Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Aikaterini, Tika"'
Autor:
Georgios, Feretzakis, Aikaterini, Sakagianni, Dimitris, Kalles, Evangelos, Loupelis, Lazaros, Tzelves, Vasileios, Panteris, Rea, Chatzikyriakou, Nikolaos, Trakas, Stavroula, Kolokytha, Polyxeni, Batiani, Zoi, Rakopoulou, Aikaterini, Tika, Stavroula, Petropoulou, Ilias, Dalainas, Vasileios, Kaldis
Publikováno v:
Studies in health technology and informatics. 295
Emergency department (ED) overcrowding is an increasing global problem raising safety concerns for the patients. Elaborating an effective triage system that properly separates patients requiring hospital admission remains difficult. The objective of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::89268d92d03c0c0a4ad67d48e5f81a0d
https://pubmed.ncbi.nlm.nih.gov/35773921
https://pubmed.ncbi.nlm.nih.gov/35773921
Autor:
Georgios Feretzakis, Aikaterini Sakagianni, Dimitris Kalles, Evangelos Loupelis, Vasileios Panteris, Lazaros Tzelves, Rea Chatzikyriakou, Nikolaos Trakas, Stavroula Kolokytha, Polyxeni Batiani, Zoi Rakopoulou, Aikaterini Tika, Stavroula Petropoulou, Ilias Dalainas, Vasileios Kaldis
Artificial intelligence processes are increasingly being used in emergency medicine, notably for supporting clinical decisions and potentially improving healthcare services. This study investigated demographics, coagulation tests, and biochemical mar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6888559f8c3a9228fb074767f4e20848
https://doi.org/10.3233/shti220751
https://doi.org/10.3233/shti220751
Autor:
Georgios Feretzakis, Aikaterini Sakagianni, Dimitris Kalles, Evangelos Loupelis, Lazaros Tzelves, Vasileios Panteris, Rea Chatzikyriakou, Nikolaos Trakas, Stavroula Kolokytha, Polyxeni Batiani, Zoi Rakopoulou, Aikaterini Tika, Stavroula Petropoulou, Ilias Dalainas, Vasileios Kaldis
Emergency department (ED) overcrowding is an increasing global problem raising safety concerns for the patients. Elaborating an effective triage system that properly separates patients requiring hospital admission remains difficult. The objective of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::83ff9ed4d0fa10c64751012d62b0d9f7
https://doi.org/10.3233/shti220775
https://doi.org/10.3233/shti220775
Autor:
Georgios, Feretzakis, Aikaterini, Sakagianni, Evangelos, Loupelis, Dimitris, Kalles, Vasileios, Panteris, Lazaros, Tzelves, Rea, Chatzikyriakou, Nikolaos, Trakas, Stavroula, Kolokytha, Polyxeni, Batiani, Zoi, Rakopoulou, Aikaterini, Tika, Stavroula, Petropoulou, Ilias, Dalainas, Vasileios, Kaldis
Publikováno v:
Studies in health technology and informatics. 294
The objective of this study was to evaluate the predictive capability of five machine learning models regarding the admission or discharge of emergency department patients. A Random Forest classifier outperformed other models with respect to the area
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::a73f945805649724d07bb51558f953cf
https://pubmed.ncbi.nlm.nih.gov/35612042
https://pubmed.ncbi.nlm.nih.gov/35612042
Autor:
Georgios Feretzakis, Aikaterini Sakagianni, Evangelos Loupelis, Dimitris Kalles, Vasileios Panteris, Lazaros Tzelves, Rea Chatzikyriakou, Nikolaos Trakas, Stavroula Kolokytha, Polyxeni Batiani, Zoi Rakopoulou, Aikaterini Tika, Stavroula Petropoulou, Ilias Dalainas, Vasileios Kaldis
The objective of this study was to evaluate the predictive capability of five machine learning models regarding the admission or discharge of emergency department patients. A Random Forest classifier outperformed other models with respect to the area
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b93367d81d068fa22a5c83a25b526bac
https://doi.org/10.3233/shti220422
https://doi.org/10.3233/shti220422
Autor:
Georgios, Feretzakis, Aikaterini, Sakagianni, Evangelos, Loupelis, Georgios, Karlis, Dimitris, Kalles, Lazaros, Tzelves, Rea, Chatzikyriakou, Nikolaos, Trakas, Stavroula, Petropoulou, Aikaterini, Tika, Zoi, Rakopoulou, Ilias, Dalainas, Vasileios, Kaldis
Publikováno v:
Studies in health technology and informatics. 289
The objective of this study was to establish a machine learning model and to evaluate its predictive capability of admission to the hospital. This observational retrospective study included 3204 emergency department visits to a public tertiary care h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::b3798ba621d74a48cc9d9c93411b5a95
https://pubmed.ncbi.nlm.nih.gov/35062151
https://pubmed.ncbi.nlm.nih.gov/35062151
Autor:
Aikaterini, Sakagianni, Georgios, Feretzakis, Georgios, Karlis, Evangelos, Loupelis, Lazaros, Tzelves, Rea, Chatzikyriakou, Nikolaos, Trakas, Eugenia, Karakou, Stavroula, Petropoulou, Aikaterini, Tika, Zoi, Rakopoulou, Ilias, Dalainas, Vasileios, Kaldis
Publikováno v:
Studies in health technology and informatics. 289
Emergency ambulance use is deemed necessary for the transport of acutely ill patients to hospital emergency departments (ED). However, some patients are discharged as they present low acuity or chronic problems and should receive primary healthcare s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::5d93196cf6fcc587ec4e29f7480ff635
https://pubmed.ncbi.nlm.nih.gov/35062180
https://pubmed.ncbi.nlm.nih.gov/35062180
Autor:
Aikaterini Sakagianni, Georgios Feretzakis, Georgios Karlis, Evangelos Loupelis, Lazaros Tzelves, Rea Chatzikyriakou, Nikolaos Trakas, Eugenia Karakou, Stavroula Petropoulou, Aikaterini Tika, Zoi Rakopoulou, Ilias Dalainas, Vasileios Kaldis
Emergency ambulance use is deemed necessary for the transport of acutely ill patients to hospital emergency departments (ED). However, some patients are discharged as they present low acuity or chronic problems and should receive primary healthcare s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ad710f895bd94c1c20c04e3f79189bb
https://doi.org/10.3233/shti210947
https://doi.org/10.3233/shti210947
Autor:
Georgios Feretzakis, George Karlis, Evangelos Loupelis, Dimitris Kalles, Rea Chatzikyriakou, Nikolaos Trakas, Eugenia Karakou, Aikaterini Sakagianni, Lazaros Tzelves, Stavroula Petropoulou, Aikaterini Tika, Ilias Dalainas, Vasileios Kaldis
Publikováno v:
Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures). 8(2)
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da162829543f96bd7466931c8cb6f91f
https://pubmed.ncbi.nlm.nih.gov/35950158
https://pubmed.ncbi.nlm.nih.gov/35950158
Autor:
Georgios Feretzakis, Stavroula Petropoulou, Konstantinos Mantzouranis, Aikaterini Sofianou, Vasileios Kaldis, Rea Chatzikyriakou, Ilias Dalainas, Evangelos Loupelis, Paraskevi Zafeiriadou, Stamatios Orfanos, Aikaterini Tika, Georgios Karlis, Makrina Tsafaridou, Konstantinos Tsekouras
Publikováno v:
MIE
During the COVID-19 pandemic, the number of visits in emergency departments (ED) worldwide decreased significantly based on several studies. This study aims to compare the patient flow in the emergency surgery department during the COVID-19 pandemic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22aa45941cb28c684718a4cdf1fd0be4
https://doi.org/10.3233/shti210229
https://doi.org/10.3233/shti210229