Zobrazeno 1 - 10
of 451
pro vyhledávání: '"A. Sakagianni"'
Autor:
Zarkogianni, Konstantia, Dervakos, Edmund, Filandrianos, George, Ganitidis, Theofanis, Gkatzou, Vasiliki, Sakagianni, Aikaterini, Raghavendra, Raghu, Nikias, C. L. Max, Stamou, Giorgos, Nikita, Konstantina S.
Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially dur
Externí odkaz:
http://arxiv.org/abs/2307.05096
Autor:
Aikaterini Sakagianni, Christina Koufopoulou, Petros Koufopoulos, Sofia Kalantzi, Nikolaos Theodorakis, Maria Nikolaou, Evgenia Paxinou, Dimitris Kalles, Vassilios S. Verykios, Pavlos Myrianthefs, Georgios Feretzakis
Publikováno v:
Antibiotics, Vol 13, Iss 11, p 1052 (2024)
Background/Objectives: The emergence of antimicrobial resistance (AMR) due to the misuse and overuse of antibiotics has become a critical threat to global public health. There is a dire need to forecast AMR to understand the underlying mechanisms of
Externí odkaz:
https://doaj.org/article/8e8b675e83054b4896b6a09be3f4d474
Autor:
Konstantia Zarkogianni, Edmund Dervakos, George Filandrianos, Theofanis Ganitidis, Vasiliki Gkatzou, Aikaterini Sakagianni, Raghu Raghavendra, C. L. Max Nikias, Giorgos Stamou, Konstantina S. Nikita
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-19 (2023)
Abstract Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest espec
Externí odkaz:
https://doaj.org/article/3aa9f5588b734923acf1fc8d3ed0b086
Autor:
Feretzakis, Georgios, Karlis, George, Loupelis, Evangelos, Kalles, Dimitris, Chatzikyriakou, Rea, Trakas, Nikolaos, Karakou, Eugenia, Sakagianni, Aikaterini, Tzelves, Lazaros, Petropoulou, Stavroula, Tika, Aikaterini, Dalainas, Ilias, Kaldis, Vasileios
Introduction: One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare. Material and
Externí odkaz:
http://arxiv.org/abs/2106.12921
Autor:
Kiourt, Chairi, Feretzakis, Georgios, Dalamarinis, Konstantinos, Kalles, Dimitris, Pantos, Georgios, Papadopoulos, Ioannis, Kouris, Spyros, Ioannakis, George, Loupelis, Evangelos, Antonopoulos, Petros, Sakagianni, Aikaterini
The main objective of this work is to utilize state-of-the-art deep learning approaches for the identification of pulmonary embolism in CTPA-Scans for COVID-19 patients, provide an initial assessment of their performance and, ultimately, provide a fa
Externí odkaz:
http://arxiv.org/abs/2105.11187
Autor:
Aikaterini Sakagianni, Christina Koufopoulou, Petros Koufopoulos, Georgios Feretzakis, Dimitris Kalles, Evgenia Paxinou, Pavlos Myrianthefs, Vassilios S. Verykios
Publikováno v:
Antibiotics, Vol 13, Iss 10, p 996 (2024)
Background/Objectives: Carbapenem resistance poses a significant threat to public health by undermining the efficacy of one of the last lines of antibiotic defense. Addressing this challenge requires innovative approaches that can enhance our underst
Externí odkaz:
https://doaj.org/article/4f457255c54b45a0b3574ddf2143bd27
Autor:
Nikolaos Theodorakis, Georgios Feretzakis, Christos Hitas, Magdalini Kreouzi, Sofia Kalantzi, Aikaterini Spyridaki, Iris Zoe Boufeas, Aikaterini Sakagianni, Evgenia Paxinou, Vassilios S. Verykios, Maria Nikolaou
Publikováno v:
Microorganisms, Vol 12, Iss 10, p 1978 (2024)
Antibiotic resistance presents a critical challenge in healthcare, particularly among the elderly, where multidrug-resistant organisms (MDROs) contribute to increased morbidity, mortality, and healthcare costs. This review focuses on the mechanisms u
Externí odkaz:
https://doaj.org/article/13f3de301f0a4ec7b6c623792114c868
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Georgios Feretzakis, Aikaterini Sakagianni, Athanasios Anastasiou, Ioanna Kapogianni, Rozita Tsoni, Christina Koufopoulou, Dimitrios Karapiperis, Vasileios Kaldis, Dimitris Kalles, Vassilios S. Verykios
Publikováno v:
Applied Sciences, Vol 14, Iss 15, p 6623 (2024)
The study explores the application of automated machine learning (AutoML) using the MIMIC-IV-ED database to enhance decision-making in emergency department (ED) triage. We developed a predictive model that utilizes triage data to forecast hospital ad
Externí odkaz:
https://doaj.org/article/df37219d51e84cc1b880296da7216e91