Pregled bibliografske jedinice broj: 1091955
Artificial intelligence in prediction of mental health disorders induced by the COVID19 pandemic among health care workers
Artificial intelligence in prediction of mental health disorders induced by the COVID19 pandemic among health care workers // Croatian medical journal, 61 (2020), 3; 279-288 doi:10.3325/cmj.2020.61.279 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1091955 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Artificial intelligence in prediction of mental
health disorders induced by the COVID19 pandemic
among health care workers
Autori
Ćosić, Krešimir ; Popović, Siniša ; Šarlija, Marko ; Kesedžić, Ivan ; Jovanovic, Tanja
Izvornik
Croatian medical journal (0353-9504) 61
(2020), 3;
279-288
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
artificial intelligence ; COVID-19 pandemic ; prediction of mental health disorders ; health care workers ; neuro-psycho-physiological features
Sažetak
The coronavirus disease 2019 (COVID-19) pandemic and its immediate aftermath present a serious threat to the mental health of health care workers (HCWs), who may develop elevated rates of anxiety, depression, posttraumatic stress disorder, or even suicidal behaviors. Therefore, the aim of this article is to address the problem of prevention of HCWs’ mental health disorders by early prediction of individuals at a higher risk of later chronic mental health disorders due to high distress during the COVID-19 pandemic. The article proposes a methodology for prediction of mental health disorders induced by the pandemic, which includes: Phase 1) objective assessment of the intensity of HCWs’ stressor exposure, based on information retrieved from hospital archives and clinical records ; Phase 2) subjective self-report assessment of stress during the COVID-19 pandemic experienced by HCWs and their relevant psychological traits ; Phase 3) design and development of appropriate multimodal stimulation paradigms to optimally elicit specific neuro-physiological reactions ; Phase 4) objective measurement and computation of relevant neuro- physiological predictor features based on HCWs’ reactions ; and Phase 5) statistical and machine learning analysis of highly heterogeneous data sets obtained in previous phases. The proposed methodology aims to expand traditionally used subjective self-report predictors of mental health disorders with more objective metrics, which is aligned with the recent literature related to predictive modeling based on artificial intelligence. This approach is generally applicable to all those exposed to high levels of stress during the COVID19 pandemic and might assist mental health practitioners to make diagnoses more quickly and accurately.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Kliničke medicinske znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Profili:
Ivan Kesedžić
(autor)
Siniša Popović
(autor)
Krešimir Ćosić
(autor)
Marko Šarlija
(autor)
Tanja Jovanović
(autor)
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
- MEDLINE