TY - GEN
T1 - First insights on a passive major depressive disorder prediction system with incorporated conversational chatbot
AU - Delahunty, Fionn
AU - Wood, Ian D.
AU - Arcan, Mihael
N1 - Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2018
Y1 - 2018
N2 - Almost 50% of cases of major depressive disorder go undiagnosed. In this paper, we propose a passive diagnostic system that combines the areas of clinical psychology, machine learning and conversational dialogue systems. We have trained a dialogue system, powered by sequence-to-sequence neural networks that can have a real-time conversation with individuals. In tandem, we have developed specific machine learning classifiers that monitor the conversation and predict the presence or absence of certain crucial depression symptoms. This would facilitate real-time instant crisis support for those suffering from depression. Our evaluation metrics have suggested this could be a positive future direction of research in both developing more human like chatbots and identifying depression in written text. We hope this work may additionally have practical implications in the area of crisis support services for mental health organisations.
AB - Almost 50% of cases of major depressive disorder go undiagnosed. In this paper, we propose a passive diagnostic system that combines the areas of clinical psychology, machine learning and conversational dialogue systems. We have trained a dialogue system, powered by sequence-to-sequence neural networks that can have a real-time conversation with individuals. In tandem, we have developed specific machine learning classifiers that monitor the conversation and predict the presence or absence of certain crucial depression symptoms. This would facilitate real-time instant crisis support for those suffering from depression. Our evaluation metrics have suggested this could be a positive future direction of research in both developing more human like chatbots and identifying depression in written text. We hope this work may additionally have practical implications in the area of crisis support services for mental health organisations.
KW - Depression
KW - Social Media
KW - Conversational Chatbot
UR - http://ceur-ws.org/Vol-2259/
UR - https://www.scopus.com/pages/publications/85058207398
M3 - Conference proceeding contribution
T3 - CEUR Workshop Proceedings
SP - 1
EP - 12
BT - Proceedings for the 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science
A2 - Brennan, Rob
A2 - Beel, Joeran
A2 - Byrne, Ruth
A2 - Debattista, Jeremy
A2 - Crotti Junior, Ademar
PB - CEUR Workshop Proceedings
CY - Aachen, Germany
T2 - 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science
Y2 - 6 December 2018 through 8 December 2018
ER -