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8 results on '"George Vogiatzis"'

1. Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study

Author

Konstantinos Tsioufis, Despoina Ntiloudi, George Giannakoulas, George Lazaros, Constantinos Bakogiannis, George Kassimis, Anastasios Kartas, Panos E Vardas, Dimitrios Farmakis, Periklis Davlouros, Maria Ioannou, George Kochiadakis, Athanasios Samaras, Antonios Ziakas, Theoni Theodoropoulou, Dimitrios V Moysidis, John Skoularigis, Andreas S Papazoglou, Alexandra Bekiaridou, Grigorios Tsoumakas, Panagiotis Bamidis, Grigorios Tsigkas, Nikolaos Fragakis, Vassilios Vassilikos, Ioannis Zarifis, Dimitrios N Tziakas, Athanasios Feidakis, Vasiliki Patsiou, Eirinaios Tsiartas, Antonios Orfanidis, Triantafyllia Grantza, Chrysanthi Ioanna Lampropoulou, Dimitrios Kostakakis, Olga Kazarli, Maria Eirini Kiriakideli, Melina Kyriakou, Dimitra Kontopyrgou, Martha Zergioti, Eleftherios Gemousakakis, Amalia Baroutidou, Alexios Vagianos, Alexandros Liatsos, Konstantinos Barmpagiannos, George Tyrikos, George Konstantinou, Anthi Vasilopoulou, Marina Spaho, Eleni Manthou, Panagiotis Zymaris, Eleni Baliafa, Maria Baloka, Iasonas Dermitzakis, Vasiliki Anagnostopoulou, Chrysi Solovou, Anna Maria Louka, Aliki Iliadou, Ioanna Filimidou, Aspasia Kyriafini, Odysseas Kamzolas, Ioannis Vouloagkas, Despoina Nteli, Nikolaos Outountzidis, Athanasia Vathi, Anastasia Foka, Michael Botis, Anastasia Christodoulou, George Vogiatzis, Eleni Vrana, Maria Nteli, Stefanos Antοniadis, Foteini Charisi, Mairifylli Vamvaka, Dimitrios Triantis, Efi Delilampou, Vaggelis Axarloglou, Georgios Charistos, George Anagnostou, Sofia Christodoulou, Anastasios Papanastasiou, Eleni Tziona, Nikolaos Batis, Katerina Gakidi, Artemis Iosifidou, Andreanna Moura, Christos Alexandropoulos, Theoni Exintaveloni, Asterios Karakoutas, Damianos Porfyropoulos, Michail Bountas, Athanasios Pachoumis, Eleftherios Markidis, Maria Sitmalidou, Athanasia Pappa, Konstantinos C Theodoropoulos, George Rampidis, Apostolos Tzikas, Stylianos Paraskevaidis, Georgios Efthimiadis, Theofilatos Athinagoras, Christoforos Travlos, Nikolaos Vythoulkas-Biotis, Kassiani Maria Nastouli, Nikolaos Kartas, Angeliki Vakka, Maria Bozika, Virginia Anagnostopoulou, Georgios Tsioulos, Emilia Lazarou, Panagiotis Tsioufis, Ioannis Kachrimanidis, Nick Argyriou, Emmanouil Kampanieris, Alexandros Patrianakos, Ioannis Kanakakis, Marios Vasileios Koutroulos, Georgios K Chalikias, Sophia Alexiou, Athena Nasoufidou, Panagiotis Stachteas, Tsantikos Christos, Grigorios Giamouzis, Ioannis Alexanian, Ioannis Styliadis, George Fotos, Nikolaos Bourboulis, Evangelos Pisimisis, Antonis Billis, Ilias Kyparissidis, Dimitrios Tsalikakis, Jens-Michael Papaioannou, and Alexander Löser

Subjects
Medicine
Abstract

Introduction Mining of electronic health record (EHRs) data is increasingly being implemented all over the world but mainly focuses on structured data. The capabilities of artificial intelligence (AI) could reverse the underusage of unstructured EHR data and enhance the quality of medical research and clinical care. This study aims to develop an AI-based model to transform unstructured EHR data into an organised, interpretable dataset and form a national dataset of cardiac patients.Methods and analysis CardioMining is a retrospective, multicentre study based on large, longitudinal data obtained from unstructured EHRs of the largest tertiary hospitals in Greece. Demographics, hospital administrative data, medical history, medications, laboratory examinations, imaging reports, therapeutic interventions, in-hospital management and postdischarge instructions will be collected, coupled with structured prognostic data from the National Institute of Health. The target number of included patients is 100 000. Natural language processing techniques will facilitate data mining from the unstructured EHRs. The accuracy of the automated model will be compared with the manual data extraction by study investigators. Machine learning tools will provide data analytics. CardioMining aims to cultivate the digital transformation of the national cardiovascular system and fill the gap in medical recording and big data analysis using validated AI techniques.Ethics and dissemination This study will be conducted in keeping with the International Conference on Harmonisation Good Clinical Practice guidelines, the Declaration of Helsinki, the Data Protection Code of the European Data Protection Authority and the European General Data Protection Regulation. The Research Ethics Committee of the Aristotle University of Thessaloniki and Scientific and Ethics Council of the AHEPA University Hospital have approved this study. Study findings will be disseminated through peer-reviewed medical journals and international conferences. International collaborations with other cardiovascular registries will be attempted.Trial registration number NCT05176769.

Published
2023
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6. Microplastics in marine biota of North Aegean Sea: Summarizing and comparing preliminary findings

Author

Eva Krasakopoulou, Dimitra Marmara, George Vogiatzis, Charis-Konstantinos Christofidis, and Alexios Stamelos

Abstract

Rising scientific literature and legislation measures have highlighted the concern on plastic pollution and the issue of microplastics. In the context of this study, 3 marine species of commercial interest (bogues, Mediterranean mussels, blue crabs) were examined, concerning microplastics’ presence and characteristics. The abundance of the ingested items ranged between 1.22 and 3.30 items/individual. In total, 172 items were extracted from the samples, while only three items were categorised as mesoplastics. The most frequent size for all three species was particles smaller than 200 μm, while fragment was the most frequent type of microplastic (58.48%). In total, 12 colours were recorded, with blue being the most frequent one. Within the sampling areas, significant differences were determined (p

Published
2022

8. Learning an augmentation strategy for sparse datasets

Author

Diego R. Faria, George Vogiatzis, and Renato Barros Arantes

Subjects
Training set, Computer science, business.industry, Percentage point, Overfitting, Machine learning, computer.software_genre, Signal Processing, Domain knowledge, Segmentation, Computer Vision and Pattern Recognition, Artificial intelligence, History of use, business, computer
Abstract

The limited quantity of training data can hamper supervised machine learning methods, that generally need large amounts of data to avoid overfitting. Data augmentation has a long history of use with machine learning algorithms and is a straightforward method to overcome overfitting and improve model generalisation. However, data augmentation schemes are typically designed by hand and demand substantial domain knowledge to create suitable data transformations. This paper introduces a GAN based method that automatically learns an augmentation strategy appropriate for sparse datasets and can improve pixel-level semantic segmentation accuracy by filling the gaps in the training set. Our method can also be combined with other augmentation techniques to further improve performance. We evaluate the proposed method's feasibility on four datasets and three semantic segmentation models, leading to improvement in the mean intersection-over-union (mIoU) score of between 0.5 and 14 percentage points, under different circumstances.

Published
2022

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