AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2023
Su, Qiqi; Peretokin, Vadim; Basdekis, Ioannis; Kouris, Ioannis; Maggesi, Jonatan; Sicuranza, Mario; Acebes, Alberto; Bucur, Anca; Mukkala, Vinod Jaswanth Roy; Pozdniakov, Konstantin; Kloukinas, Christos; Koutsouris, Dimitrios D.; Iliadou, Elefteria; Leontsinis, Ioannis; Gallo, Luigi; Pietro, Giuseppe De; Spanoudakis, George
The SMART BEAR Project: An Overview of Its Infrastructure Proceedings Article
In: Maciaszek, Leszek A.; Mulvenna, Maurice D.; Ziefle, Martina (Ed.): Information and Communication Technologies for Ageing Well and E-Health, pp. 408–425, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-37496-8.
Abstract | Links | BibTeX | Tags: Ageing, AI, Balance Disorder, Cardiovascular Disease, Cloud, Evidence-based, GDPR, Healthcare, Hearing Loss, HL7 FHIR, Semantic interoperability
@inproceedings{suSMARTBEARProject2023,
title = {The SMART BEAR Project: An Overview of Its Infrastructure},
author = {Qiqi Su and Vadim Peretokin and Ioannis Basdekis and Ioannis Kouris and Jonatan Maggesi and Mario Sicuranza and Alberto Acebes and Anca Bucur and Vinod Jaswanth Roy Mukkala and Konstantin Pozdniakov and Christos Kloukinas and Dimitrios D. Koutsouris and Elefteria Iliadou and Ioannis Leontsinis and Luigi Gallo and Giuseppe De Pietro and George Spanoudakis},
editor = { Leszek A. Maciaszek and Maurice D. Mulvenna and Martina Ziefle},
url = {https://link.springer.com/chapter/10.1007/978-3-031-37496-8_21},
doi = {10.1007/978-3-031-37496-8_21},
isbn = {978-3-031-37496-8},
year = {2023},
date = {2023-07-14},
urldate = {2023-01-01},
booktitle = {Information and Communication Technologies for Ageing Well and E-Health},
pages = {408–425},
publisher = {Springer Nature Switzerland},
address = {Cham},
series = {Communications in Computer and Information Science},
abstract = {The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the SMART BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big Data Analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites.},
keywords = {Ageing, AI, Balance Disorder, Cardiovascular Disease, Cloud, Evidence-based, GDPR, Healthcare, Hearing Loss, HL7 FHIR, Semantic interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the SMART BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big Data Analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites.
Basdekis, Ioannis; Kloukinas, Christos; Agostinho, Carlos; Vezakis, Ioannis; Pimenta, Andreia; Gallo, Luigi; Spanoudakis, Georgios
Pseudonymisation in the Context of GDPR-compliant Medical Research Proceedings Article
In: 2023 19th International Conference on the Design of Reliable Communication Networks (DRCN), pp. 1–6, IEEE, Vilanova i la Geltru, Spain, 2023, ISBN: 978-1-66547-598-3.
Abstract | Links | BibTeX | Tags: GDPR, Healthcare, Security
@inproceedings{basdekisPseudonymisationContextGDPRcompliant2023,
title = {Pseudonymisation in the Context of GDPR-compliant Medical Research},
author = { Ioannis Basdekis and Christos Kloukinas and Carlos Agostinho and Ioannis Vezakis and Andreia Pimenta and Luigi Gallo and Georgios Spanoudakis},
doi = {10.1109/DRCN57075.2023.10108370},
isbn = {978-1-66547-598-3},
year = {2023},
date = {2023-04-01},
urldate = {2023-04-01},
booktitle = {2023 19th International Conference on the Design of Reliable Communication Networks (DRCN)},
pages = {1--6},
publisher = {IEEE},
address = {Vilanova i la Geltru, Spain},
abstract = {Pseudonymisation is a data protection technique often used to protect the privacy of individuals when their personal data are being used for research purposes. Not only is it a key ingredient of the General Data Protection Regulation (GDPR) that requires organisations to ensure that the personal data they process is handled in a secure manner, but it is particularly important in assisting medical research given that often relies on sensitive personal data, since it reduces the risk that medical data could be misused or mishandled. For managing their medical data, it is important to ensure that such data are protected against unauthorised access, and can be reutilised in an anonymous fashion, while still authorised personnel is able to identify the study participant that some data belong to (e.g., for personalised interventions, technical alerts, technical support). In addition, the re-identification of a study participant is a pre-requisite for exercising their rights under the GDPR, since it assists organisations in meeting GDPR requirements (such as the right to access, rectify and portability of data). We argue that the application of pseudonymisation is particularly effective when considered during the early stages (Privacy by Design) of digital services implementation, as well as when defining the complementary to these organizational procedures. Aim of this paper is to present the way in which the pseudonymisation mechanism of the SMART BEAR H2020 project supports the triptych of research activities conducted within the context of an observational medical study, legal obligations arising from the regulatory framework for the protection of personal data, and reutilisation of data for research purposes. Evidence-based security and privacy assessments will be conducted on two different H2020 projects to evaluate such privacy practice.},
keywords = {GDPR, Healthcare, Security},
pubstate = {published},
tppubtype = {inproceedings}
}
