-angle Glaucoma: The Thessaloniki Eye Study.

OBJECTIVE: The objective of this study was to determine the 12-year incidence of open-angle glaucoma (OAG), with further classification into primary open-angle glaucoma (POAG) and pseudoexfoliative glaucoma (PEXG), in an elderly White population. DESIGN: A longitudinal, population-based study in urban Northern Greece. PARTICIPANT: Surviving cohort of the 2554 Thessaloniki Eye Study subjects 60 years and above who had the baseline examination. METHODS: The surviving cohort was re-examined 12 years after baseline, using the same methodology and the same standard operating procedures as in the baseline examination. The definitions of glaucoma and pseudoexfoliation were consistent throughout the study. The 12-year incidences of OAG, POAG, and PEXG with corresponding 95% confidence intervals (CIs) were calculated for the whole study population, consisting of clinic-visit and home-visit participants. The population at risk was defined as those who did not meet the study criteria for the diagnosis of glaucoma in either eye at baseline. MAIN OUTCOME MEASURES: Twelve-year incidence of OAG, with further classification into POAG and PEXG. RESULTS: Of 1468 eligible subjects in the surviving cohort, 1092 were examined (participation rate 74%). Mean age at baseline was 68.9±4.6 years. Mean follow-up time was 11.6±1.6 years. The 12-year incidence of OAG was 4.4% (95% CI: 3.3-5.8); 0.37% per year. In the overall population the incidence of POAG and PEXG was 2.1% (95% CI: 1.3-3.2) and 2.3% (95% CI: 1.5-3.4), respectively. The corresponding incidence proportions were 2.9 (95% CI: 1.8-4.3) in those without PEX and 8.9 (95% CI: 5.8-12.9) in those with PEX at baseline and/or incidence. The latter was strongly associated with higher odds for incident glaucoma (odds ratio=3.34, 95% CI: 1.83-6.08, P<0.001). Of all incident OAG cases, 11.1% (95% CI: 4.4-24) had baseline intraocular pressure >21 mm Hg. CONCLUSIONS: The incidence of OAG was similar or higher compared with other White populations. The incidence of glaucoma in those with PEX was higher compared with the incidence of glaucoma in those without PEX.

Serious Gaming Technology in Upper Extremity Rehabilitation: Scoping Review.

BACKGROUND: Serious gaming has increasingly gained attention as a potential new component in clinical practice. Specifically, its use in the rehabilitation of motor dysfunctions has been intensively researched during the past three decades. OBJECTIVE: The aim of this scoping review was to evaluate the current role of serious games in upper extremity rehabilitation, and to identify common methods and practice as well as technology patterns. This objective was approached via the exploration of published research efforts over time. METHODS: The literature search, using the PubMed and Scopus databases, included articles published from 1999 to 2019. The eligibility criteria were (i) any form of game-based arm rehabilitation; (ii) published in a peer-reviewed journal or conference; (iii) introduce a game in an electronic format; (iv) published in English; and (v) not a review, meta-analysis, or conference abstract. The search strategy identified 169 relevant articles. RESULTS: The results indicated an increasing research trend in the domain of serious gaming deployment in upper extremity rehabilitation. Furthermore, differences regarding the number of publications and the game approach were noted between studies that used commercial devices in their rehabilitation systems and those that proposed a custom-made robotic arm, glove, or other devices for the connection and interaction with the game platform. A particularly relevant observation concerns the evaluation of the introduced systems. Although one-third of the studies evaluated their implementations with patients, in most cases, there is the need for a larger number of participants and better testing of the rehabilitation scheme efficiency over time. Most of the studies that included some form of assessment for the introduced rehabilitation game mentioned user experience as one of the factors considered for evaluation of the system. Besides user experience assessment, the most common evaluation method involving patients was the use of standard medical tests. Finally, a few studies attempted to extract game features to introduce quantitative measurements for the evaluation of patient improvement. CONCLUSIONS: This paper presents an overview of a significant research topic and highlights the current state of the field. Despite extensive attempts for the development of gamified rehabilitation systems, there is no definite answer as to whether a serious game is a favorable means for upper extremity functionality improvement; however, this certainly constitutes a supplementary means for motivation. The development of a unified performance quantification framework and more extensive experiments could generate richer evidence and contribute toward this direction.

