Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system

Exploring the Capacity of Open, Linked Data Sources to Assess Adverse Drug Reaction Signals

Abstract. In this work, we explore the capacity of open, linked data sources to assess adverse drug reaction (ADR) signals. Our study is based on a set of drugrelated Bio2RDF data sources and three reference datasets, containing both positive and negative ADR signals, which were used for benchmarking. We present the overall approach for this assessment and refer to some early findings based on the analysis performed so far

Applying Digital Health in Cancer and Palliative Care in Europe : Policy Recommendations from an International Expert Workshop (MyPal Project)

Background: Digital health interventions are becoming increasingly important for adults, children, and young people with cancer and palliative care needs, but there is little research to guide policy and practice. Objectives: To identify recommendations for policy development of digital health interventions in cancer and palliative care. Design: Expert elicitation workshop. Setting: European clinical (cancer and palliative care, adult and pediatric), policy, technical, and research experts attended a one-day workshop in London, England, in October 2022, along with MyPal research consortium members. Methods: As part of the European Commission-funded MyPal project, we elicited experts' views on global, national, and institutional policies within structured facilitated groups, and conducted qualitative analysis on these discussions. Results/Implementation: Thirty-two experts from eight countries attended. Key policy drivers and levers in digital health were highlighted. Global level: global technology regulation, definitions, access to information technology, standardizing citizens' rights and data safety, digital infrastructure and implementation guidance, and incorporation of technology into existing health systems. National level: country-specific policy, compatibility of health apps, access to digital infrastructure including vulnerable groups and settings, development of guidelines, and promoting digital literacy. Institutional level: undertaking a needs assessment of service users and clinicians, identifying best practice guidelines, providing education and training for clinicians on digital health and safe digital data sharing, implementing plans to minimize barriers to accessing digital health care, minimizing bureaucracy, and providing technical support. Conclusions: Developers and regulators of digital health interventions may find the identified recommendations useful in guiding policy making and future research initiatives. MyPal child study Clinical Trial Registration NCT04381221; MyPal adult study Clinical Trial Registration NCT0437045

Developing an infrastructure for secure patient summary exchange in the EU context: Lessons learned from the KONFIDO project:

Background: The increase of healthcare digitalization comes along with potential information security risks. Thus, the EU H2020 KONFIDO project aimed to provide a toolkit supporting secure cross-border health data exchange. Methods: KONFIDO focused on the so-called "User Goals", while also identifying barriers and facilitators regarding eHealth acceptance. Key user scenarios were elaborated both in terms of threat analysis and legal challenges. Moreover, KONFIDO developed a toolkit aiming to enhance the security of OpenNCP, the reference implementation framework. Results: The main project outcomes are highlighted and the "Lessons Learned," the technical challenges and the EU context are detailed. Conclusions: The main "Lessons Learned" are summarized and a set of recommendations is provided, presenting the position of the KONFIDO consortium toward a robust EU-wide health data exchange infrastructure. To this end, the lack of infrastructure and technical capacity is highlighted, legal and policy challenges are identified and the need to focus on usability and semantic interoperability is emphasized. Regarding technical issues, an emphasis on transparent and standards-based development processes is recommended, especially for landmark software projects. Finally, promoting mentality change and knowledge dissemination is also identified as key step toward the development of secure cross-border health data exchange services

Comprehensive user requirements engineering methodology for secure and interoperable health data exchange

Background: Increased digitalization of healthcare comes along with the cost of cybercrime proliferation. This results to patients' and healthcare providers' skepticism to adopt Health Information Technologies (HIT). In Europe, this shortcoming hampers efficient cross-border health data exchange, which requires a holistic, secure and interoperable framework. This study aimed to provide the foundations for designing a secure and interoperable toolkit for cross-border health data exchange within the European Union (EU), conducted in the scope of the KONFIDO project. Particularly, we present our user requirements engineering methodology and the obtained results, driving the technical design of the KONFIDO toolkit. Methods: Our methodology relied on four pillars: (a) a gap analysis study, reviewing a range of relevant projects/initiatives, technologies as well as cybersecurity strategies for HIT interoperability and cybersecurity; (b) the definition of user scenarios with major focus on cross-border health data exchange in the three pilot countries of the project; (c) a user requirements elicitation phase containing a threat analysis of the business processes entailed in the user scenarios, and (d) surveying and discussing with key stakeholders, aiming to validate the obtained outcomes and identify barriers and facilitators for HIT adoption linked with cybersecurity and interoperability. Results: According to the gap analysis outcomes, full adherence with information security standards is currently not universally met. Sustainability plans shall be defined for adapting existing/evolving frameworks to the state-of-the-art. Overall, lack of integration in a holistic security approach was clearly identified. For each user scenario, we concluded with a comprehensive workflow, highlighting challenges and open issues for their application in our pilot sites. The threat analysis resulted in a set of 30 user goals in total, documented in detail. Finally, indicative barriers of HIT acceptance include lack of awareness regarding HIT risks and legislations, lack of a security-oriented culture and management commitment, as well as usability constraints, while important facilitators concern the adoption of standards and current efforts for a common EU legislation framework. Conclusions: Our study provides important insights to address secure and interoperable health data exchange, while our methodological framework constitutes a paradigm for investigating diverse cybersecurity-related risks in the health sector

