COMPASS: Computational mapping of patient-therapist alliance strategies with language modeling
Abstract The therapeutic working alliance is a critical predictor of psychotherapy success. Traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. In this paper, we present COMPASS, a novel framework to directly infer the therapeutic working al...
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025-05-01
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| Series: | Translational Psychiatry |
| Online Access: | https://doi.org/10.1038/s41398-025-03379-3 |
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| _version_ | 1849310292827176960 |
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| author | Baihan Lin Djallel Bouneffouf Yulia Landa Rachel Jespersen Cheryl Corcoran Guillermo Cecchi |
| author_facet | Baihan Lin Djallel Bouneffouf Yulia Landa Rachel Jespersen Cheryl Corcoran Guillermo Cecchi |
| author_sort | Baihan Lin |
| collection | DOAJ |
| description | Abstract The therapeutic working alliance is a critical predictor of psychotherapy success. Traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. In this paper, we present COMPASS, a novel framework to directly infer the therapeutic working alliance from the natural language used in psychotherapy sessions. Our approach leverages advanced large language models (LLMs) to analyze session transcripts and map them to distributed representations. These representations capture the semantic similarities between the dialogues and psychometric instruments, such as the Working Alliance Inventory. Analyzing a dataset of over 950 sessions spanning diverse psychiatric conditions -- including anxiety (N = 498), depression (N = 377), schizophrenia (N = 71), and suicidal tendencies (N = 12) -- collected between 1970 and 2012, we demonstrate the effectiveness of our method in providing fine-grained mapping of patient-therapist alignment trajectories, offering interpretable insights for clinical practice, and identifying emerging patterns related to the condition being treated. By employing various deep learning-based topic modeling techniques in combination with prompting generative language models, we analyze the topical characteristics of different psychiatric conditions and how these topics evolve during each turn of the conversation. This integrated framework enhances the understanding of therapeutic interactions, enables timely feedback for therapists on the quality of therapeutic relationships, and provides clear, actionable insights to improve the effectiveness of psychotherapy. |
| format | Article |
| id | doaj-art-aada23d170634e2eb9bb65bfd60f4e8b |
| institution | Kabale University |
| issn | 2158-3188 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Translational Psychiatry |
| spelling | doaj-art-aada23d170634e2eb9bb65bfd60f4e8b2025-08-20T03:53:46ZengNature Publishing GroupTranslational Psychiatry2158-31882025-05-0115111510.1038/s41398-025-03379-3COMPASS: Computational mapping of patient-therapist alliance strategies with language modelingBaihan Lin0Djallel Bouneffouf1Yulia Landa2Rachel Jespersen3Cheryl Corcoran4Guillermo Cecchi5Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount SinaiIBM Research, T.J. Watson Research CenterDepartment of Psychiatry, Icahn School of Medicine at Mount SinaiDepartment of Psychiatry, Icahn School of Medicine at Mount SinaiDepartment of Psychiatry, Icahn School of Medicine at Mount SinaiIBM Research, T.J. Watson Research CenterAbstract The therapeutic working alliance is a critical predictor of psychotherapy success. Traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. In this paper, we present COMPASS, a novel framework to directly infer the therapeutic working alliance from the natural language used in psychotherapy sessions. Our approach leverages advanced large language models (LLMs) to analyze session transcripts and map them to distributed representations. These representations capture the semantic similarities between the dialogues and psychometric instruments, such as the Working Alliance Inventory. Analyzing a dataset of over 950 sessions spanning diverse psychiatric conditions -- including anxiety (N = 498), depression (N = 377), schizophrenia (N = 71), and suicidal tendencies (N = 12) -- collected between 1970 and 2012, we demonstrate the effectiveness of our method in providing fine-grained mapping of patient-therapist alignment trajectories, offering interpretable insights for clinical practice, and identifying emerging patterns related to the condition being treated. By employing various deep learning-based topic modeling techniques in combination with prompting generative language models, we analyze the topical characteristics of different psychiatric conditions and how these topics evolve during each turn of the conversation. This integrated framework enhances the understanding of therapeutic interactions, enables timely feedback for therapists on the quality of therapeutic relationships, and provides clear, actionable insights to improve the effectiveness of psychotherapy.https://doi.org/10.1038/s41398-025-03379-3 |
| spellingShingle | Baihan Lin Djallel Bouneffouf Yulia Landa Rachel Jespersen Cheryl Corcoran Guillermo Cecchi COMPASS: Computational mapping of patient-therapist alliance strategies with language modeling Translational Psychiatry |
| title | COMPASS: Computational mapping of patient-therapist alliance strategies with language modeling |
| title_full | COMPASS: Computational mapping of patient-therapist alliance strategies with language modeling |
| title_fullStr | COMPASS: Computational mapping of patient-therapist alliance strategies with language modeling |
| title_full_unstemmed | COMPASS: Computational mapping of patient-therapist alliance strategies with language modeling |
| title_short | COMPASS: Computational mapping of patient-therapist alliance strategies with language modeling |
| title_sort | compass computational mapping of patient therapist alliance strategies with language modeling |
| url | https://doi.org/10.1038/s41398-025-03379-3 |
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