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1. Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts

   https://doi.org/10.1109/ICHI61247.2024.00057  

   Han, Guangzeng; Liu, Weisi; Huang, Xiaolei; Borsari, Brian (June 2024, IEEE)

   Automatic coding patient behaviors is essential to support decision making for psychotherapists during the motivational interviewing (MI), a collaborative communication intervention approach to address psychiatric issues, such as alcohol and drug addiction. While the behavior coding task has rapidly adapted language models to predict patient states during the MI sessions, lacking of domain-specific knowledge and overlooking patient-therapist interactions are major challenges in developing and deploying those models in real practice. To encounter those challenges, we introduce the Chain-of- Interaction (CoI) prompting method aiming to contextualize large language models (LLMs) for psychiatric decision support by the dyadic interactions. The CoI prompting approach systematically breaks down the coding task into three key reasoning steps, extract patient engagement, learn therapist question strategies, and integrates dyadic interactions between patients and therapists. This approach enables large language models to leverage the coding scheme, patient state, and domain knowledge for patient behavioral coding. Experiments on real-world datasets can prove the effectiveness and flexibility of our prompting method with multiple state-of-the-art LLMs over existing prompting baselines. We have conducted extensive ablation analysis and demonstrate the critical role of dyadic interactions in applying LLMs for psychotherapy behavior understanding. 

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2. Technology's Role in Fostering Therapist-Client Collaboration and Engagement with Goals

   https://doi.org/10.1145/3687055  

   Oewel, Bruna; Azizan, Nadia; Arean, Patricia A; Agapie, Elena (November 2024, Proceedings of the ACM on Human-Computer Interaction)

   Psychosocial therapies play a crucial role in effectively treating anxiety and depression. An integral aspect of these therapies involves setting goals that clients engage in outside therapy, known as therapy homework or between-session goals. Yet, clients overwhelmingly do not complete between-session goals. This study explores mental health therapists' and clients' challenges in collaborating to set and manage engagement with between-session goals and discusses how technology could better support them. We interviewed 13 therapists and 14 clients about their experiences with between-session goals. We identified therapists' needs for information to support their clients, challenges in collaboration, and how technology can support client-therapist collaboration. Therapists need in-the-moment information about clients' engagement with goals to inform their decision-making. Clients may feel reluctant to share information due to a lack of trust, embarrassment, or not knowing what to share. Clients could use technology to asynchronously communicate about sensitive topics with their therapists. Technologies could facilitate gathering in-the-moment data that supports client-therapist collaboration on goals. 

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3. Psychotherapy is Not One Thing: Simultaneous Modeling of Different Therapeutic Approaches

   https://doi.org/10.18653/v1/2022.clpsych-1.5  

   Mehta, Maitrey; Caperton, Derek; Axford, Katherine; Weitzman, Lauren; Atkins, David; Srikumar, Vivek; Imel, Zac (July 2022, Eighth Workshop on Computational Linguistics and Clinical Psychology)

   There are many different forms of psychotherapy. Itemized inventories of psychotherapeutic interventions provide a mechanism for evaluating the quality of care received by clients and for conducting research on how psychotherapy helps. However, evaluations such as these are slow, expensive, and are rarely used outside of well-funded research studies. Natural language processing research has progressed to allow automating such tasks. Yet, NLP work in this area has been restricted to evaluating a single approach to treatment, when prior research indicates therapists used a wide variety of interventions with their clients, often in the same session. In this paper, we frame this scenario as a multi-label classification task, and develop a group of models aimed at predicting a wide variety of therapist talk-turn level orientations. Our models achieve F1 macro scores of 0.5, with the class F1 ranging from 0.36 to 0.67. We present analyses which offer insights into the capability of such models to capture psychotherapy approaches, and which may complement human judgment. 

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4. Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes

   Cao, Jie; Tanana, Michael; Imel, Zac; Poitras, Eric; Atkins, David; Srikumar, Vivek (July 2019, Computational linguistics - Association for Computational Linguistics)

   Automatically analyzing dialogue can help understand and guide behavior in domains such as counseling, where interactions are largely mediated by conversation. In this paper, we study modeling behavioral codes used to asses a psychotherapy treatment style called Motivational Interviewing (MI), which is effective for addressing substance abuse and related problems. Specifically, we address the problem of providing real-time guidance to therapists with a dialogue observer that (1) categorizes therapist and client MI behavioral codes and, (2) forecasts codes for upcoming utterances to help guide the conversation and potentially alert the therapist. For both tasks, we define neural network models that build upon recent successes in dialogue modeling. Our experiments demonstrate that our models can outperform several baselines for both tasks. We also report the results of a careful analysis that reveals the impact of the various network design tradeoffs for modeling therapy dialogue. 

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5. Automated measurement of head movement synchrony during dyadic depression severity interviews

   Bhatia, S.; Hammal, Z.; Cohn, J. F.; Goecke, R. (January 2019, Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition)

   With few exceptions, most research in automated assessment of depression has considered only the patient’s behavior to the exclusion of the therapist’s behavior. We investigated the interpersonal coordination (synchrony) of head movement during patient-therapist clinical interviews. Participants with major depressive disorder were recorded in clinical interviews (Hamilton Rating Scale for Depression, HRSD) at 7-week intervals over a period of 21 weeks. For each session, patient and therapist 3D head movement was tracked from 2D videos. Head angles in the horizontal (pitch) and vertical (yaw) axes were used to measure head movement. Interpersonal coordination of head movement between patients and therapists was measured using windowed cross-correlation. Patterns of coordination in head movement were investigated using the peak picking algorithm. Changes in head movement coordination over the course of treatment were measured using a hierarchical linear model (HLM). The results indicated a strong effect for patient-therapist head movement synchrony. Within-dyad variability in head movement coordination was higher than between-dyad variability, meaning that differences over time in a dyad were higher as compared to the differences between dyads. Head movement synchrony did not change over the course of treatment. To the best of our knowledge, this study is the first attempt to analyze the mutual influence of patient-therapist head movement in relation to depression severity. 

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