Factors associated with non-active retinal capillary density as measured with Confocal Scanning Laser Doppler Flowmetry in an elderly population: the Thessaloniki Eye Study (TES).

PURPOSE: To identify factors associated with retinal capillary density as measured with Confocal Scanning Laser Doppler Flowmetry (Heidelberg retina flowmeter (HRF)) in the Thessaloniki Eye Study (TES). METHODS: Participants of the TES (age ≥60 years, cross-sectional population-based study) were assessed for active capillary density in the superior and inferior peripapillary retina using the HRF. Pixel-by-pixel analysis was performed to quantify the percentage of zero flow pixels (ZFPs; surrogate for % retinal area with non-active capillaries). Multivariable regression analyses were performed to assess the association of non-active vascular density with ophthalmic and systemic variables. Glaucoma, late age-related macular degeneration and diabetic retinopathy subjects were excluded. RESULTS: 1189 subjects were included in the analysis. Older age (per year) was associated with higher percentage of ZFP in both the superior (slope estimate (SE)=0.0020) and the inferior (SE=0.0019) peripapillary retina (p<0.0001). History of migraine was associated with lower percentage of ZFP (SE=-0.0166) compared with no history of migraine in the superior peripapillary retina only (p<0.05). Higher intraocular pressure ((IOP) per mm Hg) and height (per cm) were associated with higher percentage of ZFP in the inferior peripapillary retina only (SE=0.0012, p<0.05 and SE=0.0005, p<0.05, respectively). The group consuming vegetables one to three times per week compared with the group consuming vegetables at least once a day had higher percentage of ZFP only in the inferior peripapillary retina (SE=0.0080, p<0.05). CONCLUSION: At a population level, our study revealed associations of older age, higher IOP and taller height with lower active retinal capillary density and of migraine with higher capillary density. Looking further into these associations may provide insight into disease mechanisms.

A knowledge engineering framework towards clinical support for adverse drug event prevention: the PSIP approach.

Adverse Drug Events (ADEs) are currently considered as a major public health issue, endangering patients' safety and causing significant healthcare costs. Several research efforts are currently concentrating on the reduction of preventable ADEs by employing Information Technology (IT) solutions, which aim to provide healthcare professionals and patients with relevant knowledge and decision support tools. In this context, we present a knowledge engineering approach towards the construction of a Knowledge-based System (KBS) regarded as the core part of a CDSS (Clinical Decision Support System) for ADE prevention, all developed in the context of the EU-funded research project PSIP (Patient Safety through Intelligent Procedures in Medication). In the current paper, we present the knowledge sources considered in PSIP and the implications they pose to knowledge engineering, the methodological approach followed, as well as the components defining the knowledge engineering framework based on relevant state-of-the-art technologies and representation formalisms.

A sustainable HL7 FHIR based ontology for PHR data.

One of the most widely acknowledged standards in health informatics is HL7 (Health Level 7 International). HL7 FHIR® (Fast Healthcare Interoperability Resources) is a new HL7 standard for exchanging electronic health data. It builds upon previous HL7 data format standards, but also leverages more modern technical concepts and approaches, aiming to be more developer-friendly. We present a developed ontology that, not only represents the domain entities of a personal health record (PHR) focusing on tele-health and integrated care, but also stores the actual data as instances of the defined ontology classes. Inspired and based on HL7 FHIR we defined a methodology for representing FHIR data types and FHIR resources in OWL and we have extended or restricted the resources to match specific domain needs. Additionally, since HL7 FHIR is a developing standard, we present a methodology for maintaining backward compatibility as the ontology is updated to match the latest definition of the standard. All the effort is represented as an OWL-DL ontology that is publicly available for reuse and extension.

