Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers

Ćosić, Krešimir; Popović, Siniša; Šarlija, Marko; Kesedžić, Ivan; Jovanović, Tanja

doi:10.3325/cmj.2020.61.279

Cited by 61 publications

(34 citation statements)

References 65 publications

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“…The impact of loneliness on social networks has also been studied using text mining strategies [78]. In addition, AI has been able to predict mental disorders in health care workers during the worst of the pandemic [79].…”

Section: Data Analysis Applied To Psychosocial Issues and Covid-19 Pandemic (Light Blue Cluster)mentioning

confidence: 99%

Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

Rodríguez‐Rodríguez

Rodríguez

Shirvanizadeh

et al. 2021

IJERPH

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The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.

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Section: Data Analysis Applied To Psychosocial Issues and Covid-19 Pandemic (Light Blue Cluster)mentioning

confidence: 99%

Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

Rodríguez‐Rodríguez

Rodríguez

Shirvanizadeh

et al. 2021

IJERPH

View full text Add to dashboard Cite

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“…Particularly, regarding the healthcare sector, the hospital workplace is characterized by increased level and diversity of occupational risks. In addition, the coronavirus disease 2019 (COVID- 19) pandemic and its immediate aftermath pose a significant burden of workload and a major strain on mental health of healthcare workers [3].…”

Section: Imentioning

confidence: 99%

Utilization of Machine Learning in Supporting Occupational Safety and Health Decisions in Hospital Workplace

Koklonis

Sarafidis

Vastardi

et al. 2021

Eng. Technol. Appl. Sci. Res.

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The prediction of possible future incidents or accidents and the efficiency assessment of the Occupational Safety and Health (OSH) interventions are essential for the effective protection of healthcare workers, as the occupational risks in their workplace are multiple and diverse. Machine learning algorithms have been utilized for classifying post-incident and post-accident data into the following 5 classes of events: Needlestick/Cut, Falling, Incident, Accident, and Safety. 476 event reports from Metaxa Cancer Hospital (Greece), during 2014-2019, were used to train the machine learning models. The developed models showed high predictive performance, with area under the curve range 0.950-0.990 and average accuracy of 93% on the 10-fold cross set, compared to the safety engineer’s study reports. The proposed DSS model can contribute to the prediction of incidents or accidents and efficiency evaluation of OSH interventions.

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“…The first group comprises approaches that utilize machine learning and artificial intelligence to model and predict the spread of the disease [5]- [8]. These approaches are based on underlying algorithms, such as adaptive network-based inference systems, and can yield predictions for various time periods due to a low dependence on sample data as input.…”

Section: Current State Of the Research And Literaturementioning

confidence: 99%

The Pandemic Holiday Blip in New York City

Vierlboeck

Nilchiani

Edwards

2021

IEEE Trans. Comput. Soc. Syst.

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When it comes to pandemics, such as the one caused by the Coronavirus disease COVID-19, various issues and problems have arisen for the healthcare infrastructure and institutions. With increasing number of patients in need of urgent medical care and hospitalizations, the healthcare systems and regional hospitals may approach their maximum service capacity and may face shortage of various parameters, such as supplies including PPE, medications, therapeutic devices, ventilators, beds, and many more. The article at hand describes the development and framework of a simulation model that enables the modeling and evaluation of the COVID-19 pandemic progress. To achieve this, the model dynamically mimics and simulates the developments and time-dependent behavior of various crucial parameters of the pandemic, among others, the daily infection numbers and death rate. In addition, the model enables the simulation of single events and scenarios that occur outside of the regular pandemic developments as anomalies, such as holidays. Unlike traditional models, the proposed framework is based on factors and parameters closely derived from reality, such as the contact rate of individuals, which allows for a much more realistic representation. In addition, the real connection enables the assessment of effects of various influences regarding the development and progress of the pandemic, such as hospitalization numbers over time. All the aforementioned points are possible within the simulation framework and do not require awaiting the unfolding of the effects in reality. Thus, the model is capable of dynamically predicting how different scenarios turn out. The abilities of the model are demonstrated, illustrated, and proven in a specific case study that shows the impact of holidays, such as Passover and Easter in New York City when quarantine measures might have been ignored, and an increase in extended family gatherings temporarily occurred. As a result, the simulation showed significant impacts and disproportionate number of patients in need of medical care that could be potentially detrimental in reality. For example, compared to the previous trajectory of the pandemic, for a temporary increase of 50% in the contact rate of individuals, the model showed that the total number of cases would increase by 461 090, the maximum number of required hospitalizations would rise to 79 733, and the total number of fatalities would climb by 19 125 over 90 days. In addition to its function and proven capabilities, the model can and is furthermore planned to be adapted to other areas, not necessarily only metropolitan regions in order to expand the utilization of its predictive power. Such predictions could be used to derive regulatory measures and to test various policies for COVID-19 containment.

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Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers

Cited by 61 publications

References 65 publications

Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

Utilization of Machine Learning in Supporting Occupational Safety and Health Decisions in Hospital Workplace

The Pandemic Holiday Blip in New York City

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