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Article ; Online: Effectiveness and safety of minocycline combination therapy for the treatment of patients with ventilator-associated pneumonia due to extensively drug- or pandrug-resistant Acinetobacter baumannii.

Athanassa, Zoe / Manioudaki, Sofia / Petsa, Irina / Koumaki, Vasiliki / Sakagianni, Aikaterini / Tsakris, Athanasios

International journal of antimicrobial agents

2024 Volume 63, Issue 5, Page(s) 107129

MeSH term(s)	Humans ; Acinetobacter baumannii/drug effects ; Anti-Bacterial Agents/therapeutic use ; Anti-Bacterial Agents/administration & dosage ; Minocycline/therapeutic use ; Pneumonia, Ventilator-Associated/drug therapy ; Pneumonia, Ventilator-Associated/microbiology ; Acinetobacter Infections/drug therapy ; Acinetobacter Infections/microbiology ; Drug Resistance, Multiple, Bacterial ; Male ; Drug Therapy, Combination ; Treatment Outcome ; Middle Aged ; Aged ; Female
Chemical Substances	Anti-Bacterial Agents ; Minocycline (FYY3R43WGO)
Language	English
Publishing date	2024-03-01
Publishing country	Netherlands
Document type	Letter
ZDB-ID	1093977-5
ISSN	1872-7913 ; 0924-8579
ISSN (online)	1872-7913
ISSN	0924-8579
DOI	10.1016/j.ijantimicag.2024.107129
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Article ; Online: Prediction of COVID-19 Mortality in the Intensive Care Unit Using Machine Learning.

Sakagianni, Aikaterini / Koufopoulou, Christina / Verykios, Vassilios / Loupelis, Evangelos / Kalles, Dimitrios / Feretzakis, Georgios

Studies in health technology and informatics

2023 Volume 302, Page(s) 536–540

Abstract: Since its emergence, the COVID-19 pandemic still poses a major global health threat. In this setting, a number of useful machine learning applications have been explored to assist clinical decision-making, predict the severity of disease and admission to ...

Abstract	Since its emergence, the COVID-19 pandemic still poses a major global health threat. In this setting, a number of useful machine learning applications have been explored to assist clinical decision-making, predict the severity of disease and admission to the intensive care unit, and also to estimate future demand for hospital beds, equipment, and staff. The present study examined demographic data, hematological and biochemical markers routinely measured in Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, in relation to the ICU outcome, during the second and third Covid-19 waves, from October 2020 until February 2022. In this dataset, we applied eight well-known classifiers of the caret package for machine learning of the R programming language, to evaluate their performance in forecasting ICU mortality. The best performance regarding area under the receiver operating characteristic curve (AUC-ROC) was observed with Random Forest (0.82), while k-nearest neighbors (k-NN) were the lowest performing machine learning algorithm (AUC-ROC: 0.59). However, in terms of sensitivity, XGB outperformed the other classifiers (max Sens: 0.7). The six most important predictors of mortality in the Random Forest model were serum urea, age, hemoglobin, C-reactive protein, platelets, and lymphocyte count.
MeSH term(s)	Humans ; COVID-19 ; Pandemics ; Intensive Care Units ; Algorithms ; Machine Learning ; Retrospective Studies
Language	English
Publishing date	2023-05-19
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI230200
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Article ; Online: Automated ML Techniques for Predicting COVID-19 Mortality in the ICU.

Sakagianni, Aikaterini / Koufopoulou, Christina / Kalles, Dimitrios / Loupelis, Evangelos / Verykios, Vassilios S / Feretzakis, Georgios

Studies in health technology and informatics

2023 Volume 305, Page(s) 517–520

Abstract: The COVID-19 infection is still a serious threat to public health and healthcare systems. Numerous practical machine learning applications have been investigated in this context to support clinical decision-making, forecast disease severity and admission ...

Abstract	The COVID-19 infection is still a serious threat to public health and healthcare systems. Numerous practical machine learning applications have been investigated in this context to support clinical decision-making, forecast disease severity and admission to the intensive care unit, as well as to predict the demand for hospital beds, equipment, and staff in the future. We retrospectively analyzed demographics, and routine blood biomarkers from consecutive Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, during a 17-month period, relative to the outcome, in order to build a prognostic model. We used the Google Vertex AI platform, on the one hand, to evaluate its performance in predicting ICU mortality, and on the other hand to show the ease with which even non-experts can make prognostic models. The model's performance regarding the area under the receiver operating characteristic curve (AUC-ROC) was 0.955. The six highest-ranked predictors of mortality in the prognostic model were age, serum urea, platelets, C-reactive protein, hemoglobin, and SGOT.
MeSH term(s)	Humans ; COVID-19/diagnosis ; Retrospective Studies ; Area Under Curve ; Blood Platelets ; Intensive Care Units
Language	English
Publishing date	2023-06-19
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI230547
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Article ; Online: Discovering Association Rules in Antimicrobial Resistance in Intensive Care Unit.