Factors Associated With Retinal Vessel Diameters in an Elderly Population: the Thessaloniki Eye Study.

Purpose: To identify the factors associated with retinal vessel diameters in the population of the Thessaloniki Eye Study. Methods: Cross-sectional population-based study (age ≥ 60 years). Subjects with glaucoma, late age-related macular degeneration, and diabetic retinopathy were excluded from the analyses. Retinal vessel diameters were measured using the IVAN software, and measurements were summarized to central retinal artery equivalent (CRAE), central retinal vein equivalent (CRVE), and arteriole to venule ratio (AVR). Results: The analysis included 1614 subjects. The hypertensive group showed lower values of CRAE (P = 0.033) and AVR (P = 0.0351) compared to the normal blood pressure (BP) group. On the contrary, the group having normal BP under antihypertensive treatment did not have different values compared to the normal BP group. Diastolic BP (per mm Hg) was negatively associated with CRAE (P < 0.0001) and AVR (P < 0.0001), while systolic BP (per mm Hg) was positively associated with CRAE (P = 0.001) and AVR (P = 0.0096). Other factors significantly associated included age, sex, alcohol, smoking, cardiovascular disease history, ophthalmic medication, weight, and IOP; differences were observed in a stratified analysis based on BP medication use. Conclusions: Our study confirms previous reports about the association of age and BP with vessel diameters. The negative correlation between BP and CRAE seems to be guided by the effect of diastolic BP as higher systolic BP is independently associated with higher values of CRAE. The association of BP status with retinal vessel diameters is determined by diastolic BP status in our population. Multiple other factors are also independently associated with retinal vessel diameters.

Gamifying Motion Control Assessments Using Leap Motion Controller.

In rehabilitation, exergames and serious games are widely usedin order to motivate patients in the therapeutic procedure. Patients are asked to modify their incorrect motor patterns or reinforce the proper ones through activity rather than exercise. Interactive applications as such, can have a huge impact on a patient's motivation making repetitive physical exercises into pleasant experiences, thus maximizing the gains of therapy. In this paper we present the design and implementation of a serious game platform based on virtual 3D game environment and leap motion controller for interaction. For each session, achieved goals and response to stimuli is recorded and analyzed. Preliminary analysis results from evaluating the game with healthy subjects are encouraging.

Tolerable rates of visual field progression in a population-based sample of patients with glaucoma.

AIMS: To provide population-based data on the maximum tolerable rate of progression to avoid visual impairment (maxTRoP_VI) and blindness (maxTRoP_BL) from open-angle glaucoma (OAG). METHODS: Participants with OAG in the Thessaloniki Eye Study (cross-sectional, population-based study in a European population) were included in the analysis. Visual impairment was defined as mean deviation (MD) equal to or worse than -12dB and blindness as MD equal to or worse than -24dB. Additional thresholds for visual impairment were tested. For each participant maxTRoP_VI was defined as the rate of progression which would not lead to visual impairment during expected lifetime. MaxTRoP_BL was defined accordingly. Both parameters were calculated for each OAG subject using age, sex, MD and life expectancy data. The eye with the better MD per subject was included in the analysis. RESULTS: Among 135 subjects with OAG, 123 had reliable visual fields and were included in the analysis. The mean age was 73±6 years and the median MD was -3.65±5.28dB. Among those, 69.1% would have a maxTRoP_VI slower than -1dB/year and 18.7% would have a maxTRoP_VI between -1 and -2dB/year. Also, 72.4% would have a maxTRoP_BL slower than -2dB/year. For all tested thresholds for visual impairment, approximately 86% of the OAG study participants would not be able to tolerate a rate of progression equal to or faster than -2dB/year. CONCLUSIONS: The majority of patients with glaucoma in our study would have a maximum tolerable rate of progression slower than -1dB/year in their better eye. Patient-tailored strategies to monitor the visual field are important, but raise the issue of feasibility with regard to the number of visual field tests needed.

Overdiagnosis of open-angle glaucoma in the general population: the Thessaloniki Eye Study.

PURPOSE: To assess the overdiagnosis of open-angle glaucoma (OAG) and to investigate associated factors. METHODS: This was a cross-sectional, population-based study of an urban Caucasian population in northern Greece. Randomly selected subjects ≥60 years (n = 2554) participated in the Thessaloniki Eye Study. The definition of OAG required the presence of structural and functional damage, irrespective of intraocular pressure (IOP). Non-OAG subjects were classified as overdiagnosed with OAG if they had reported at least one of the following (self-reported glaucoma): (i) prior diagnosis of glaucoma, (ii) prior laser for glaucoma, (iii) prior glaucoma surgery. Factors associated with the overdiagnosis of OAG were investigated using a logistic regression model. RESULTS: Of 57 (2.2%) subjects with self-reported glaucoma, 34 (60%) were overdiagnosed with OAG, corresponding to a prevalence of 1.3% (34/2554). In a logistic regression model among non-OAG subjects, worse visual acuity (VA) (20/200 or worse versus 20/25 or better; odds ratio (OR) = 4.30, 95% Confidence Intervals (CI), 1.13-16.35), family history of glaucoma (OR = 8.69, 95% CI, 2.83-26.67) and history of cataract surgery (OR = 11.50, 95% CI, 3.85-34.36) were statistically significantly associated with the overdiagnosis of OAG. Age, sex, higher IOP, higher vertical cup-to-disc ratio and pseudoexfoliation were not statistically significant. CONCLUSION: The overdiagnosis of OAG was substantial in this elderly, Caucasian population. The overdiagnosis of glaucoma has not been previously addressed in population-based studies and needs to be further explored.

Reliability assessment of home health care services.

In this paper, a model of reliability assessment of services in Home Health Care Delivery is presented. Reliability is an important quality dimension for services and is included in non-functional requirements of a system. A stochastic Markov model for reliability assessment is applied to patient communication services, in the field of home health care delivery. The methodology includes the specification of scenarios, the definition of failures in scenarios as well as the application of the analytical model. The results of the methodology reveal the critical states of the Home Health Care System and recommendations for improvement of the services are proposed. The model gives valuable results in predicting service reliability and, independently of the error types, it can be applied to all fields of Regional Health Network (RHN).

Integrated Care and Connected Health Approaches Leveraging Personalised Health through Big Data Analytics.

Integrated care and connected health are two fast evolving concepts that have the potential to leverage personalised health. From the one side, the restructuring of care models and implementation of new systems and integrated care programs providing coaching and advanced intervention possibilities, enable medical decision support and personalized healthcare services. From the other side, the connected health ecosystem builds the means to follow and support citizens via personal health systems in their everyday activities and, thus, give rise to an unprecedented wealth of data. These approaches are leading to the deluge of complex data, as well as in new types of interactions with and among users of the healthcare ecosystem. The main challenges refer to the data layer, the information layer, and the output of information processing and analytics. In all the above mentioned layers, the primary concern is the quality both in data and information, thus, increasing the need for filtering mechanisms. Especially in the data layer, the big biodata management and analytics ecosystem is evolving, telemonitoring is a step forward for data quality leverage, with numerous challenges still left to address, partly due to the large number of micro-nano sensors and technologies available today, as well as the heterogeneity in the users' background and data sources. This leads to new R&D pathways as it concerns biomedical information processing and management, as well as to the design of new intelligent decision support systems (DSS) and interventions for patients. In this paper, we illustrate these issues through exemplar research targeting chronic patients, illustrating the current status and trends in PHS within the integrated care and connected care world.