ArticleInternational journal of medical informatics2021
Development and evaluation of novel ophthalmology domain-specific neural word embeddings to predict visual prognosis.
Article in International journal of medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 22 citations in OpenAlex.
- Improving Retrieval Augmented Generation for Health Care by Fine-Tuning Clinical Embedding Models: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Identifying Transportation Needs in Ophthalmology Clinic Notes Using Natural Language Processing: Retrospective, Cross-Sectional Study.JMIR medical informatics · 2025Article
- Visual acuity prediction on real-life patient data using a machine learning based multistage system.Scientific reports · 2024Article
- Impact of word embedding models on text analytics in deep learning environment: a review.Artificial intelligence review · 2023Article
- Predicting near-term glaucoma progression: An artificial intelligence approach using clinical free-text notes and data from electronic health records.Frontiers in medicine · 2023Article
- Deep Learning Approaches for Predicting Glaucoma Progression Using Electronic Health Records and Natural Language Processing.Ophthalmology science · 2022Article
- Predicting Glaucoma Progression Requiring Surgery Using Clinical Free-Text Notes and Transfer Learning With Transformers.Translational vision science & technology · 2022Article
- Looking for low vision: Predicting visual prognosis by fusing structured and free-text data from electronic health records.International journal of medical informatics · 2022Article
- Applications of natural language processing in ophthalmology: present and future.Frontiers in medicine · 2022Review
- Use of artificial intelligence in forecasting glaucoma progression.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo develop and evaluate novel word embeddings (WEs) specific to ophthalmology, using text corpora from published literature and electronic health records (EHR). MATERIALS AND
methodsWe trained ophthalmology-specific WEs using 121,740 PubMed abstracts and 89,282 EHR notes using word2vec continuous bag-of-words architecture. PubMed and EHR WEs were compared to general domain GloVe WEs and general biomedical domain BioWordVec embeddings using a novel ophthalmology-domain-specific 200-question analogy test and prediction of prognosis in 5547 low vision patients using EHR notes as inputs to a deep learning model.
resultsWe found that many words representing important ophthalmic concepts in the EHR were missing from the general domain GloVe vocabulary, but covered in the ophthalmology abstract corpus. On ophthalmology analogy testing, PubMed WEs scored 95.0 %, outperforming EHR (86.0 %) and GloVe (91.0 %) but less than BioWordVec (99.5 %). On predicting low vision prognosis, PubMed and EHR WEs resulted in similar AUROC (0.830; 0.826), outperforming GloVe (0.778) and BioWordVec (0.784).
conclusionWe found that using ophthalmology domain-specific WEs improved performance in ophthalmology-related clinical prediction compared to general WEs. Deep learning models using clinical notes as inputs can predict the prognosis of visually impaired patients. This work provides a framework to improve predictive models using domain-specific WEs.
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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.