TY - GEN
T1 - Sensor-Based Virtual Reality for Clinical Decision Support in the Assessment of Mental Disorders
AU - Niederriter, Bryant
AU - Rong, Alice
AU - Aqlan, Faisal
AU - Yang, Hui
N1 - Funding Information:
The authors of this work would like to acknowledge the NSF I/UCRC Center for Healthcare Organization Transformation (CHOT), NSF I/UCRC award #1624727 for funding this research. Any opinions, findings, or conclusions found in this paper are those of the authors and do not necessarily reflect the views of the sponsors. The authors would like to thank all members of the research groups at Penn State Erie and Penn State University Park for their support.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/8
Y1 - 2020/8
N2 - Recent reports show that 1 in 4 families has at least one member with a mental disorder. In the current practice, most diagnosis methods in psychiatry are based on clinical interviews and questionnaires, which are subjective and can lead to recalls and interviewer biases. In the healthcare context, Virtual Reality (VR) has shown a strong potential to improve decision making and help patients to better connect with reality, cope with pain, and overcome mental disorders such as anxiety and depression. This study integrates sensing technology (i.e., eye tracking) with a VR simulation of healthcare environments to improve the clinical decision-support system for diagnosis and assessment of mental disorders. Traditional scenario-based patient simulations are used as a basis for the development of VR modules. Data collected via the eye-tracking sensing technology are utilized to develop analytical models for predicting the risk of mental illness. Moreover, artificial intelligence (AI) tools for VR-based healthcare training help medical students learn faster and make smarter decisions. This research helps contribute to improved population health by developing new methods for promoting health and effectively predicting and treating mental disorders.
AB - Recent reports show that 1 in 4 families has at least one member with a mental disorder. In the current practice, most diagnosis methods in psychiatry are based on clinical interviews and questionnaires, which are subjective and can lead to recalls and interviewer biases. In the healthcare context, Virtual Reality (VR) has shown a strong potential to improve decision making and help patients to better connect with reality, cope with pain, and overcome mental disorders such as anxiety and depression. This study integrates sensing technology (i.e., eye tracking) with a VR simulation of healthcare environments to improve the clinical decision-support system for diagnosis and assessment of mental disorders. Traditional scenario-based patient simulations are used as a basis for the development of VR modules. Data collected via the eye-tracking sensing technology are utilized to develop analytical models for predicting the risk of mental illness. Moreover, artificial intelligence (AI) tools for VR-based healthcare training help medical students learn faster and make smarter decisions. This research helps contribute to improved population health by developing new methods for promoting health and effectively predicting and treating mental disorders.
UR - http://www.scopus.com/inward/record.url?scp=85096911413&partnerID=8YFLogxK
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U2 - 10.1109/CoG47356.2020.9231896
DO - 10.1109/CoG47356.2020.9231896
M3 - Conference contribution
AN - SCOPUS:85096911413
T3 - IEEE Conference on Computational Intelligence and Games, CIG
SP - 666
EP - 669
BT - IEEE Conference on Games, CoG 2020
PB - IEEE Computer Society
T2 - 2020 IEEE Conference on Games, CoG 2020
Y2 - 24 August 2020 through 27 August 2020
ER -