SBDH-Reader: an LLM-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 an LLM-powered method to extract structured SBDH data from clinical notes through prompt engineering.

Materials and methods

We developed SBDH-Reader to extract six 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 7,225 notes from 6,382 patients in the MIMI

SUBMITTER: Gu Z 

PROVIDER: S-EPMC11875322 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature