Education
[ 23/02/2023 – Current ] Phd Candidate
University of the Aegean: Department of Information and Communication Systems Engineering https://www.icsd.aegean.gr/
Field(s) of study: Information and Communication Technologies.
Thesis: Multivariate analysis of clinical - medical data
[ 30/09/2020 – 22/06/2022 ] MSc Information and Communication Systems
University of the Aegean: Department of Information and Communication Systems Engineering https://www.icsd.aegean.gr/
Field(s) of study: Information and Communication Technologies.
Final grade: 8.43 Level in EQF: EQF level 7
Type of credits: ECTS Number of credits: 90
Thesis: Mortality Prediction in ICU Patients Suffering from Stroke
[ 30/09/2003 – 30/09/2009 ] Bachelor's degree, Accounting and Finance
Technological Educational Institute of Chalkida http://acc.teiste.gr/
Address: Psachna Evoias, 34400, Chalkida, Greece
Field(s) of study: Business, administration and law
Final grade: 6.1 Level in EQF: EQF level 6
Thesis: Accountancy of damping (I.A.M. No 16) analysis and comparison with Greek Legislation.
[ 30/09/2003 – 30/09/2005 ] Computer Network Technical Degree Institute of Vocational Training
Address: 13561, Agioi Anargiroi - Athens, Greece
Field(s) of study: Information and Communication Technologie
Research Interests
As a dedicated researcher with a Master's degree in Information and Communication Systems, I am currently pursuing a Ph.D. in the dynamic realm of machine learning. My focus lies at the intersection of machine learning and multivariate analysis, particularly within the domain of clinical-medical data. I am passionate about developing innovative algorithms and methodologies that can effectively analyze and extract valuable insights from complex healthcare datasets. Through my academic journey, I aspire to contribute to the advancement of medical research, fostering a deeper understanding of diseases and improving healthcare outcomes through the application of cutting-edge machine learning techniques.
Teaching Activities
Journals
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Abstract
Background: Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific Clinical Prediction Models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records (EHRs). We aimed to explore ML-CPMs applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment. Methods: Three databases were searched, PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included. Results: Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison. Conclusion: ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of AI in VTE.
Conferences
Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.
Abstract
Ischemic stroke is a medical emergency that requires hospitalization and occasionally, specialized care at the Intensive Care Unit. Mortality prediction in the ICUs has been a challenge for intensivists, since prompt identification could impact medical clinical practices and allow efficient allocation of health resources in the ICUs, which are extremely restricted, especially in the era of COVID-19 pandemic. Clinical decision support systems based on machine learning algorithms are taking advantage of the vast amount of information available in the ICUs and are becoming popular in the medical predictive analysis. This study aims to explore the feasibility of interpretable machine learning models to predict mortality in critically-ill patients suffering from stroke. To do so, a vast variety of clinical and laboratory information stored in the electronic health record, are pre-processed to allow taking into account the temporal characteristics of a patient’s stay. An 8-hour sliding observation window was utilized. For the experimental evaluation we used the Medical Information Mart for Intensive Care Database (MIMIC-IV). Results indicate sufficient ability to predict mortality at the end of a given day during the patient’s stay. Moreover, attribute evaluation highlights the important indicators to consider when following up with a patient.
Books
Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.
Chapters in Books
Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.
Conferences Proceedings Editor
Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.