Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers.

ABSTRACT: The prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (INEADS) remains unknown because such data are not routinely captured in structured electronic health records. This study sought to develop and validate a natural language processing (NLP) algorithm to identify information related to being INEADS from clinical notes. We used a publicly available dataset of critical care patients from 2001 through 2012 at a United States academic medical center, which contained 418,393 relevant clinical notes for 23,904 adult admissions. We developed 17 subcategories indicating reduced or elevated potential for being INEADS, and created a vocabulary of terms and expressions within each. We used an NLP application to create a language model and expand these voca

SUBMITTER: Song J 

PROVIDER: S-EPMC9273092 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature