Using LLMs to Analyze Antecedent, Behavior and Consequence Narrative Recordings in Behavioral Health Science

The Antecedent, Behavior, and Consequence narrative recordings is a recognized approach for data collection and analysis in conducting a Functional Behavior Assessment. Currently, this method relies on a manual process that necessitates detailed descriptive analyses of the behavior in question, which must be performed by certified professionals, such as Board Certified Behavior Analysts (BCBAs). Only 4.7% of Board Certified Behavior Analysts (BCBAs) reside outside US and Canada making it difficult to conduct a Functional Behavior Assessment outside these two countries. Within the US, systemic challenges, including insufficient funding within public education and operational inefficiencies, have contributed to significant delays in executing Functional Behavior Assessments with wait times often extending for several months. This paper proposes the utilization of Chain of Thought prompting with Large Language Models to analyze data derived from Antecedent, Behavior, and Consequence narrative recordings. Through our proposed approach, we achieve an 78% accuracy in identifying antecedent labels and 70% accuracy for consequence labels from narrative recordings, demonstrating that this method produces scalable and accurate results. To ensure the generalizability of our approach, especially in scenarios with limited data, we employed data augmentation techniques and Leave One Out Cross Validation (LOOCV). Our model significantly outperformed traditional machine learning algorithms like Support Vector Machines and Random Forest, demonstrating its robustness and potential for real-world applications. By reducing operational inefficiencies, this approach can significantly improve the accessibility of behavioral healthcare for traditionally marginalized populations such as low-income, rural, minority, and other disadvantaged communities across the world. Furthermore, we aim to position this paper as a catalyst for advancing the use of Large Language Models and Natural Language Processing in the field of behavioral health, paving the way for further research and development.