Pseudonymisation is a data protection technique often used to protect the privacy of individuals when their personal data are being used for research purposes. Not only is it a key ingredient of the General Data Protection Regulation (GDPR) that requires organisations to ensure that the personal data they process is handled in a secure manner, but it is particularly important in assisting medical research given that often relies on sensitive personal data, since it reduces the risk that medical data could be misused or mishandled. For managing their medical data, it is important to ensure that such data are protected against unauthorised access, and can be reutilised in an anonymous fashion, while still authorised personnel is able to identify the study participant that some data belong to (e.g., for personalised interventions, technical alerts, technical support). In addition, the re-identification of a study participant is a pre-requisite for exercising their rights under the GDPR, since it assists organisations in meeting GDPR requirements (such as the right to access, rectify and portability of data). We argue that the application of pseudonymisation is particularly effective when considered during the early stages (Privacy by Design) of digital services implementation, as well as when defining the complementary to these organizational procedures. Aim of this paper is to present the way in which the pseudonymisation mechanism of the SMART BEAR H2020 project supports the triptych of research activities conducted within the context of an observational medical study, legal obligations arising from the regulatory framework for the protection of personal data, and reutilisation of data for research purposes. Evidence-based security and privacy assessments will be conducted on two different H2020 projects to evaluate such privacy practice.
Basdekis, Ioannis; Kloukinas, Christos; Agostinho, Carlos; Vezakis, Ioannis; Pimenta, Andreia; Gallo, Luigi; Spanoudakis, Georgios
Pseudonymisation in the context of GDPR-compliant medical research Proceedings Article
In: 2023 19th International Conference on the Design of Reliable Communication Networks (DRCN), pp. 1–6, IEEE, Vilanova i la Geltru, Spain, 2023, ISBN: 978-1-66547-598-3.
Abstract | Links | BibTeX | Tags: GDPR, Healthcare, Security
@inproceedings{basdekis_pseudonymisation_2023,
title = {Pseudonymisation in the context of GDPR-compliant medical research},
author = {Ioannis Basdekis and Christos Kloukinas and Carlos Agostinho and Ioannis Vezakis and Andreia Pimenta and Luigi Gallo and Georgios Spanoudakis},
url = {https://ieeexplore.ieee.org/document/10108370/},
doi = {10.1109/DRCN57075.2023.10108370},
isbn = {978-1-66547-598-3},
year = {2023},
date = {2023-04-01},
urldate = {2023-05-09},
booktitle = {2023 19th International Conference on the Design of Reliable Communication Networks (DRCN)},
pages = {1–6},
publisher = {IEEE},
address = {Vilanova i la Geltru, Spain},
abstract = {Pseudonymisation is a data protection technique often used to protect the privacy of individuals when their personal data are being used for research purposes. Not only is it a key ingredient of the General Data Protection Regulation (GDPR) that requires organisations to ensure that the personal data they process is handled in a secure manner, but it is particularly important in assisting medical research given that often relies on sensitive personal data, since it reduces the risk that medical data could be misused or mishandled. For managing their medical data, it is important to ensure that such data are protected against unauthorised access, and can be reutilised in an anonymous fashion, while still authorised personnel is able to identify the study participant that some data belong to (e.g., for personalised interventions, technical alerts, technical support). In addition, the re-identification of a study participant is a pre-requisite for exercising their rights under the GDPR, since it assists organisations in meeting GDPR requirements (such as the right to access, rectify and portability of data). We argue that the application of pseudonymisation is particularly effective when considered during the early stages (Privacy by Design) of digital services implementation, as well as when defining the complementary to these organizational procedures. Aim of this paper is to present the way in which the pseudonymisation mechanism of the SMART BEAR H2020 project supports the triptych of research activities conducted within the context of an observational medical study, legal obligations arising from the regulatory framework for the protection of personal data, and reutilisation of data for research purposes. Evidence-based security and privacy assessments will be conducted on two different H2020 projects to evaluate such privacy practice.},
keywords = {GDPR, Healthcare, Security},
pubstate = {published},
tppubtype = {inproceedings}
}
Pseudonymisation is a data protection technique often used to protect the privacy of individuals when their personal data are being used for research purposes. Not only is it a key ingredient of the General Data Protection Regulation (GDPR) that requires organisations to ensure that the personal data they process is handled in a secure manner, but it is particularly important in assisting medical research given that often relies on sensitive personal data, since it reduces the risk that medical data could be misused or mishandled. For managing their medical data, it is important to ensure that such data are protected against unauthorised access, and can be reutilised in an anonymous fashion, while still authorised personnel is able to identify the study participant that some data belong to (e.g., for personalised interventions, technical alerts, technical support). In addition, the re-identification of a study participant is a pre-requisite for exercising their rights under the GDPR, since it assists organisations in meeting GDPR requirements (such as the right to access, rectify and portability of data). We argue that the application of pseudonymisation is particularly effective when considered during the early stages (Privacy by Design) of digital services implementation, as well as when defining the complementary to these organizational procedures. Aim of this paper is to present the way in which the pseudonymisation mechanism of the SMART BEAR H2020 project supports the triptych of research activities conducted within the context of an observational medical study, legal obligations arising from the regulatory framework for the protection of personal data, and reutilisation of data for research purposes. Evidence-based security and privacy assessments will be conducted on two different H2020 projects to evaluate such privacy practice.
