COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies
with Language Modeling
- URL: http://arxiv.org/abs/2402.14701v1
- Date: Thu, 22 Feb 2024 16:56:44 GMT
- Title: COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies
with Language Modeling
- Authors: Baihan Lin, Djallel Bouneffouf, Yulia Landa, Rachel Jespersen, Cheryl
Corcoran, Guillermo Cecchi
- Abstract summary: We present a novel framework to infer the therapeutic working alliance from the natural language used in psychotherapy sessions.
Our approach utilizes advanced large language models to analyze transcripts of psychotherapy sessions and compare them with distributed representations of statements in the working alliance inventory.
- Score: 14.753147070569653
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The therapeutic working alliance is a critical factor in predicting the
success of psychotherapy treatment. 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 utilizes advanced large language models to analyze transcripts of
psychotherapy sessions and compare them with distributed representations of
statements in the working alliance inventory. Analyzing a dataset of over 950
sessions covering diverse psychiatric conditions, we demonstrate the
effectiveness of our method in microscopically mapping patient-therapist
alignment trajectories and providing interpretability for clinical psychiatry
and in identifying emerging patterns related to the condition being treated. By
employing various neural topic modeling techniques in combination with
generative language prompting, we analyze the topical characteristics of
different psychiatric conditions and incorporate temporal modeling to capture
the evolution of topics at a turn-level resolution. This combined framework
enhances the understanding of therapeutic interactions, enabling timely
feedback for therapists regarding conversation quality and providing
interpretable insights to improve the effectiveness of psychotherapy.
Related papers
- LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains.
The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
arXiv Detail & Related papers (2024-06-09T09:03:11Z) - Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts [4.403408362362806]
We introduce the Chain-of-Interaction prompting method to contextualize large language models for psychiatric decision support by the dyadic interactions.
This approach enables large language models to leverage the coding scheme, patient state, and domain knowledge for patient behavioral coding.
arXiv Detail & Related papers (2024-03-20T17:47:49Z) - Safe and Interpretable Estimation of Optimal Treatment Regimes [54.257304443780434]
We operationalize a safe and interpretable framework to identify optimal treatment regimes.
Our findings support personalized treatment strategies based on a patient's medical history and pharmacological features.
arXiv Detail & Related papers (2023-10-23T19:59:10Z) - TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling
and AI-Generated Arts [24.740247834989248]
We present the TherapyView, a demonstration system to help therapists visualize the dynamic contents of past treatment sessions.
The system incorporates temporal modeling to provide a time-series representation of topic similarities at a turn-level resolution and AI-generated artworks.
This system provides a proof of concept of AI-augmented therapy tools with e in-depth understanding of the patient's mental state and enabling more effective treatment.
arXiv Detail & Related papers (2023-02-21T17:53:45Z) - GDPR Compliant Collection of Therapist-Patient-Dialogues [48.091760741427656]
We elaborate on the challenges we faced in starting our collection of therapist-patient dialogues in a psychiatry clinic under the General Data Privacy Regulation of the European Union.
We give an overview of each step in our procedure and point out the potential pitfalls to motivate further research in this field.
arXiv Detail & Related papers (2022-11-22T15:51:10Z) - Working Alliance Transformer for Psychotherapy Dialogue Classification [27.80555922579736]
We believe that the working alliance can be better characterized using natural language processing technique directly in the dialogue transcribed in each therapy session.
We propose a Transformer-based classification model that has a psychological state encoder which infers the working alliance scores.
We evaluate our method in a real-world dataset with over 950 therapy sessions with anxiety, depression, schizophrenia and suicidal patients.
arXiv Detail & Related papers (2022-10-27T16:43:05Z) - Automated Fidelity Assessment for Strategy Training in Inpatient
Rehabilitation using Natural Language Processing [53.096237570992294]
Strategy training is a rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke.
Standardized fidelity assessment is used to measure adherence to treatment principles.
We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task.
arXiv Detail & Related papers (2022-09-14T15:33:30Z) - Deep Annotation of Therapeutic Working Alliance in Psychotherapy [27.80555922579736]
The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment.
In this work, we propose an analytical framework of directly inferring the therapeutic working alliance from the natural language within the psychotherapy sessions.
arXiv Detail & Related papers (2022-04-12T04:42:51Z) - Automated Quality Assessment of Cognitive Behavioral Therapy Sessions
Through Highly Contextualized Language Representations [34.670548892766625]
A BERT-based model is proposed for automatic behavioral scoring of a specific type of psychotherapy, called Cognitive Behavioral Therapy (CBT)
The model is trained in a multi-task manner in order to achieve higher interpretability.
BERT-based representations are further augmented with available therapy metadata, providing relevant non-linguistic context and leading to consistent performance improvements.
arXiv Detail & Related papers (2021-02-23T09:22:29Z) - MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos.
We release a new dataset, MEDICA, for mental health patient engagement detection.
arXiv Detail & Related papers (2020-11-17T15:18:38Z) - Pose-based Body Language Recognition for Emotion and Psychiatric Symptom
Interpretation [75.3147962600095]
We propose an automated framework for body language based emotion recognition starting from regular RGB videos.
In collaboration with psychologists, we extend the framework for psychiatric symptom prediction.
Because a specific application domain of the proposed framework may only supply a limited amount of data, the framework is designed to work on a small training set.
arXiv Detail & Related papers (2020-10-30T18:45:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.