Explainable Multi-Label Classification Framework for Behavioral Health Based on Domain Concepts

Behavioral health, which covers mental health, lifestyle choices, addictions, and crises, poses serious issues in the community. Thus, appropriately analyzing and classifying behavioral health data is crucial for making informed health-care decisions. Traditional deep learning and natural language processing approaches struggle to effectively identify behavioral health issues because the data is unstructured, complex, and lacks sufficient context. Furthermore, subject matter experts must be consulted to ensure effective identification. In this work, we proposed a deep learning-based framework consisting of several modules: A) domain concept encoder converts the keywords and their evidence types to vectors, which were predefined by a subject matter expert; B) the semantic representation encoder (SRE) is trained on the vectors to learn the relationship between them; C) transformed-based feature learner is an advanced learner that extracts feature embeddings from documents and generates attention weights since it has more context given the incorporated relationship weights; D) The behavioral health multilabel classifier utilizes feature embeddings to classify a document into one or more behavioral health classes; and E) The LLM-enabled explainer provides explanations based on attention weights and classifications. Our proposed framework outperformed state-of-the-art models in multilabel behavioral health case classification while also providing explanations for each classification. Which is crucial in behavioral health analysis.