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Pregled bibliografske jedinice broj: 1168820

AI-based prediction and prevention of psychological and behavioral changes in ex-COVID-19 patients


Ćosić, Krešimir; Popović, Siniša; Šarlija, Marko; Kesedžić, Ivan; Gambiraža, Mate; Dropuljić, Branimir; Mijić, Igor; Henigsberg, Neven; Jovanovic, Tanja
AI-based prediction and prevention of psychological and behavioral changes in ex-COVID-19 patients // Frontiers in psychology, 12 (2021), 782866, 18 doi:10.3389/fpsyg.2021.782866 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1168820 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
AI-based prediction and prevention of psychological and behavioral changes in ex-COVID-19 patients

Autori
Ćosić, Krešimir ; Popović, Siniša ; Šarlija, Marko ; Kesedžić, Ivan ; Gambiraža, Mate ; Dropuljić, Branimir ; Mijić, Igor ; Henigsberg, Neven ; Jovanovic, Tanja

Izvornik
Frontiers in psychology (1664-1078) 12 (2021); 782866, 18

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
artificial intelligence ; mental health disorder ; prediction and prevention ; ex-COVID-19 patients ; semantic/acoustic features ; neurophysiological features ; facial/oculometric features

Sažetak
The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of- the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex- COVID-19 patients’ susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of- the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Kliničke medicinske znanosti, Psihologija



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Medicinski fakultet, Zagreb

Poveznice na cjeloviti tekst rada:

doi www.frontiersin.org

Citiraj ovu publikaciju:

Ćosić, Krešimir; Popović, Siniša; Šarlija, Marko; Kesedžić, Ivan; Gambiraža, Mate; Dropuljić, Branimir; Mijić, Igor; Henigsberg, Neven; Jovanovic, Tanja
AI-based prediction and prevention of psychological and behavioral changes in ex-COVID-19 patients // Frontiers in psychology, 12 (2021), 782866, 18 doi:10.3389/fpsyg.2021.782866 (međunarodna recenzija, članak, znanstveni)
Ćosić, K., Popović, S., Šarlija, M., Kesedžić, I., Gambiraža, M., Dropuljić, B., Mijić, I., Henigsberg, N. & Jovanovic, T. (2021) AI-based prediction and prevention of psychological and behavioral changes in ex-COVID-19 patients. Frontiers in psychology, 12, 782866, 18 doi:10.3389/fpsyg.2021.782866.
@article{article, author = {\'{C}osi\'{c}, Kre\v{s}imir and Popovi\'{c}, Sini\v{s}a and \v{S}arlija, Marko and Kesed\v{z}i\'{c}, Ivan and Gambira\v{z}a, Mate and Dropulji\'{c}, Branimir and Miji\'{c}, Igor and Henigsberg, Neven and Jovanovic, Tanja}, year = {2021}, pages = {18}, DOI = {10.3389/fpsyg.2021.782866}, chapter = {782866}, keywords = {artificial intelligence, mental health disorder, prediction and prevention, ex-COVID-19 patients, semantic/acoustic features, neurophysiological features, facial/oculometric features}, journal = {Frontiers in psychology}, doi = {10.3389/fpsyg.2021.782866}, volume = {12}, issn = {1664-1078}, title = {AI-based prediction and prevention of psychological and behavioral changes in ex-COVID-19 patients}, keyword = {artificial intelligence, mental health disorder, prediction and prevention, ex-COVID-19 patients, semantic/acoustic features, neurophysiological features, facial/oculometric features}, chapternumber = {782866} }
@article{article, author = {\'{C}osi\'{c}, Kre\v{s}imir and Popovi\'{c}, Sini\v{s}a and \v{S}arlija, Marko and Kesed\v{z}i\'{c}, Ivan and Gambira\v{z}a, Mate and Dropulji\'{c}, Branimir and Miji\'{c}, Igor and Henigsberg, Neven and Jovanovic, Tanja}, year = {2021}, pages = {18}, DOI = {10.3389/fpsyg.2021.782866}, chapter = {782866}, keywords = {artificial intelligence, mental health disorder, prediction and prevention, ex-COVID-19 patients, semantic/acoustic features, neurophysiological features, facial/oculometric features}, journal = {Frontiers in psychology}, doi = {10.3389/fpsyg.2021.782866}, volume = {12}, issn = {1664-1078}, title = {AI-based prediction and prevention of psychological and behavioral changes in ex-COVID-19 patients}, keyword = {artificial intelligence, mental health disorder, prediction and prevention, ex-COVID-19 patients, semantic/acoustic features, neurophysiological features, facial/oculometric features}, chapternumber = {782866} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Social Science Citation Index (SSCI)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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