Assessing the utility of natural language processing for detecting postoperative complications from free medical text.

ABSTRACT:

Background

Postoperative complication rates are often assessed through administrative data, although this method has proven to be imprecise. Recently, new developments in natural language processing have shown promise in detecting specific phenotypes from free medical text. Using the clinical challenge of extracting four specific and frequently undercoded postoperative complications (pneumonia, urinary tract infection, sepsis, and septic shock), it was hypothesized that natural language processing would capture postoperative complications on a par with human-level curation from electronic health record free medical text.

Methods

Electronic health record data were extracted for surgical cases (across 11 surgical sub-specialties) from 18 hospitals in the Capital and Zealand re

SUBMITTER: Dencker EE 

PROVIDER: S-EPMC11003538 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature