ArticleTranslational vision science & technology2022
Predicting Glaucoma Progression Requiring Surgery Using Clinical Free-Text Notes and Transfer Learning With Transformers.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 40 citations in OpenAlex.
- Benchmarking GPT-5, LLaMA, and Mistral for Clinical Named Entity Recognition in Ophthalmology Progress Notes.Translational vision science & technology · 2026Article
- Improving Fairness and Mitigating Bias in Multicenter Electronic Health Records Models to Predict Glaucoma Outcomes.Ophthalmology science · 2026Article
- Applications of Large Language Models in Glaucoma: A Scoping Review.Vision (Basel, Switzerland) · 2026Review
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- Leveraging Large Language Models with Sequential Prompting to Extract Eye Examination Findings from Free-Text Ophthalmology Notes.Ophthalmology science · 2026Article
- Interpretable longitudinal glaucoma visual field estimation deep learning system from fundus images and clinical narratives.NPJ digital medicine · 2025Article
- OphthaBERT: Automated Glaucoma Diagnosis from Clinical Notes.medRxiv : the preprint server for health sciences · 2025Article
- Deep Learning-Based Glaucoma Detection Using Clinical Notes: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models.Diagnostics (Basel, Switzerland) · 2025Article
- Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans.Translational vision science & technology · 2025Article
- A scoping review of advancements in machine learning for glaucoma: current trends and future direction.Frontiers in medicine · 2025Article
- Large language models in the management of chronic ocular diseases: a scoping review.Frontiers in cell and developmental biology · 2025Review
- Multicenter evaluation of machine and deep learning methods to predict glaucoma surgical outcomes.Frontiers in artificial intelligence · 2025Article
- Utilizing Large Language Models in Ophthalmology: The Current Landscape and Challenges.Ophthalmology and therapy · 2024Review
- Predicting Glaucoma Surgical Outcomes Using Neural Networks and Machine Learning on Electronic Health Records.Translational vision science & technology · 2024Article
- Extracting patient lifestyle characteristics from Dutch clinical text with BERT models.BMC medical informatics and decision making · 2024Article
- Visual acuity prediction on real-life patient data using a machine learning based multistage system.Scientific reports · 2024Article
- Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection.Bioengineering (Basel, Switzerland) · 2024Review
- Applications of natural language processing tools in the surgical journey.Frontiers in surgery · 2024Review
- New era after ChatGPT in ophthalmology: advances from data-based decision support to patient-centered generative artificial intelligence.Annals of translational medicine · 2023Article
- Exploring large language model for next generation of artificial intelligence in ophthalmology.Frontiers in medicine · 2023Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Purpose: We evaluated the use of massive transformer-based language models to predict glaucoma progression requiring surgery using ophthalmology clinical notes from electronic health records (EHRs). Methods: Ophthalmology clinical notes for 4512 glaucoma patients at a single center from 2008 to 2020 were identified from the EHRs. Four different pre-trained Bidirectional Encoder Representations from Transformers (BERT)-based models were fine-tuned on ophthalmology clinical notes from the patients' first 120 days of follow-up for the task of predicting which patients would require glaucoma surgery. Models were evaluated with standard metrics, including area under the receiver operating characteristic curve (AUROC) and F1 score. Results: Of the patients, 748 progressed to require glaucoma surgery (16.6%). The original BERT model had the highest AUROC (73.4%; F1 = 45.0%) for identifying these patients, followed by RoBERTa, with an AUROC of 72.4% (F1 = 44.7%); DistilBERT, with an AUROC of 70.2% (F1 = 42.5%); and BioBERT, with an AUROC of 70.1% (F1 = 41.7%). All models had higher F1 scores than an ophthalmologist's review of clinical notes (F1 = 29.9%). Conclusions: Using transfer learning with massively pre-trained BERT-based models is a natural language processing approach that can access the wealth of clinical information stored within ophthalmology clinical notes to predict the progression of glaucoma. Future work to improve model performance can focus on integrating structured or imaging data or further tailoring the BERT models to ophthalmology domain-specific text. Translational Relevance: Predictive models can provide the basis for clinical decision support tools to aid clinicians in identifying high- or low-risk patients to maximally tailor glaucoma treatments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.