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.

采用新的框架 COMPASS，直接从心理疗法会话中使用的自然语言推断治疗性工作联盟，利用大型语言模型分析会话文本与工作联盟清单中的语句进行比较，以及神经主题建模技术和生成式语言提示相结合的各种方法，增强了对治疗交互作用的理解，为治疗师提供及时反馈，并提供可解释的见解以提高心理疗法的有效性。

COMPASS: 使用语言建模进行患者 - 治疗师联盟策略的计算化映射

COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies  with Language Modeling

We propose a recommendation system that suggests treatment strategies to a
therapist during the psychotherapy session in real-time. Our system uses a
turn-level rating mechanism that predicts the therapeutic outcome by computing
a similarity score between the deep embedding of a scoring inventory, and the
current sentence that the patient is speaking. The system automatically
transcribes a continuous audio stream and separates it into turns of the
patient and of the therapist and perform real-time inference of their
therapeutic working alliance. The dialogue pairs along with their computed
working alliance as ratings are then fed into a deep reinforcement learning
recommendation system where the sessions are treated as users and the topics
are treated as items. Other than evaluating the empirical advantages of the
core components on an existing dataset of psychotherapy sessions, we
demonstrate the effectiveness of this system in a web app.

文章提出一个实时的推荐系统，用于心理治疗期间向治疗师建议治疗策略。系统通过计算评分清单深度嵌入的相似性得分与患者当前言语之间的相似性，预测治疗结果。系统自动将连续音频流转录并分成患者和治疗师的对话，实时推断他们的治疗工作联盟。对话及其计算的工作联盟得分作为评级被馈送到深度强化学习推荐系统中，其中会话被视为用户，主题被视为项。除了在现有心理治疗数据集上评估核心组件的实证优势外，我们还在一个 Web 应用中展示了该系统的有效性。

使用深度强化学习进行自然语言处理注释的心理治疗治疗策略实时推荐

SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning

The therapeutic working alliance is an important predictor of the outcome of
the psychotherapy treatment. In practice, the working alliance is estimated
from a set of scoring questionnaires in an inventory that both the patient and
the therapists fill out. In this work, we propose an analytical framework of
directly inferring the therapeutic working alliance from the natural language
within the psychotherapy sessions in a turn-level resolution with deep
embeddings such as the Doc2Vec and SentenceBERT models. The transcript of each
psychotherapy session can be transcribed and generated in real-time from the
session speech recordings, and these embedded dialogues are compared with the
distributed representations of the statements in the working alliance
inventory. We demonstrate, in a real-world dataset with over 950 sessions of
psychotherapy treatments in anxiety, depression, schizophrenia and suicidal
patients, the effectiveness of this method in mapping out trajectories of
patient-therapist alignment and the interpretability that can offer insights in
clinical psychiatry. We believe such a framework can be provide timely feedback
to the therapist regarding the quality of the conversation in interview
sessions.

这篇论文提出了一种从心理治疗会话中直接推断治疗工作联盟的分析框架，并使用自然语言处理模型如 Doc2Vec 和 SentenceBERT 模型以逐句的方式嵌入文本来实现。作者证明了这种方法的有效性和解释性，并提出这种框架可以向治疗师提供有关问诊会话质量的及时反馈。