Nouvelles méthodes pour soutenir la Pharmacovigilance Active dans l'environnement clinique

Drug safety is a critical issue causing significant public health and financial burden. Pharmacovigilance (PV) is the science related with all aspects of drug safety, focusing on the post-marketing drug safety. Typically, PV activities are not supported by specific tools systematically developed to this purpose. Active Pharmacovigilance (AP) is a new paradigm aiming to increase the PV data space via the active pursue of data which could in principle be produced for another main purpose, but they could also potentially be useful for PV as a secondary use. Technically, the exploitation of these data sources requires the use of Knowledge Engineering (KE) computational approaches, aiming to integrate, mine and semantically align the respective data. The challenges of integrating the use of these KE and potentially other “intelligent” technical paradigms as well as data sources in the clinical environment and beyond that are evident. Revisiting the AP paradigm towards focusing on the exploitation of emerging data sources and technologies is necessary in order to improve medical practice, patient safety and clinical research. This thesis aims to redefine AP based on the opportunities provided by the potential of integrating emerging data sources and “intelligent” technologies. More specifically, it emphasizes on the legal and organizational aspects, elaborating the related “Business Processes” (BPs) and the respective “User Goals” (UGs) and their value in updating the AP paradigm. To this end, a systematic review of the research papers focusing on the use of KE for drug safety has been conducted, identifying the technical approaches and the data sources used, while also investigating potential technical and research gaps. Based on these findings a number of activities has been launched, aiming to support the integration of these data sources and technical approaches in the clinical context and beyond. The regulatory context has been analysed, the respective BPs were identified and elaborated and finally a well-defined set of “User-Goals” has been produced and the respective challenges on the integration of “intelligent” technologies were elaborated. Ultimately, based on these findings, the vision of integrating AP as part of a “Learning Healthcare System” is presented. Finally, a set of technical research lines were also elaborated, providing clear pathways for future research initiatives. More specifically, a Knowledge Graph using PV data is currently under construction, based on PV signal report information published by Uppsala Monitoring Centre, the World Health Organization reference centre for PV. Furthermore, emphasizing on the potential use of Systems Pharmacology oriented data, an ontology enabling the semantic modelling of biochemical pathway information aligned with Systems Theory concepts has also been designed.La sécurité des médicaments (DS) est un problème de santé publique important, car les effets indésirables des médicaments (EIM) entraînent un fardeau de santé publique important. La pharmacovigilance (PV) est définie comme «la science et les activités liées à la détection, à l'évaluation, à la compréhension et à la prévention des effets indésirables ou de tout autre problème éventuel lié aux médicaments ». Les progrès des technologies de l'information et de la communication (TIC) permettent l'utilisation de nouvelles sources de données émergentes, généralement construites à d'autres fins principales, élargissant l'espace de preuves du monde réel utilisé pour rechercher de nouveaux signaux PV potentiels. Ces sources de données émergentes pourraient améliorer considérablement l'identification et l'élaboration de signaux PV potentiels, en complément des preuves produites par l'analyse des ICSR. Ainsi, la pharmacovigilance active (PA) va au-delà de l'utilisation des bases de données ICSR et peut être définie comme «un processus systématique qui cherche à identifier les problèmes de sécurité grâce à des analyses épidémiologiques des bases de données de soins de santé», faisant généralement référence à l'exploitation des dossiers patients (DP) dans les systèmes d’information hospitaliers ou des bases de données d'observation, s'étendant parfois également à d'autres types de sources de données. L'objectif principal de cette thèse est de définir clairement le concept de pharmacovigilance active et de le réviser, en mettant l'accent sur l'impact potentiel des approches d'ingénierie des connaissances et en identifiant les étapes nécessaires en termes de «feuille de route»

