NLP-ROPCare: predicting retinopathy of prematurity with admission notes using natural language processing.

ABSTRACT:

Objectives

Retinopathy of prematurity (ROP) is a leading cause of blindness in children worldwide, requiring more efficient models to help predict treatment-requiring ROP. Our study aimed to develop a new prediction model for ROP occurrence and severity, named NLP-ROPCare, using natural language processing (NLP).

Methods and analysis

A retrospective observational study. Infants with a gestational age ≤32 weeks or birth weight ≤2000 g were collected in Guangdong Women and Children Hospital from 2013 to 2022, including 3922 preterm infants with 1106 patients with ROP. Four pretrained language models - BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT pretraining Approach), MC-BERT (language pre-training via a Meta Controller) and

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12815143 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature