Pantelis Natsiavas

WELCOME — Innovative integrated care platform using wearable sensing and smart cloud computing for COPD patients with Comorbidities

We propose WELCOME, an innovative integrated care platform using wearable sensors and smart cloud computing for Chronic Obstructive Pulmonary Disease (COPD) patients with co-morbidities. WELCOME aims to bring about a change in the reactive nature of the management of chronic diseases and its comorbidities, in particular through the development of a patient centred and proactive approach to COPD management. The aim of WELCOME is to support healthcare services to give early detection of complications (potentially reducing hospitalisations) and the prevention and mitigation of comorbidities (Heart Failure, Diabetes, Anxiety and Depression). The system incorporates patient hub, where it interacts with the patient via a light vest including a large number of non-invasive chest sensors for monitoring various relevant parameters. In addition, interactive applications to monitor and manage diabetes, anxiety and lifestyle issues will be provided to the patient. Informal carers will also be supported in dealing with their patients. On the other hand, welcome smart cloud platform is the heart of the proposed system where all the medical records and the monitoring data are managed and processed via the decision support system. Healthcare professionals will be able to securely access the WELCOME applications to monitor and manage the patients conditions and respond to alerts on personalized level.

Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data.

BackgroundIntegrating multiple sources of pharmacovigilance evidence has the potential to advance the science of safety signal detection and evaluation. In this regard, there is a need for more research on how to integrate multiple disparate evidence sources while making the evidence computable from a knowledge representation perspective (i.e., semantic enrichment). Existing frameworks suggest well-promising outcomes for such integration but employ a rather limited number of sources. In particular, none have been specifically designed to support both regulatory and clinical use cases, nor have any been designed to add new resources and use cases through an open architecture. This paper discusses the architecture and functionality of a system called Large-scale Adverse Effects Related to Treatment Evidence Standardization (LAERTES) that aims to address these shortcomings.ResultsLAERTES provides a standardized, open, and scalable architecture for linking evidence sources relevant to the association of drugs with health outcomes of interest (HOIs). Standard terminologies are used to represent different entities. For example, drugs and HOIs are represented in RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms respectively. At the time of this writing, six evidence sources have been loaded into the LAERTES evidence base and are accessible through prototype evidence exploration user interface and a set of Web application programming interface services. This system operates within a larger software stack provided by the Observational Health Data Sciences and Informatics clinical research framework, including the relational Common Data Model for observational patient data created by the Observational Medical Outcomes Partnership. Elements of the Linked Data paradigm facilitate the systematic and scalable integration of relevant evidence sources.ConclusionsThe prototype LAERTES system provides useful functionality while creating opportunities for further research. Future work will involve improving the method for normalizing drug and HOI concepts across the integrated sources, aggregated evidence at different levels of a hierarchy of HOI concepts, and developing more advanced user interface for drug-HOI investigations.Background Integrating multiple sources of pharmacovigilance evidence has the potential to advance the science of safety signal detection and evaluation. In this regard, there is a need for more research on how to integrate multiple disparate evidence sources while making the evidence computable from a knowledge representation perspective (i.e., semantic enrichment). Existing frameworks suggest well-promising outcomes for such integration but employ a rather limited number of sources. In particular, none have been specifically designed to support both regulatory and clinical use cases, nor have any been designed to add new resources and use cases through an open architecture. This paper discusses the architecture and functionality of a system called Large-scale Adverse Effects Related to Treatment Evidence Standardization (LAERTES) that aims to address these shortcomings. Results LAERTES provides a standardized, open, and scalable architecture for linking evidence sources relevant to the association of drugs with health outcomes of interest (HOIs). Standard terminologies are used to represent different entities. For example, drugs and HOIs are represented in RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms respectively. At the time of this writing, six evidence sources have been loaded into the LAERTES evidence base and are accessible through prototype evidence exploration user interface and a set of Web application programming interface services. This system operates within a larger software stack provided by the Observational Health Data Sciences and Informatics clinical research framework, including the relational Common Data Model for observational patient data created by the Observational Medical Outcomes Partnership. Elements of the Linked Data paradigm facilitate the systematic and scalable integration of relevant evidence sources. Conclusions The prototype LAERTES system provides useful functionality while creating opportunities for further research. Future work will involve improving the method for normalizing drug and HOI concepts across the integrated sources, aggregated evidence at different levels of a hierarchy of HOI concepts, and developing more advanced user interface for drug-HOI investigations.

A Collision Detection and Resolution Multi Agent Approach Using Utility Functions

Free Flight is the concept introduced by NASA and FAA in order to change the aviation of the 21st century, allowing for pilots to choose dynamically (“on the fly”) their nominal paths. Despite its many advantages as opposed to today’s situation, the free flight concept raises many new issues that need to be addressed before being applicable, with the main interest focusing on conflict detection and resolution (CD&R), in order to ensure the safety of the aircrafts. In this paper we present a decentralized CD&R approach using agents, as well as a general multi-agent framework where not only the proposed but new agent-based approaches may be implemented and tested.

Evaluation of Linked, Open Data Sources for Mining Adverse Drug Reaction Signals

Linked Data is an emerging paradigm of publishing data in the Internet, accompanied with semantic annotations in a machine understandable fashion. The Internet provides vast data, useful in identifying Public Health trends, e.g. concerning the use of drugs, or the spread of diseases. Current practice of exploiting such data includes their combination from different sources, in order to reinforce their exploitation potential, based on unstructured data management practices and the Linked Data paradigm. In this paper, we present the design, the challenges and an evaluation of a Linked Data model to be used in the context of a platform exploiting social media and bibliographic data sources (namely, Twitter and PubMed), focusing on the application of Adverse Drug Reaction (ADR) signal identification. More specifically, we present the challenges of exploiting Bio2RDF as a Linked Open Data source in this respect, focusing on collecting, updating and normalizing data with the ultimate goal of identifying ADR signals, and evaluate the presented model against three reference evaluation datasets.

A Public Health Surveillance Platform Exploiting Free-Text Sources via Natural Language Processing and Linked Data: Application in Adverse Drug Reaction Signal Detection Using PubMed and Twitter

This paper presents a platform enabling the systematic exploitation of diverse, free-text data sources for public health surveillance applications. The platform relies on Natural Language Processing (NLP) and a micro-services architecture, utilizing Linked Data as a data representational formalism. In order to perform NLP in an extendable and modular fashion, the proposed platform employs the Apache Unstructured Information Management Architecture (UIMA) and semantically annotates the results through a newly developed UIMA Semantic Common Analysis Structure Consumer (SCC). The SCC output is a graph represented in the Resource Description Framework (RDF) based on the W3C Web Annotation Data Model (WADM) and SNOMED-CT. We also present the use of the proposed platform through an exemplar application scenario concerning the detection of adverse drug reaction (ADR) signals using data retrieved from PubMed and Twitter.

OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies

Signal detection and management is a key activity in pharmacovigilance (PV). When a new PV signal is identified, the respective information is publicly communicated in the form of periodic newsletters or reports by organizations that monitor and investigate PV-related information (such as the World Health Organization and national PV centers). However, this type of communication does not allow for systematic access, discovery and explicit data interlinking and, therefore, does not facilitate automated data sharing and reuse. In this paper, we present OpenPVSignal, a novel ontology aiming to support the semantic enrichment and rigorous communication of PV signal information in a systematic way, focusing on two key aspects: (a) publishing signal information according to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles, and (b) exploiting automatic reasoning capabilities upon the interlinked PV signal report data. OpenPVSignal is developed as a reusable, extendable and machine-understandable model based on Semantic Web standards/recommendations. In particular, it can be used to model PV signal report data focusing on: (a) heterogeneous data interlinking, (b) semantic and syntactic interoperability, (c) provenance tracking and (d) knowledge expressiveness. OpenPVSignal is built upon widely-accepted semantic models, namely, the provenance ontology (PROV-O), the Micropublications semantic model, the Web Annotation Data Model (WADM), the Ontology of Adverse Events (OAE) and the Time ontology. To this end, we describe the design of OpenPVSignal and demonstrate its applicability as well as the reasoning capabilities enabled by its use. We also provide an evaluation of the model against the FAIR data principles. The applicability of OpenPVSignal is demonstrated by using PV signal information published in: (a) the World Health Organizations Pharmaceuticals Newsletter, (b) the Netherlands Pharmacovigilance Centre Lareb Web site and (c) the U.S. Food and Drug Administration (FDA) Drug Safety Communications, also available on the FDA Web site.

A data model to support the evaluation of coordinating care EU programmes in the context of the ACT programme

Advancing Care Coordination and Telehealth Deployment (ACT) is a European Union (EU) project, completed last October, which has developed a framework for evaluating and improving pioneering health care programs regarding coordinating care and telehealth (CC & TH) across specific EU regions. In this paper we present the key design decisions of the projects data model and the challenges faced. We focus on the definition of the multi-dimensional indicators in order to overcome data incompleteness and heterogeneity issues. Finally, we also suggest a graph based approach that could facilitate development of such data models in similar projects.

Clinical flows and decision support systems for co-ordinated and integrated care in COPD

WELCOME project aims at the development of a technical solution that will leverage integrated care of COPD and comorbidities via wearable technologies, user applications and cloud computing. This work focuses on the latter part and proposes technological approaches that will enable standardized cloud based data management and analytics in a challenging multimorbid health scenario. WELCOME cloud approach encompasses the data model and semantics binding to HL7-FHIR, the persistent data storage for performance and scalability, and the orchestration in a loose coupling with the feature extraction services. While DSS is currently being built on existing clinical knowledge combining the calculated features, the richness of new features and their solid semantics will enable the generation of new knowledge towards a learning health system. This can leverage coordinated care for the management of COPD and comorbidities.

Design of a RESTful middleware to enable a web of medical things

In this paper, we consider the design methodology of a mobile patient hub for the remote self-management of COPD patients. The patient hub design forms a part of the WELCOME system. WELCOME is a current EU project that aims to design and develop a new mobile health system to provide integrated care for COPD patients with comorbidities. The approach adopted for this research is based on the Web of Things architecture with RESTful principles as the enabler of communications. The proposed patient hub architecture design is based on three layers: an application layer, a middleware layer and the sensors layer. This paper presents the detail of the initial design of the middleware and an analysis of the architecture in the context of the systems requirements.