Nouvelles méthodes pour soutenir la Pharmacovigilance Active dans l'environnement clinique

La sécurité des médicaments (DS) est un problème de santé publique important, car les effets indésirables des médicaments (EIM) entraînent un fardeau de santé publique important. La pharmacovigilance (PV) est définie comme «la science et les activités liées à la détection, à l'évaluation, à la compréhension et à la prévention des effets indésirables ou de tout autre problème éventuel lié aux médicaments ». Les progrès des technologies de l'information et de la communication (TIC) permettent l'utilisation de nouvelles sources de données émergentes, généralement construites à d'autres fins principales, élargissant l'espace de preuves du monde réel utilisé pour rechercher de nouveaux signaux PV potentiels. Ces sources de données émergentes pourraient améliorer considérablement l'identification et l'élaboration de signaux PV potentiels, en complément des preuves produites par l'analyse des ICSR. Ainsi, la pharmacovigilance active (PA) va au-delà de l'utilisation des bases de données ICSR et peut être définie comme «un processus systématique qui cherche à identifier les problèmes de sécurité grâce à des analyses épidémiologiques des bases de données de soins de santé», faisant généralement référence à l'exploitation des dossiers patients (DP) dans les systèmes d’information hospitaliers ou des bases de données d'observation, s'étendant parfois également à d'autres types de sources de données. L'objectif principal de cette thèse est de définir clairement le concept de pharmacovigilance active et de le réviser, en mettant l'accent sur l'impact potentiel des approches d'ingénierie des connaissances et en identifiant les étapes nécessaires en termes de «feuille de route».Drug safety is a critical issue causing significant public health and financial burden. Pharmacovigilance (PV) is the science related with all aspects of drug safety, focusing on the post-marketing drug safety. Typically, PV activities are not supported by specific tools systematically developed to this purpose. Active Pharmacovigilance (AP) is a new paradigm aiming to increase the PV data space via the active pursue of data which could in principle be produced for another main purpose, but they could also potentially be useful for PV as a secondary use. Technically, the exploitation of these data sources requires the use of Knowledge Engineering (KE) computational approaches, aiming to integrate, mine and semantically align the respective data. The challenges of integrating the use of these KE and potentially other “intelligent” technical paradigms as well as data sources in the clinical environment and beyond that are evident. Revisiting the AP paradigm towards focusing on the exploitation of emerging data sources and technologies is necessary in order to improve medical practice, patient safety and clinical research. This thesis aims to redefine AP based on the opportunities provided by the potential of integrating emerging data sources and “intelligent” technologies. More specifically, it emphasizes on the legal and organizational aspects, elaborating the related “Business Processes” (BPs) and the respective “User Goals” (UGs) and their value in updating the AP paradigm. To this end, a systematic review of the research papers focusing on the use of KE for drug safety has been conducted, identifying the technical approaches and the data sources used, while also investigating potential technical and research gaps. Based on these findings a number of activities has been launched, aiming to support the integration of these data sources and technical approaches in the clinical context and beyond. The regulatory context has been analysed, the respective BPs were identified and elaborated and finally a well-defined set of “User-Goals” has been produced and the respective challenges on the integration of “intelligent” technologies were elaborated. Ultimately, based on these findings, the vision of integrating AP as part of a “Learning Healthcare System” is presented. Finally, a set of technical research lines were also elaborated, providing clear pathways for future research initiatives. More specifically, a Knowledge Graph using PV data is currently under construction, based on PV signal report information published by Uppsala Monitoring Centre, the World Health Organization reference centre for PV. Furthermore, emphasizing on the potential use of Systems Pharmacology oriented data, an ontology enabling the semantic modelling of biochemical pathway information aligned with Systems Theory concepts has also been designed

Nouvelles méthodes pour soutenir la Pharmacovigilance Active dans l'environnement clinique

