SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning
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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 using an online registration-free diarization method. The dialogue pairs along with their computed ratings are then fed into a deep reinforcement learning recommender 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 existing datasets, we demonstrate the effectiveness of this system in a web app.
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