Su, Qiqi; Peretokin, Vadim; Basdekis, Ioannis; Kouris, Ioannis; Maggesi, Jonatan; Sicuranza, Mario; Acebes, Alberto; Bucur, Anca; Mukkala, Vinod Jaswanth Roy; Pozdniakov, Konstantin; Kloukinas, Christos; Koutsouris, Dimitrios D.; Iliadou, Elefteria; Leontsinis, Ioannis; Gallo, Luigi; Pietro, Giuseppe De; Spanoudakis, George
The SMART BEAR Project: An Overview of Its Infrastructure Proceedings Article
In: Maciaszek, Leszek A.; Mulvenna, Maurice D.; Ziefle, Martina (Ed.): Information and Communication Technologies for Ageing Well and e-Health, pp. 408–425, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-37496-8.
Abstract | Links | BibTeX | Tags: Ageing, AI, Balance Disorder, Cardiovascular Disease, Cloud, Evidence-based, GDPR, Healthcare, Hearing Loss, HL7 FHIR, Semantic interoperability
@inproceedings{su_smart_2023,
title = {The SMART BEAR Project: An Overview of Its Infrastructure},
author = {Qiqi Su and Vadim Peretokin and Ioannis Basdekis and Ioannis Kouris and Jonatan Maggesi and Mario Sicuranza and Alberto Acebes and Anca Bucur and Vinod Jaswanth Roy Mukkala and Konstantin Pozdniakov and Christos Kloukinas and Dimitrios D. Koutsouris and Elefteria Iliadou and Ioannis Leontsinis and Luigi Gallo and Giuseppe De Pietro and George Spanoudakis},
editor = {Leszek A. Maciaszek and Maurice D. Mulvenna and Martina Ziefle},
doi = {10.1007/978-3-031-37496-8_21},
isbn = {978-3-031-37496-8},
year = {2023},
date = {2023-01-01},
booktitle = {Information and Communication Technologies for Ageing Well and e-Health},
pages = {408–425},
publisher = {Springer Nature Switzerland},
address = {Cham},
series = {Communications in Computer and Information Science},
abstract = {The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the SMART BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big Data Analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites.},
keywords = {Ageing, AI, Balance Disorder, Cardiovascular Disease, Cloud, Evidence-based, GDPR, Healthcare, Hearing Loss, HL7 FHIR, Semantic interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the SMART BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big Data Analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites.
2022
Peretokin, Vadim; Basdekis, Ioannis; Kouris, Ioannis; Maggesi, Jonatan; Sicuranza, Mario; Su, Qiqi; Acebes, Alberto; Bucur, Anca; Mukkala, Vinod; Pozdniakov, Konstantin; Kloukinas, Christos; Koutsouris, Dimitrios; Iliadou, Elefteria; Leontsinis, Ioannis; Gallo, Luigi; Pietro, Giuseppe De; Spanoudakis, George
Overview of the SMART-BEAR Technical Infrastructure Best Paper Proceedings Article
In: Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and E-Health, pp. 117–125, SCITEPRESS - Science and Technology Publications, Online, 2022, ISBN: 978-989-758-566-1.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Balance Disorder, Cardiovascular Disease, Cloud computing, Evidence-based, GDPR, Healthcare, Hearing Loss, HL7 FHIR, Interoperability, Semantics
@inproceedings{peretokinOverviewSMARTBEARTechnical2022,
title = {Overview of the SMART-BEAR Technical Infrastructure},
author = { Vadim Peretokin and Ioannis Basdekis and Ioannis Kouris and Jonatan Maggesi and Mario Sicuranza and Qiqi Su and Alberto Acebes and Anca Bucur and Vinod Mukkala and Konstantin Pozdniakov and Christos Kloukinas and Dimitrios Koutsouris and Elefteria Iliadou and Ioannis Leontsinis and Luigi Gallo and Giuseppe De Pietro and George Spanoudakis},
doi = {10.5220/0011082700003188},
isbn = {978-989-758-566-1},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and E-Health},
pages = {117--125},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Online},
abstract = {This paper describes a cloud-based platform that offers evidence-based, personalised interventions powered by Artificial Intelligence to help support efficient remote monitoring and clinician-driven guidance to people over 65 who suffer or are at risk of hearing loss, cardiovascular diseases, cognitive impairments, balance disorders, and mental health issues. This platform has been developed within the SMART-BEAR integrated project to power its large-scale clinical pilots and comprises a standards-based data harmonisation and management layer, a security component, a Big Data Analytics system, a Clinical Decision Support tool, and a dashboard component for efficient data collection across the pilot sites.},
keywords = {Artificial intelligence, Balance Disorder, Cardiovascular Disease, Cloud computing, Evidence-based, GDPR, Healthcare, Hearing Loss, HL7 FHIR, Interoperability, Semantics},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes a cloud-based platform that offers evidence-based, personalised interventions powered by Artificial Intelligence to help support efficient remote monitoring and clinician-driven guidance to people over 65 who suffer or are at risk of hearing loss, cardiovascular diseases, cognitive impairments, balance disorders, and mental health issues. This platform has been developed within the SMART-BEAR integrated project to power its large-scale clinical pilots and comprises a standards-based data harmonisation and management layer, a security component, a Big Data Analytics system, a Clinical Decision Support tool, and a dashboard component for efficient data collection across the pilot sites.
Peretokin, Vadim; Basdekis, Ioannis; Kouris, Ioannis; Maggesi, Jonatan; Sicuranza, Mario; Su, Qiqi; Acebes, Alberto; Bucur, Anca; Mukkala, Vinod; Pozdniakov, Konstantin; Kloukinas, Christos; Koutsouris, Dimitrios; Iliadou, Elefteria; Leontsinis, Ioannis; Gallo, Luigi; Pietro, Giuseppe De; Spanoudakis, George
Overview of the SMART-BEAR Technical Infrastructure Proceedings Article
In: Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health, pp. 117–125, SCITEPRESS - Science and Technology Publications, Online, 2022, ISBN: 978-989-758-566-1.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Balance Disorder, Cardiovascular Disease, Cloud computing, Evidence-based, GDPR, Healthcare, Hearing Loss, HL7 FHIR, Interoperability, Semantics
@inproceedings{peretokin_overview_2022,
title = {Overview of the SMART-BEAR Technical Infrastructure},
author = {Vadim Peretokin and Ioannis Basdekis and Ioannis Kouris and Jonatan Maggesi and Mario Sicuranza and Qiqi Su and Alberto Acebes and Anca Bucur and Vinod Mukkala and Konstantin Pozdniakov and Christos Kloukinas and Dimitrios Koutsouris and Elefteria Iliadou and Ioannis Leontsinis and Luigi Gallo and Giuseppe De Pietro and George Spanoudakis},
url = {https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0011082700003188},
doi = {10.5220/0011082700003188},
isbn = {978-989-758-566-1},
year = {2022},
date = {2022-01-01},
urldate = {2023-03-15},
booktitle = {Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health},
pages = {117–125},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Online},
abstract = {This paper describes a cloud-based platform that offers evidence-based, personalised interventions powered by Artificial Intelligence to help support efficient remote monitoring and clinician-driven guidance to people over 65 who suffer or are at risk of hearing loss, cardiovascular diseases, cognitive impairments, balance disorders, and mental health issues. This platform has been developed within the SMART-BEAR integrated project to power its large-scale clinical pilots and comprises a standards-based data harmonisation and management layer, a security component, a Big Data Analytics system, a Clinical Decision Support tool, and a dashboard component for efficient data collection across the pilot sites.},
keywords = {Artificial intelligence, Balance Disorder, Cardiovascular Disease, Cloud computing, Evidence-based, GDPR, Healthcare, Hearing Loss, HL7 FHIR, Interoperability, Semantics},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes a cloud-based platform that offers evidence-based, personalised interventions powered by Artificial Intelligence to help support efficient remote monitoring and clinician-driven guidance to people over 65 who suffer or are at risk of hearing loss, cardiovascular diseases, cognitive impairments, balance disorders, and mental health issues. This platform has been developed within the SMART-BEAR integrated project to power its large-scale clinical pilots and comprises a standards-based data harmonisation and management layer, a security component, a Big Data Analytics system, a Clinical Decision Support tool, and a dashboard component for efficient data collection across the pilot sites.