Drug safety is a critical issue causing significant public health and financial burden. Pharmacovigilance (PV) is the science related with all aspects of drug safety, focusing on the post-marketing drug safety. Typically, PV activities are not supported by specific tools systematically developed to this purpose. Active Pharmacovigilance (AP) is a new paradigm aiming to increase the PV data space via the active pursue of data which could in principle be produced for another main purpose, but they could also potentially be useful for PV as a secondary use. Technically, the exploitation of these data sources requires the use of Knowledge Engineering (KE) computational approaches, aiming to integrate, mine and semantically align the respective data. The challenges of integrating the use of these KE and potentially other “intelligent” technical paradigms as well as data sources in the clinical environment and beyond that are evident. Revisiting the AP paradigm towards focusing on the exploitation of emerging data sources and technologies is necessary in order to improve medical practice, patient safety and clinical research. This thesis aims to redefine AP based on the opportunities provided by the potential of integrating emerging data sources and “intelligent” technologies. More specifically, it emphasizes on the legal and organizational aspects, elaborating the related “Business Processes” (BPs) and the respective “User Goals” (UGs) and their value in updating the AP paradigm. To this end, a systematic review of the research papers focusing on the use of KE for drug safety has been conducted, identifying the technical approaches and the data sources used, while also investigating potential technical and research gaps. Based on these findings a number of activities has been launched, aiming to support the integration of these data sources and technical approaches in the clinical context and beyond. The regulatory context has been analysed, the respective BPs were identified and elaborated and finally a well-defined set of “User-Goals” has been produced and the respective challenges on the integration of “intelligent” technologies were elaborated. Ultimately, based on these findings, the vision of integrating AP as part of a “Learning Healthcare System” is presented. Finally, a set of technical research lines were also elaborated, providing clear pathways for future research initiatives. More specifically, a Knowledge Graph using PV data is currently under construction, based on PV signal report information published by Uppsala Monitoring Centre, the World Health Organization reference centre for PV. Furthermore, emphasizing on the potential use of Systems Pharmacology oriented data, an ontology enabling the semantic modelling of biochemical pathway information aligned with Systems Theory concepts has also been designed.La sécurité des médicaments (DS) est un problème de santé publique important, car les effets indésirables des médicaments (EIM) entraînent un fardeau de santé publique important. La pharmacovigilance (PV) est définie comme «la science et les activités liées à la détection, à l'évaluation, à la compréhension et à la prévention des effets indésirables ou de tout autre problème éventuel lié aux médicaments ». Les progrès des technologies de l'information et de la communication (TIC) permettent l'utilisation de nouvelles sources de données émergentes, généralement construites à d'autres fins principales, élargissant l'espace de preuves du monde réel utilisé pour rechercher de nouveaux signaux PV potentiels. Ces sources de données émergentes pourraient améliorer considérablement l'identification et l'élaboration de signaux PV potentiels, en complément des preuves produites par l'analyse des ICSR. Ainsi, la pharmacovigilance active (PA) va au-delà de l'utilisation des bases de données ICSR et peut être définie comme «un processus systématique qui cherche à identifier les problèmes de sécurité grâce à des analyses épidémiologiques des bases de données de soins de santé», faisant généralement référence à l'exploitation des dossiers patients (DP) dans les systèmes d’information hospitaliers ou des bases de données d'observation, s'étendant parfois également à d'autres types de sources de données. L'objectif principal de cette thèse est de définir clairement le concept de pharmacovigilance active et de le réviser, en mettant l'accent sur l'impact potentiel des approches d'ingénierie des connaissances et en identifiant les étapes nécessaires en termes de «feuille de route»

OpenPVSignal Knowledge Graph: An openly available data source for pharmacovigilance signal reports

Pharmacovigilance (PV) Signal Reports (SRs) are the consolidation of numerous Individual Case Safety Reports (ICSRs) by experts for the early detection of causal relationships between Drugs and Adverse Drug Reactions using statistical correlations. These reports currently exist in a format not useable by Information and Communications Technology (ICT) systems. OpenPVSignal model was an effort to bridge the gap between the SRs and ICTs by converting them to OWL/RDF format. This paper presents the resulting data from the conversion of 108 SRs using OpenPVSignal as the base data model

OMOP-CDM mapping to RDF/OWL: Attempting to bridge the OHDSI ecosystem and the Semantic Web world

Utilizing Real-World Data (RWD) for secondary use is still an open issue. Initiatives like OHDSI aim to tackle it by introducing a common data model (OMOP-CDM) to which data providers can opt to convert their data. While OMOP-CDM supports data interoperability and maintains a degree of intertwined terminologies/vocabularies, does not utilize the benefits of the Semantic Web technical paradigm. This paper presents an effort to convert the OMOP-CDM to RDF format to further enhance its linked data capabilities