From Lived Experience to Digital Biomarkers: Methods for Scaling Narrative Data with Large Language Models in Mental Health

Abstract

Lived experience narratives provide a rich account of how individuals interpret and organize their mental health, capturing dimensions of meaning and context that are often missed by structured assessments and traditional language-based features. Despite their importance, the unstructured nature of these data has limited their systematic use. Recent advances in large language models (LLMs) enable scalable analysis of narrative data, allowing for the extraction of thematic and structural features across large datasets. These approaches position lived experience as a promising digital biomarker, with the potential to capture early, ecologically valid signals and complement existing clinical measures. However, challenges related to validation, interpretability, bias, and data governance remain. We outline emerging methodological frameworks and discuss how LLMbased approaches can support more scalable, longitudinal, and person-centered models of mental health.