The development of a novel natural language processing tool to identify pediatric chest radiograph reports with pneumonia.

ABSTRACT:

Objective

Chest radiographs are frequently used to diagnose community-acquired pneumonia (CAP) for children in the acute care setting. Natural language processing (NLP)-based tools may be incorporated into the electronic health record and combined with other clinical data to develop meaningful clinical decision support tools for this common pediatric infection. We sought to develop and internally validate NLP algorithms to identify pediatric chest radiograph (CXR) reports with pneumonia.

Materials and methods

We performed a retrospective study of encounters for patients from six pediatric hospitals over a 3-year period. We utilized six NLP techniques: word embedding, support vector machines, extreme gradient boosting (XGBoost), light gradient boosting machines Naïve Bayes an

SUBMITTER: Rixe N 

PROVIDER: S-EPMC9992200 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature