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Cognitive Behavioral Therapy (CBT) for tinnitus alleviates psychological discomfort caused by severe tinnitus symptoms. During CBT, the patients will have various homework assignments, including writing daily diaries and self-monitoring. Most of these homework assignments are hand-written, textual data. This paper proposes that tinnitus therapeutics can utilize Large Language Models (LLMs) to analyze CBT and predict the outcomes of CBT treatments to manage high caseloads. We anonymized patient data and examined it with GPT-2-based-embedding, dimensionality reduction, and clustering process to observe how patients themselves changed their misconceptions and developed less unnecessary excessive emotional discomfort and how their Tinnitus Handicap Inventory (THI) scores were improved after the CBT treatment. We also discussed clustering results as a part of the demonstrations that LLMs can give us insights into the CBT. Then, we augmented textual patient data in three ways to minimize augmentation bias with a corresponding penalty to overcome the constraints of limitation of the number of datasets. We trained the Google T5 Transformer with the augmented data to predict the THI score outcomes at the end of the CBT sessions. We measured the performance using the ROUGE-L metric during the training and validation. The generated THI scores by Google T5 were converted from strings to floats to measure RMSE performance, which proved that the LLM could predict the outcome of CBT treatment with CBT data. Even though there is a risk of overfitting issues, this work demonstrated that tinnitus therapeutics experts can employ LLMs to manage caseloads.

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Advancing Tinnitus Therapeutics: GPT-2 Driven Clustering Analysis of Cognitive Behavioral Therapy Sessions and Google T5-Based Predictive Modeling for THI Score Assessment