Adaptive Contextual Embeddings for Detecting Social Determinants of Health in Patient Narratives

Abstract

This paper investigates an advanced methodological framework for extracting Social Determinants of Health from patient narratives by leveraging adaptive contextual embeddings. Building upon contemporary natural language processing approaches, it aims to illuminate the mechanisms by which domain-specific context enhances feature representations in neural architectures. The central premise is that embedding spaces can be dynamically aligned with linguistic variability present in clinical text, thereby facilitating robust detection of factors such as socioeconomic status, housing stability, and access to care. Rather than relying on rigid static word vectors, the proposed approach adapts embedding spaces to capture latent relationships within patient descriptions, transcending shallow lexical correlations. The work further explores how auxiliary signals, derived from the semantic composition of clinically relevant terms, can refine the learned representations through iterative alignment techniques. In doing so, it addresses the challenges inherent in modeling subtle language patterns that encode sensitive social characteristics. By integrating advanced linear algebraic formulations and deductive logic statements into the core modeling process, the framework aspires to provide a new layer of interpretability and rigor. This paper will elaborate on the theoretical foundations, architecture, and empirical evaluations that substantiate the effectiveness of the proposed system, offering a blueprint for future innovations in adaptive embeddings for health information extraction and SDOH-driven predictive analytics.