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An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff
The mental and physical well-being of healthcare workers is being affected by global COVID-19. The pandemic has impacted the mental health of medical staff in numerous ways. However, most studies have examined sleep disorders, depression, anxiety, and post-traumatic problems in healthcare workers du...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249891/ https://www.ncbi.nlm.nih.gov/pubmed/37289778 http://dx.doi.org/10.1371/journal.pone.0286155 |
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author | Irfan, Muhammad Shaf, Ahmad Ali, Tariq Zafar, Maryam Rahman, Saifur I. Hendi, Meiaad Ali M. Baeshen, Shatha Abduh Maghfouri, Maryam Mohammed Mastoor Alahmari, Hailah Saeed Mohammed Shahhar, Ftimah Ahmed Ibrahim Shahhar, Nujud Ahmed Ibrahim Halawi, Amnah Sultan Mahnashi, Fatima Hussen Alqhtani, Samar M. Ali M., Bahran Taghreed |
author_facet | Irfan, Muhammad Shaf, Ahmad Ali, Tariq Zafar, Maryam Rahman, Saifur I. Hendi, Meiaad Ali M. Baeshen, Shatha Abduh Maghfouri, Maryam Mohammed Mastoor Alahmari, Hailah Saeed Mohammed Shahhar, Ftimah Ahmed Ibrahim Shahhar, Nujud Ahmed Ibrahim Halawi, Amnah Sultan Mahnashi, Fatima Hussen Alqhtani, Samar M. Ali M., Bahran Taghreed |
author_sort | Irfan, Muhammad |
collection | PubMed |
description | The mental and physical well-being of healthcare workers is being affected by global COVID-19. The pandemic has impacted the mental health of medical staff in numerous ways. However, most studies have examined sleep disorders, depression, anxiety, and post-traumatic problems in healthcare workers during and after the outbreak. The study’s objective is to evaluate COVID-19’s psychological effects on healthcare professionals of Saudi Arabia. Healthcare professionals from tertiary teaching hospitals were invited to participate in the survey. Almost 610 people participated in the survey, of whom 74.3% were female, and 25.7% were male. The survey included the ratio of Saudi and non-Saudi participants. The study has utilized multiple machine learning algorithms and techniques such as Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The machine learning models offer 99% accuracy for the credentials added to the dataset. The dataset covers several aspects of medical workers, such as profession, working area, years of experience, nationalities, and sleeping patterns. The study concluded that most of the participants who belonged to the medical department faced varying degrees of anxiety and depression. The results reveal considerable rates of anxiety and depression in Saudi frontline workers. |
format | Online Article Text |
id | pubmed-10249891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-102498912023-06-09 An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff Irfan, Muhammad Shaf, Ahmad Ali, Tariq Zafar, Maryam Rahman, Saifur I. Hendi, Meiaad Ali M. Baeshen, Shatha Abduh Maghfouri, Maryam Mohammed Mastoor Alahmari, Hailah Saeed Mohammed Shahhar, Ftimah Ahmed Ibrahim Shahhar, Nujud Ahmed Ibrahim Halawi, Amnah Sultan Mahnashi, Fatima Hussen Alqhtani, Samar M. Ali M., Bahran Taghreed PLoS One Research Article The mental and physical well-being of healthcare workers is being affected by global COVID-19. The pandemic has impacted the mental health of medical staff in numerous ways. However, most studies have examined sleep disorders, depression, anxiety, and post-traumatic problems in healthcare workers during and after the outbreak. The study’s objective is to evaluate COVID-19’s psychological effects on healthcare professionals of Saudi Arabia. Healthcare professionals from tertiary teaching hospitals were invited to participate in the survey. Almost 610 people participated in the survey, of whom 74.3% were female, and 25.7% were male. The survey included the ratio of Saudi and non-Saudi participants. The study has utilized multiple machine learning algorithms and techniques such as Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The machine learning models offer 99% accuracy for the credentials added to the dataset. The dataset covers several aspects of medical workers, such as profession, working area, years of experience, nationalities, and sleeping patterns. The study concluded that most of the participants who belonged to the medical department faced varying degrees of anxiety and depression. The results reveal considerable rates of anxiety and depression in Saudi frontline workers. Public Library of Science 2023-06-08 /pmc/articles/PMC10249891/ /pubmed/37289778 http://dx.doi.org/10.1371/journal.pone.0286155 Text en © 2023 Irfan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Irfan, Muhammad Shaf, Ahmad Ali, Tariq Zafar, Maryam Rahman, Saifur I. Hendi, Meiaad Ali M. Baeshen, Shatha Abduh Maghfouri, Maryam Mohammed Mastoor Alahmari, Hailah Saeed Mohammed Shahhar, Ftimah Ahmed Ibrahim Shahhar, Nujud Ahmed Ibrahim Halawi, Amnah Sultan Mahnashi, Fatima Hussen Alqhtani, Samar M. Ali M., Bahran Taghreed An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff |
title | An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff |
title_full | An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff |
title_fullStr | An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff |
title_full_unstemmed | An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff |
title_short | An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff |
title_sort | intelligent framework to measure the effects of covid-19 on the mental health of medical staff |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249891/ https://www.ncbi.nlm.nih.gov/pubmed/37289778 http://dx.doi.org/10.1371/journal.pone.0286155 |
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