NLP-ROPCare: predicting retinopathy of prematurity with admission notes using natural language processing
The NLP-ROPCare combines language models NEZHA and RoBERTa to enable early prediction of ROP occurrence and severity based on unstructured free-form texts in the admission notes of preterm infants, highlighting its value in early prevention of ROP. Further external validation should be carried out to better adjust the model.
Authors
- Chuan Hui Nie
- Xuelin Huang (ORCID: https://orcid.org/0000-0003-1192-9336)
- Yulin Zhang (ORCID: https://orcid.org/0000-0002-4234-2397)
- Jie Sun
- XIANQIONG LUO
- Guoming Zhang
- Jianbing Ren
- Xinyu Zhao
- Shuai Zhao
- Jinming Wen
- Yuwen Li
- Suzhen Xie
Institutions
- Nanyang Technological University (SG)
- Jilin University (CN)
- Huizhou Central People's Hospital (CN)
- Southern Medical University Shenzhen Hospital (CN)
- Guangdong Province Women and Children Hospital (CN)
- Northeastern University (CN)
Publication Details
- Journal
- BMJ Open Ophthalmology
- Published
- 2026-01-16
- DOI
- https://doi.org/10.1136/bmjophth-2025-002385
- Primary Topic
- Retinopathy of Prematurity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Sanming Project of Medicine in Shenzhen
- Basic and Applied Basic Research Foundation of Guangdong Province