Assessing the utility of deep neural networks in detecting superficial surgical site infections from free text electronic health record data.

ABSTRACT:

Background

High-quality outcomes data is crucial for continued surgical quality improvement. Outcomes are generally captured through structured administrative data or through manual curation of unstructured electronic health record (EHR) data. The aim of this study was to apply natural language processing (NLP) to chart notes in the EHR to accurately capture postoperative superficial surgical site infections (SSSIs).

Methods

Deep Learning (DL) NLP models were trained on data from 389,865 surgical cases across all 11 hospitals in the Capital Region of Denmark. Surgical cases in the training dataset were performed between January 01st, 2017, and October 30th, 2021. We trained a forward reading and a backward reading universal language model on unlabeled postoperative chart not

SUBMITTER: Bonde A 

PROVIDER: S-EPMC10801170 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature