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

TL;DR

SupervisorBot is a real-time NLP-based recommendation system that aids therapists by suggesting treatment strategies during psychotherapy sessions, leveraging deep reinforcement learning and turn-level outcome predictions.

Contribution

The paper introduces a novel real-time recommendation system for psychotherapy that combines NLP, deep reinforcement learning, and turn-level outcome prediction.

Findings

System effectively predicts therapeutic outcomes in real-time.

Demonstrates improved session management via reinforcement learning.

Validated on an existing psychotherapy dataset.

Abstract

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.