Identifying Distorted Thinking in Patient-Therapist Text Message Exchanges by Leveraging Dynamic Multi-Turn Context

Kevin Lybarger, Justin Tauscher, Xiruo Ding, Dror Ben-zeev, Trevor Cohen


Abstract
There is growing evidence that mobile text message exchanges between patients and therapists can augment traditional cognitive behavioral therapy. The automatic characterization of patient thinking patterns in this asynchronous text communication may guide treatment and assist in therapist training. In this work, we automatically identify distorted thinking in text-based patient-therapist exchanges, investigating the role of conversation history (context) in distortion prediction. We identify six unique types of cognitive distortions and utilize BERT-based architectures to represent text messages within the context of the conversation. We propose two approaches for leveraging dynamic conversation context in model training. By representing the text messages within the context of the broader patient-therapist conversation, the models better emulate the therapist’s task of recognizing distorted thoughts. This multi-turn classification approach also leverages the clustering of distorted thinking in the conversation timeline. We demonstrate that including conversation context, including the proposed dynamic context methods, improves distortion prediction performance. The proposed architectures and conversation encoding approaches achieve performance comparable to inter-rater agreement. The presence of any distorted thinking is identified with relatively high performance at 0.73 F1, significantly outperforming the best context-agnostic models (0.68 F1).
Anthology ID:
2022.clpsych-1.11
Volume:
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
Month:
July
Year:
2022
Address:
Seattle, USA
Editors:
Ayah Zirikly, Dana Atzil-Slonim, Maria Liakata, Steven Bedrick, Bart Desmet, Molly Ireland, Andrew Lee, Sean MacAvaney, Matthew Purver, Rebecca Resnik, Andrew Yates
Venue:
CLPsych
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
126–136
Language:
URL:
https://aclanthology.org/2022.clpsych-1.11
DOI:
10.18653/v1/2022.clpsych-1.11
Bibkey:
Cite (ACL):
Kevin Lybarger, Justin Tauscher, Xiruo Ding, Dror Ben-zeev, and Trevor Cohen. 2022. Identifying Distorted Thinking in Patient-Therapist Text Message Exchanges by Leveraging Dynamic Multi-Turn Context. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pages 126–136, Seattle, USA. Association for Computational Linguistics.
Cite (Informal):
Identifying Distorted Thinking in Patient-Therapist Text Message Exchanges by Leveraging Dynamic Multi-Turn Context (Lybarger et al., CLPsych 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.clpsych-1.11.pdf
Video:
 https://aclanthology.org/2022.clpsych-1.11.mp4