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Review
. 2021 Dec 28:12:782866.
doi: 10.3389/fpsyg.2021.782866. eCollection 2021.

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

Krešimir Ćosić  1 , Siniša Popović  1 , Marko Šarlija  1 , Ivan Kesedžić  1 , Mate Gambiraža  1 , Branimir Dropuljić  1 , Igor Mijić  1 , Neven Henigsberg  2 , Tanja Jovanovic  3
Affiliations
Review

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

Krešimir Ćosić et al. Front Psychol. .

Abstract

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: artificial intelligence; ex-COVID-19 patients; facial/oculometric features; mental health disorders; neurophysiological features; prediction and prevention; semantic/acoustic features.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Illustration of time-synchronized multimodal input stimulation and multimodal output response of ex-COVID-19 patient during prediction protocol.
FIGURE 2
FIGURE 2
Multimodal data acquisition as input for real-time and offline feature computation. Illustrated subset of selected features includes: General Health Questionnaire (GHQ-12), Connor-Davidson Resilience Scale (CD-RISC), COVID-19 Peritraumatic Distress Index (CPDI); NLP feature LIWC “sad,” voice fundamental frequency (F0), voice root mean square (RMS); respiratory sinus arrhythmia (RSA), EMG-based startle reactivity (SREMG), prefrontal cortex activity (PFCact); saccadic peak velocity (SPV), pupil dilation (PD), a feature related to facial action coding system (FACS).
FIGURE 3
FIGURE 3
Concept of the integration of AI-based tools and methods with various preventive intervention strategies.
See this image and copyright information in PMC

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