Exploring prevalence of wound infections and related patient characteristics in homecare using natural language processing.

ABSTRACT: We aimed to create and validate a natural language processing algorithm to extract wound infection-related information from nursing notes. We also estimated wound infection prevalence in homecare settings and described related patient characteristics. In this retrospective cohort study, a natural language processing algorithm was developed and validated against a gold standard testing set. Cases with wound infection were identified using the algorithm and linked to Outcome and Assessment Information Set data to identify related patient characteristics. The final version of the natural language processing vocabulary contained 3914 terms and expressions related to the presence of wound infection. The natural language processing algorithm achieved overall good performance (F-measure = 0.88).

SUBMITTER: Woo K 

PROVIDER: S-EPMC8684883 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature