Authors:
André Dias
1
;
2
;
Tiago Dias
1
;
2
;
Eva Maia
1
;
2
and
Isabel Praça
1
;
2
Affiliations:
1
School of Engineering, Polytechnic of Porto, (ISEP/IPP), Porto, Portugal
;
2
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Porto, Portugal
Keyword(s):
Post-Traumatic Stress Disorder, Depression, Burnout, Smart Wearables, Emotion Recognition, Artificial Intelligence.
Abstract:
Health workers appear to have an increased risk of developing psychiatric diseases, namely Post-traumatic stress disorder (PTSD), Depression and Burnout, due to the nature of their job. In recent years, several approaches based on artificial intelligence have emerged, using facial expression, audio, text and physiological features to detect depression, stress and burnout. However, most of these solutions have limitations in their capacity to simultaneously detect multiple diseases, are not widely implemented in healthcare settings, and, in some cases, lack explainability. To address this challenge, we propose Psychiatric Disease Detection System (P2DS), a holistic rule-based system capable of detecting PTSD, Depression and Burnout in community pharmacists, combining emotion recognition, physiological and performance-related features. The set of rules developed to detect each disease is based on the most objective medical literature available, making the system explainable and suitabl
e for healthcare environments.
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