Occupational burnout syndrome and post-traumatic stress among healthcare professionals during the novel coronavirus disease 2019 (COVID-19) pandemic

J Raudenská, V Steinerová, A Javůrková, I Urits… - Best Practice & …, 2020 - Elsevier
This comprehensive review aims to explain the potential impact of coronavirus disease 2019
(COVID-19) on mental wellbeing of healthcare professionals (HCPs). Based on up-to-date …

Neurobiology of BDNF in fear memory, sensitivity to stress, and stress-related disorders

M Notaras, M van den Buuse - Molecular psychiatry, 2020 - nature.com
Brain-derived neurotrophic factor (BDNF) is widely accepted for its involvement in resilience
and antidepressant drug action, is a common genetic locus of risk for mental illnesses, and …

Traumatic stress in the age of COVID-19: A call to close critical gaps and adapt to new realities.

D Horesh, AD Brown - Psychological Trauma: Theory, Research …, 2020 - psycnet.apa.org
Abstract The Issue: Coronavirus-19 (COVID-19) is transforming every aspect of our lives.
Identified in late 2019, COVID-19 quickly became characterized as a global pandemic by …

Improving mental health services: A 50-year journey from randomized experiments to artificial intelligence and precision mental health

L Bickman - Administration and Policy in Mental Health and Mental …, 2020 - Springer
This conceptual paper describes the current state of mental health services, identifies critical
problems, and suggests how to solve them. I focus on the potential contributions of artificial …

A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor

K Schultebraucks, AY Shalev, V Michopoulos… - Nature medicine, 2020 - nature.com
Annually, approximately 30 million patients are discharged from the emergency department
(ED) after a traumatic event. These patients are at substantial psychiatric risk, with …

[HTML][HTML] Review of machine learning algorithms for diagnosing mental illness

G Cho, J Yim, Y Choi, J Ko, SH Lee - Psychiatry investigation, 2019 - ncbi.nlm.nih.gov
Objective Enhanced technology in computer and internet has driven scale and quality of
data to be improved in various areas including healthcare sectors. Machine Learning (ML) …

Estimating the risk of PTSD in recent trauma survivors: results of the International Consortium to Predict PTSD (ICPP)

AY Shalev, M Gevonden, A Ratanatharathorn… - World …, 2019 - Wiley Online Library
A timely determination of the risk of post‐traumatic stress disorder (PTSD) is a prerequisite
for efficient service delivery and prevention. We provide a risk estimate tool allowing a …

Implementing precision psychiatry: a systematic review of individualized prediction models for clinical practice

G Salazar de Pablo, E Studerus… - Schizophrenia …, 2021 - academic.oup.com
Background The impact of precision psychiatry for clinical practice has not been
systematically appraised. This study aims to provide a comprehensive review of validated …

Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry

AMY Tai, A Albuquerque, NE Carmona… - Artificial intelligence in …, 2019 - Elsevier
Introduction Machine learning capability holds promise to inform disease models, the
discovery and development of novel disease modifying therapeutics and prevention …

Applications of artificial intelligence− machine learning for detection of stress: a critical overview

AFA Mentis, D Lee, P Roussos - Molecular Psychiatry, 2024 - nature.com
Psychological distress is a major contributor to human physiology and pathophysiology, and
it has been linked to several conditions, such as auto-immune diseases, metabolic …