Sakagianni, Aikaterini / Feretzakis, Georgios / Kalles, Dimitris / Loupelis, Evangelos / Rakopoulou, Zoi / Dalainas, Ilias / Fildisis, Georgios

Studies in health technology and informatics

2022 Volume 295, Page(s) 430–433

Abstract: Multidrug resistant infections in intensive care units represent a worldwide problem with adverse health effects and negative economic implications. As artificial intelligence techniques are increasingly applied in diagnosing, treating, and preventing ... ...

Abstract	Multidrug resistant infections in intensive care units represent a worldwide problem with adverse health effects and negative economic implications. As artificial intelligence techniques are increasingly applied in diagnosing, treating, and preventing antimicrobial resistance, in this study, we explore the use of association rule mining in the antibiotic resistance profile of critically ill patients suffering from multidrug resistant infections.
MeSH term(s)	Anti-Bacterial Agents/pharmacology ; Artificial Intelligence ; Cross Infection/drug therapy ; Cross Infection/prevention & control ; Drug Resistance, Bacterial ; Humans ; Intensive Care Units
Chemical Substances	Anti-Bacterial Agents
Language	English
Publishing date	2022-06-30
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI220757
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Article: Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review.

Sakagianni, Aikaterini / Koufopoulou, Christina / Feretzakis, Georgios / Kalles, Dimitris / Verykios, Vassilios S / Myrianthefs, Pavlos / Fildisis, Georgios

Antibiotics (Basel, Switzerland)

2023 Volume 12, Issue 3

Abstract: Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is ...

Abstract	Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.
Language	English
Publishing date	2023-02-24
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2681345-2
ISSN	2079-6382
ISSN	2079-6382
DOI	10.3390/antibiotics12030452
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Article ; Online: Setting up an Easy-to-Use Machine Learning Pipeline for Medical Decision Support: A Case Study for COVID-19 Diagnosis Based on Deep Learning with CT Scans.

Sakagianni, Aikaterini / Feretzakis, Georgios / Kalles, Dimitris / Koufopoulou, Christina / Kaldis, Vasileios

Studies in health technology and informatics

2020 Volume 272, Page(s) 13–16

Abstract: Coronavirus disease (COVID-19) constitutes an ongoing global health problem with significant morbidity and mortality. It usually presents characteristic findings on a chest CT scan, which may lead to early detection of the disease. A timely and accurate ... ...

Abstract	Coronavirus disease (COVID-19) constitutes an ongoing global health problem with significant morbidity and mortality. It usually presents characteristic findings on a chest CT scan, which may lead to early detection of the disease. A timely and accurate diagnosis of COVID-19 is the cornerstone for the prompt management of the patients. The aim of the present study was to evaluate the performance of an automated machine learning algorithm in the diagnosis of Covid-19 pneumonia using chest CT scans. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value. The method's average precision was 0.932. We suggest that auto-ML platforms help users with limited ML expertise train image recognition models by only uploading the examined dataset and performing some basic settings. Such methods could deliver significant potential benefits for patients in the future by allowing for earlier disease detection and care.
MeSH term(s)	Betacoronavirus ; COVID-19 ; Coronavirus Infections/diagnostic imaging ; Deep Learning ; Humans ; Machine Learning ; Pandemics ; Pneumonia, Viral/diagnostic imaging ; SARS-CoV-2 ; Tomography, X-Ray Computed
Keywords	covid19
Language	English
Publishing date	2020-06-30
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI200481
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Article ; Online: Using Machine Learning for Predicting the Hospitalization of Emergency Department Patients.

Feretzakis, Georgios / Sakagianni, Aikaterini / Kalles, Dimitris / Loupelis, Evangelos / Panteris, Vasileios / Tzelves, Lazaros / Chatzikyriakou, Rea / Trakas, Nikolaos / Kolokytha, Stavroula / Batiani, Polyxeni / Rakopoulou, Zoi / Tika, Aikaterini / Petropoulou, Stavroula / Dalainas, Ilias / Kaldis, Vasileios

Studies in health technology and informatics

2022 Volume 295, Page(s) 405–408

Abstract: 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 markers ...

Abstract	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 markers routinely used for patients seen in the Emergency Department (ED) concerning hospitalization. This retrospective observational study included 13,991 emergency department visits of patients who had undergone biomarker testing to a tertiary public hospital in Greece during 2020. After applying five well-known classifiers of the caret package for machine learning of the R programming language in the whole data set and to each ED unit separately, the best performance regarding AUC ROC was observed in the Pulmonology ED unit. Furthermore, among the five classification techniques evaluated, a random forest classifier outperformed other models.
MeSH term(s)	Artificial Intelligence ; Emergency Service, Hospital ; Hospitalization ; Humans ; Machine Learning ; Retrospective Studies
Language	English
Publishing date	2022-06-30
Publishing country	Netherlands
Document type	Journal Article ; Observational Study
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI220751
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Article ; Online: Exploratory Clustering for Emergency Department Patients.

Feretzakis, Georgios / Sakagianni, Aikaterini / Kalles, Dimitris / Loupelis, Evangelos / Tzelves, Lazaros / Panteris, Vasileios / Chatzikyriakou, Rea / Trakas, Nikolaos / Kolokytha, Stavroula / Batiani, Polyxeni / Rakopoulou, Zoi / Tika, Aikaterini / Petropoulou, Stavroula / Dalainas, Ilias / Kaldis, Vasileios

Studies in health technology and informatics

2022 Volume 295, Page(s) 503–506

Abstract: 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 this ...

Abstract	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 this study was to compare a clustering-related technique assignment of emergency department patients with the admission output using the k-means algorithm. Incorporating such a model into triage practice could theoretically shorten waiting times and reduce ED overcrowding.
MeSH term(s)	Algorithms ; Cluster Analysis ; Emergency Service, Hospital/organization & administration ; Hospitalization/statistics & numerical data ; Humans ; Patient Safety/standards ; Time Factors ; Triage/methods
Language	English
Publishing date	2022-06-30
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI220775
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Article: Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department.

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

Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures)

2022 Volume 8, Issue 2, Page(s) 107–116

Abstract: 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.: Aim of the ... ...

Abstract	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. Aim of the study: Our objective was to find an algorithm using ML techniques to assist clinical decision-making in the emergency setting. Material and methods: We assessed the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, DDi-mer, International Normalized Ratio, age, gender, triage disposition to ED unit and ambulance utilization. A total of 3,204 ED visits were analyzed. Results: The proposed algorithms generated models which demonstrated acceptable performance in predicting hospital admission of ED patients. The range of F-measure and ROC Area values of all eight evaluated algorithms were [0.679-0.708] and [0.734-0.774], respectively. The main advantages of this tool include easy access, availability, yes/no result, and low cost. The clinical implications of our approach might facilitate a shift from traditional clinical decision-making to a more sophisticated model. Conclusions: Developing robust prognostic models with the utilization of common biomarkers is a project that might shape the future of emergency medicine. Our findings warrant confirmation with implementation in pragmatic ED trials.
Language	English
Publishing date	2022-05-12
Publishing country	Poland
Document type	Journal Article
ISSN	2393-1809
ISSN	2393-1809
DOI	10.2478/jccm-2022-0003
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Article ; Online: The smarty4covid dataset and knowledge base as a framework for interpretable physiological audio data analysis.

Zarkogianni, Konstantia / Dervakos, Edmund / Filandrianos, George / Ganitidis, Theofanis / Gkatzou, Vasiliki / Sakagianni, Aikaterini / Raghavendra, Raghu / Max Nikias, C L / Stamou, Giorgos / Nikita, Konstantina S

Scientific data

2023 Volume 10, Issue 1, Page(s) 770

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 especially during ... ...

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 especially during COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,665), deep breathing (4,695) and voice (4,291) as recorded by means of mobile devices following a crowd-sourcing approach. Other self reported information is also included (e.g. COVID-19 virus tests), thus providing a comprehensive dataset for the development of COVID-19 risk detection models. The smarty4covid dataset is released in the form of a web-ontology language (OWL) knowledge base enabling data consolidation from other relevant datasets, complex queries and reasoning. It has been utilized towards the development of models able to: (i) extract clinically informative respiratory indicators from regular breathing records, and (ii) identify cough, breath and voice segments in crowd-sourced audio recordings. A new framework utilizing the smarty4covid OWL knowledge base towards generating counterfactual explanations in opaque AI-based COVID-19 risk detection models is proposed and validated.
MeSH term(s)	Humans ; Artificial Intelligence ; Cough ; COVID-19 ; Data Analysis ; Knowledge Bases ; Pandemics
Language	English
Publishing date	2023-11-06
Publishing country	England
Document type	Dataset ; Journal Article
ZDB-ID	2775191-0
ISSN	2052-4463 ; 2052-4463
ISSN (online)	2052-4463
ISSN	2052-4463
DOI	10.1038/s41597-023-02646-6
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