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Leung YW, Wouterloot E, Adikari A, Hong J, Asokan V, Duan L, Lam C, Kim C, Chan KP, De Silva D, Trachtenberg L, Rennie H, Wong J, Esplen MJ
Artificial Intelligence–Based Co-Facilitator (AICF) for Detecting and Monitoring Group Cohesion Outcomes in Web-Based Cancer Support Groups: Single-Arm Trial Study
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Reading between the lines - Artificial Intelligence-based Co-Facilitator (AICF) detects and monitors group cohesion outcome in online cancer support groups: A pilot study
Yvonne W Leung;
Elise Wouterloot;
Achini Adikari;
Jinny Hong;
Veenaajaa Asokan;
Lauren Duan;
Claire Lam;
Carlina Kim;
Kai P Chan;
Daswin De Silva;
Lianne Trachtenberg;
Heather Rennie;
Jiahui Wong;
Mary Jane Esplen
ABSTRACT
Background:
Commonly offered as supportive care, therapist-led online support groups (OSGs) are a cost-effective way to provide support to individuals affected by cancer. One important indicator of a successful OSG session is group cohesion; however, monitoring group cohesion can be challenging due to the lack of non-verbal cues and in-person interactions in text-based OSGs. The Artificial Intelligence-based Co-Facilitator (AICF) was designed to contextually identify therapeutic outcomes from conversations and produce real-time analytics.
Objective:
The aim of this study was to develop a method to train and evaluate AICF’s capacity to monitor group cohesion.
Methods:
Human scorers used a confusion matrix to evaluate the performance of AICF. AICF performance was also compared against the natural language processing software Linguistic Inquiry Word Count (LIWC).
Results:
AICF was trained on 80,000 messages obtained from Cancer Chat Canada. We tested AICF on 34,048 messages. Human experts scored 6,797 (20%) of the messages to evaluate the ability of AICF to classify group cohesion. Results showed that machine-learning algorithms combined with human input can detect group cohesion, a clinically meaningful indicator of effective OSGs. After re-training with human input, AICF reached a F1-score of 0.82. AICF performed slightly better at identifying group cohesion compared to LIWC.
Conclusions:
AICF has the potential to assist therapists by detecting discord in the group amenable to real-time intervention. Overall, AICF presents a unique opportunity to strengthen patient-centered care in virtual settings by attending to individual needs.
Citation
Please cite as:
Leung YW, Wouterloot E, Adikari A, Hong J, Asokan V, Duan L, Lam C, Kim C, Chan KP, De Silva D, Trachtenberg L, Rennie H, Wong J, Esplen MJ
Artificial Intelligence–Based Co-Facilitator (AICF) for Detecting and Monitoring Group Cohesion Outcomes in Web-Based Cancer Support Groups: Single-Arm Trial Study