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.
Highlights
The COVID-19 pandemic has created enormous stressors across the globe by elevating rates of anxiety, depression, posttraumatic stress disorder (PTSD), or even suicidal behaviors (Bäuerle et al, 2020; Sherman et al, 2020; Wu T. et al, 2020; Cheung et al, 2021; Czeisler et al, 2021)
Diverse forms of data for inclusion into such heterogeneous data sets have been encountered in several publications on artificial intelligence (AI) in mental health (Eyre et al, 2016; Garcia-Ceja et al, 2018; Graham et al, 2019): various rating scales used by patients and clinicians, electronic health records (EHRs), brain imaging data, genomics, blood biomarkers, data collected while using smartphones, textual posts from social media platforms, speech and language audio data, facial video data, multiple peripheral physiological signals etc
Assuming that mentally vulnerable individuals affected by COVID-19 pandemic vs. resilient individuals have different profile of neuro-psycho-physiological indicators together with differences in personality traits and psycho-behavioral status, a comprehensive AI-based predictive approach should lead to better detection of high-risk ex-COVID-19 patients (RamosLima et al, 2020) and more effective prevention strategies
Summary
The COVID-19 pandemic has created enormous stressors across the globe by elevating rates of anxiety, depression, posttraumatic stress disorder (PTSD), or even suicidal behaviors (Bäuerle et al, 2020; Sherman et al, 2020; Wu T. et al, 2020; Cheung et al, 2021; Czeisler et al, 2021). A major strength of statistical and AI methods is their ability to identify specific non-obvious patterns beyond human computational capabilities within highly heterogeneous multimodal sets of data relevant for mental health assessment (Eyre et al, 2016; Garcia-Ceja et al, 2018), which is essential for early detection of ex-COVID-19 patients at high risk of mental health deterioration. Assuming that mentally vulnerable individuals affected by COVID-19 pandemic vs resilient individuals have different profile of neuro-psycho-physiological indicators together with differences in personality traits and psycho-behavioral status, a comprehensive AI-based predictive approach should lead to better detection of high-risk ex-COVID-19 patients (RamosLima et al, 2020) and more effective prevention strategies. The following sections present the concept of prediction of potential chronic mental health diseases among ex-COVID19 patients, which includes comprehensive psychological, neurophysiological, semantic, acoustic, and facial/oculometric measurements and features, as well as the concept of mental health disorder prevention using AI methods
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