SBDH-Reader: a large language model-powered method for extracting social and behavioral determinants of health from clinical notes.

ABSTRACT:

Objective

Social and behavioral determinants of health (SBDH) are increasingly recognized as essential for prognostication and informing targeted interventions. Clinical notes often contain details about SBDH in unstructured format. Conventional extraction methods for these data tend to be labor intensive, inaccurate, and/or unscalable. In this study, we aim to develop and validate a large language model (LLM)-powered method to extract structured SBDH data from clinical notes through prompt engineering.

Materials and methods

We developed SBDH-Reader to extract 6 categories of granular SBDH data by prompting GPT-4o, including employment, housing, marital status, and substance use including alcohol, tobacco, and drug use. SBDH-Reader was developed using 7225 notes from 6382 pa

SUBMITTER: Gu Z 

PROVIDER: S-EPMC12451939 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature