AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients

Scientific paper - Review paper

• Author: Krešimir Ćosić
• Author: Siniša Popović
• Author: Marko Šarlija
• Author: Ivan Kesedžić
• Author: Mate Gambiraža
• Author: Branimir Dropuljić
• Author: Igor Mijić
• Author: Neven Henigsberg
• Author: Tanja Jovanovic
• Author's institution: University of Zagreb; School of Medicine; (Department of Psychiatry and Psychological Medicine)
• Scientific / art field, discipline and subdiscipline: BIOMEDICINE AND HEALTHCARE; Clinical Medical Sciences; Psychiatry

Abstract (english)

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.

• Keywords (english): artificial intelligence; ex-COVID-19 patients; facial/oculometric features; mental health disorders; neurophysiological features; prediction and prevention; semantic/acoustic features
• Language: english
• Publication type: Scientific paper - Review paper
• Publication status: Published
• Peer review: Peer review
• Publication version: Published version
• Journal title: Frontiers in Psychology
• Numbering: vol. 12, 782866
• e-ISSN: 1664-1078
• DOI: https://doi.org/10.3389/fpsyg.2021.782866
• URN:NBN: urn:nbn:hr:105:695761
• Publication: 2021-12-28
• Document URL: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.782866/full
• Type of resource: Text
• Access conditions: Open access
• Created on: 2022-07-04 07:41:13