Natural language processing-driven state machines to extract social factors from unstructured clinical documentation.

ABSTRACT:

Objective

This study sought to create natural language processing algorithms to extract the presence of social factors from clinical text in 3 areas: (1) housing, (2) financial, and (3) unemployment. For generalizability, finalized models were validated on data from a separate health system for generalizability.

Materials and methods

Notes from 2 healthcare systems, representing a variety of note types, were utilized. To train models, the study utilized n-grams to identify keywords and implemented natural language processing (NLP) state machines across all note types. Manual review was conducted to determine performance. Sampling was based on a set percentage of notes, based on the prevalence of social need. Models were optimized over multiple training and evaluation cycles.

SUBMITTER: Allen KS 

PROVIDER: S-EPMC10112959 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature