Natural language processing for the automated detection of intra-operative elements in lumbar spine surgery.

ABSTRACT:

Background

The aim of this study was to develop natural language processing (NLP) algorithms to conduct automated identification of incidental durotomy, wound drains, and the use of sutures or skin clips for wound closure, in free text operative notes of patients following lumbar surgery.

Methods

A single-centre retrospective case series analysis was conducted between January 2015 and June 2022, analysing operative notes of patients aged >18 years who underwent a primary lumbar discectomy and/or decompression at any lumbar level. Extreme gradient-boosting NLP algorithms were developed and assessed on five performance metrics: accuracy, area under receiver-operating curve (AUC), positive predictive value (PPV), specificity, and Brier score.

Results

A total of 942 patie

SUBMITTER: Biswas S 

PROVIDER: S-EPMC10757971 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature