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Your search keyword '"Bolocan, Alexandra"' showing total 17 results
Start Over Author "Bolocan, Alexandra" Publisher mdpi ag
17 results on '"Bolocan, Alexandra"'

3. Silver/Graphene Oxide Nanostructured Coatings for Modulating the Microbial Susceptibility of Fixation Devices Used in Knee Surgery

Author

Constantinescu, Sorin, primary, Niculescu, Adelina-Gabriela, additional, Hudiță, Ariana, additional, Grumezescu, Valentina, additional, Rădulescu, Dragoș, additional, Bîrcă, Alexandra Cătălina, additional, Irimiciuc, Stefan Andrei, additional, Gherasim, Oana, additional, Holban, Alina Maria, additional, Gălățeanu, Bianca, additional, Oprea, Ovidiu Cristian, additional, Ficai, Anton, additional, Vasile, Bogdan Ștefan, additional, Grumezescu, Alexandru Mihai, additional, Bolocan, Alexandra, additional, and Rădulescu, Radu, additional

Published
2023
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5. The Vermiform Appendix and Its Pathologies

Author

Constantin, Marian, primary, Petrescu, Livia, additional, Mătanie, Cristina, additional, Vrancianu, Corneliu Ovidiu, additional, Niculescu, Adelina-Gabriela, additional, Andronic, Octavian, additional, and Bolocan, Alexandra, additional

Published
2023
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9. Duodenal Gangliocytic Paragangliomas—Case Series and Literature Review

Author

Stan-Ilie, Madalina, primary, Şandru, Vasile, additional, Plotogea, Oana-Mihaela, additional, Rînja, Ecaterina, additional, Pavel, Christopher, additional, Constantinescu, Gabriel, additional, Negreanu, Lucian, additional, Paduraru, Dan Nicolae, additional, Bolocan, Alexandra, additional, Andronic, Octavian, additional, Davitoiu, Dragos, additional, Bălan, Gheorghe G., additional, and Constantinescu, Alexandru, additional

Published
2023
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10. A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment

Author

Busnatu, Ștefan Sebastian, primary, Niculescu, Adelina-Gabriela, additional, Bolocan, Alexandra, additional, Andronic, Octavian, additional, Pantea Stoian, Anca Mihaela, additional, Scafa-Udriște, Alexandru, additional, Stănescu, Ana Maria Alexandra, additional, Păduraru, Dan Nicolae, additional, Nicolescu, Mihnea Ioan, additional, Grumezescu, Alexandru Mihai, additional, and Jinga, Viorel, additional

Published
2022
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12. Clinical Applications of Artificial Intelligence—An Updated Overview

Author

Busnatu, Ștefan, primary, Niculescu, Adelina-Gabriela, additional, Bolocan, Alexandra, additional, Petrescu, George E. D., additional, Păduraru, Dan Nicolae, additional, Năstasă, Iulian, additional, Lupușoru, Mircea, additional, Geantă, Marius, additional, Andronic, Octavian, additional, Grumezescu, Alexandru Mihai, additional, and Martins, Henrique, additional

Published
2022
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15. Antiviral Immunity in SARS-CoV-2 Infection: From Protective to Deleterious Responses

Author

Mihaescu, Grigore, primary, Chifiriuc, Mariana Carmen, additional, Vrancianu, Corneliu Ovidiu, additional, Constantin, Marian, additional, Filip, Roxana, additional, Popescu, Mihaela Roxana, additional, Burlibasa, Liliana, additional, Nicoara, Anca Cecilia, additional, Bolocan, Alexandra, additional, Iliescu, Ciprian, additional, and Gradisteanu Pircalabioru, Gratiela, additional

Published
2021
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17. Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests-A Systematic Review.

Author

Mușat F, Păduraru DN, Bolocan A, Palcău CA, Copăceanu AM, Ion D, Jinga V, and Andronic O

Abstract

Background. Sepsis presents significant diagnostic and prognostic challenges, and traditional scoring systems, such as SOFA and APACHE, show limitations in predictive accuracy. Machine learning (ML)-based predictive survival models can support risk assessment and treatment decision-making in the intensive care unit (ICU) by accounting for the numerous and complex factors that influence the outcome in the septic patient. Methods. A systematic literature review of studies published from 2014 to 2024 was conducted using the PubMed database. Eligible studies investigated the development of ML models incorporating commonly available laboratory and clinical data for predicting survival outcomes in adult ICU patients with sepsis. Study selection followed the PRISMA guidelines and relied on predefined inclusion criteria. All records were independently assessed by two reviewers, with conflicts resolved by a third senior reviewer. Data related to study design, methodology, results, and interpretation of the results were extracted in a predefined grid. Results. Overall, 19 studies were identified, encompassing primarily logistic regression, random forests, and neural networks. Most used datasets were US-based (MIMIC-III, MIMIC-IV, and eICU-CRD). The most common variables used in model development were age, albumin levels, lactate levels, and ventilator. ML models demonstrated superior performance metrics compared to conventional methods and traditional scoring systems. The best-performing model was a gradient boosting decision tree, with an area under curve of 0.992, an accuracy of 0.954, and a sensitivity of 0.917. However, several critical limitations should be carefully considered when interpreting the results, such as population selection bias (i.e., single center studies), small sample sizes, limited external validation, and model interpretability. Conclusions. Through real-time integration of routine laboratory and clinical data, ML-based tools can assist clinical decision-making and enhance the consistency and quality of sepsis management across various healthcare contexts, including ICUs with limited resources.

Published
2024
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