A Statistical Learning Approach to Evaluate Factors Associated With Post-Traumatic Stress Symptoms in Physicians: Insights From the COVID-19 Pandemic

Mukherjee, Sayanti; Rintamaki, Lance S.; Shucard, Janet L.; Wei, Zhiyuan; Carlasare, Lindsey E.; Sinsky, Christine A.

doi:10.1109/access.2022.3217770

Cited by 3 publications

(6 citation statements)

References 118 publications

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“…A study of the Post-Traumatic Stress Symptoms (PTSS) in physicians on the frontlines of COVID-19 was presented in [12]. The objectives of the study were (1) to evaluate the symptoms of PTSS among frontline physicians compared to second-line ones, (2) to identify the higher-risk group among them, (3) to predict the PTSS risk in the higher-risk group using ML algorithms, and (4) determine possible patterns between the predictors.…”

Section: A Literature Reviewmentioning

confidence: 99%

Using a Classifier Ensemble for Preventing Burnout in University Students: A Study Case in Valparaíso

Gonzalo,

Nicolás,

Nicolás

et al. 2023

IEEE Access

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University students are constantly exposed to high-tension situations and peaks of stress caused by the difficulty from their many responsibilities. These situations can produce disorders in students, such as psychosomatic, behavioral, or emotional disorders. Burnout is a work-related syndrome that can be observed in students. This syndrome considers depersonalization, emotional exhaustion, and diminished feelings of personal accomplishment. We aim to detect anxiety, depression, and burnout symptoms in university students to prevent further negative consequences. For this, we design a questionnaire using well-known instruments to detect these signs. We propose to use an ensemble of classifiers, including random forest and artificial neural networks, to predict a set of four possible disturbances in persons. The proposal will be used in the Human Place project to suggest strategies to tackle four disturbance types. This study considers the participation of 93 persons from the Valparaiso region in Chile. Results show that the evaluated algorithms can predict the presence or absence of the disturbances with high accuracy levels and a low number of false negative cases. We also present a detailed analysis of which questions were relevant in the classification task of each algorithm.

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Section: A Literature Reviewmentioning

confidence: 99%

Using a Classifier Ensemble for Preventing Burnout in University Students: A Study Case in Valparaíso

Gonzalo,

Nicolás,

Nicolás

et al. 2023

IEEE Access

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“…Research has demonstrated that depersonalization is the most serious aspect of burnout in terms of its impact on functioning (4) and that exhaustion, on its own, does not constitute burnout, as evidenced by its quick reversal when staff are provided with more rest (7). Finally, although the PTSD scale used cannot be regarded as diagnostic, the findings did suggest that these symptoms were elevated, as they have been found to be in other large samples of critical care workers in relation to working during the pandemic (8).…”

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confidence: 95%

“…Given the predominance of cardiovascular dysfunction and coronary anomalies identified in early reports of MIS-C (12), much of the initial literature describing postdischarge outcomes following MIS-C appropriately focused on cardiac outcomes. Reassuringly, studies evaluating patients 6 to 12 months following MIS-C have demonstrated normalization of cardiac function, resolution of coronary dilation or aneurysms, and no signs of persistent myocardial inflammation in most patients (2,4,8,9). Activity impairment and exercise intolerance have been more commonly reported in the months following hospitalization for MIS-C (4,6).…”

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confidence: 99%

“…A number of new insights related to this dilemma are provided in another recent study, which used statistical learning algorithms to examine risk and protective factors associated with symptoms of PTSD in n = 1,017 physicians' , after the second wave of COVID-19 in the United States (8). The authors found that around 30% of their sample exhibited probable or borderline PTSD and that certain PTSD symptoms discriminated between those who were frontline in the pandemic and those who did not treat COVID patients directly.…”

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confidence: 99%

“…Among the numerous commendable elements of the robust multicenter research collaborations that grew out of the COVID-19 pandemic (2-7) was the foresight to prioritize the study of long-term outcomes from the beginning. Within the first 18 months of the pandemic, literature was already beginning to emerge describing postdischarge outcomes for children surviving acute COVID-19 and multisystem inflammatory syndrome in children (MIS-C) (8)(9)(10)(11).…”

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confidence: 99%

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“How Was Work Today?”*

Colville

2023

Pediatric Critical Care Medicine

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Van Den Helm and colleagues (8) have identified a path for further investigation. If, indeed, VWF is shown to be a "canary in the coal mine" (i.e., an early predictor of bleeding or thrombosis risk), then VWF could also become a potential target for therapeutic intervention. Further investigations involving broader in vitro assessments of changes in both plasma-and platelet-VWF and their interactions with platelets, ECs, and RBCs under shear-stress conditions followed by clinical studies including a larger number of patients may be fruitful paths to pursue.

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An Innovative Artificial Intelligence Based Decision Making System for Public Health Crisis Virtual Reality Rehabilitation

Ghanimi,

Ayasrah,

Jadala

et al. 2025

JMC

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The COVID-19 disease caused by the SARS-CoV-2 virus was declared by the World Health Organization (WHO) as a spreadable viral disease. During the COVID pandemic, there was difficulty in notifying the Decision-Making System (DMS) about the rapid and precise triage of patients admitted to the emergency wards. As a method to achieve the aim and develop digital healthcare revolutions in data and analytics, digital healthcare information was established. Artificial Intelligence (AI) is a robust automation tool for sustainability in the context of the COVID-19 health crisis on big datasets. Besides, the gap between AI investment and commercial real-time application, which are the initial digital technology development curves, has been identified. It was discovered that AI’s new applications are grounded in Digital Transformation Mapping (DTM) for the DMS of Health Crises. The fast inventions in AI and Machine Learning (ML) have implications for amazingly preventive and clinical healthcare, and for the association, ML was developed as a predictable attention. Billions of smartphones, massive online datasets, linked wireless wearable devices, comparatively cost-effective computing resources and improved ML and Nural Language Processing (NLP) are leveraged by these rapid responses, with the trained dataset of 65% and evaluated in the other 35%, the renowned ML models for structured data like Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), Logistic Regressive Tree (LRT), Decision Tree (DT), Stochastic Gradient Booster (SGB), and Random Forest (RF) are used for simulating new unidentified data. AI-DTM challenges DMS of Health Crises (COVID-19) and the drawbacks of critically contributing risk factors to healthcare diseases. Meanwhile, a comprehensive collection of healthcare datasets over what is spreadable would be required to save human lives, train AI, and limit cost-effective health risks.

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A Statistical Learning Approach to Evaluate Factors Associated With Post-Traumatic Stress Symptoms in Physicians: Insights From the COVID-19 Pandemic

Cited by 3 publications

References 118 publications

Using a Classifier Ensemble for Preventing Burnout in University Students: A Study Case in Valparaíso

Using a Classifier Ensemble for Preventing Burnout in University Students: A Study Case in Valparaíso

“How Was Work Today?”*

An Innovative Artificial Intelligence Based Decision Making System for Public Health Crisis Virtual Reality Rehabilitation

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