ArticleAmerican journal of ophthalmology2021
Predictive Analytics for Glaucoma Using Data From the All of Us Research Program.
Article in American journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed, 50 citations in OpenAlex.
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- Multi-ancestry genome-wide association study in all of Us for primary open-angle glaucoma.Scientific reports · 2026Article
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- Multi-Ancestry Genome-Wide Association Study in All of Us for Primary Open- Angle Glaucoma.Research square · 2025Article
- High-Density Lipoproteins Associated with Age-Related Macular Degeneration in the All of Us Research Program.Ophthalmology · 2025Article
- ENABLING A HEALTHIER FUTURE FOR ALL THROUGH PRECISION MEDICINE.Transactions of the American Clinical and Climatological Association · 2025Article
- Racial and Ethnic Disparities in Antihypertensive Medication Prescribing Patterns and Effectiveness.Clinical pharmacology and therapeutics · 2024Article
- Construction of diagnostic models with machine-learning algorithms for colorectal cancer based on clinical laboratory parameters.Journal of gastrointestinal oncology · 2024Article
- Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence.Life (Basel, Switzerland) · 2024Review
- The All of Us Research Program is an opportunity to enhance the diversity of US biomedical research.Nature medicine · 2024Article
- Deep learning-based identification of eyes at risk for glaucoma surgery.Scientific reports · 2024Article
- Predicting Glaucoma Progression to Surgery with Artificial Intelligence Survival Models.Ophthalmology science · 2023Article
- Availability of Physical Activity Tracking Data from Wearable Devices for Glaucoma Patients.Information (Basel) · 2023Article
- TheAnnual review of biomedical data science · 2023Review
- Managing re-identification risks while providing access to the All of Us research program.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Role of artificial intelligence in determining factors impacting patients' refractive surgery decisions.Indian journal of ophthalmology · 2023Article
- The future is precision medicine-guided diagnoses, preventions and treatments for neurodegenerative diseases.Frontiers in aging neuroscience · 2023Article
- Family and personal history of cancer in the All of Us research program for precision medicine.PloS one · 2023Article
- Assessing Usability of Smartwatch Digital Health Devices for Home Blood Pressure Monitoring among Glaucoma Patients.Informatics (MDPI) · 2022Article
- Development of an Open-Source Annotated Glaucoma Medication Dataset From Clinical Notes in the Electronic Health Record.Translational vision science & technology · 2022Article
Corrections and comments
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Authors and funding
19 authors at 10 institutions in 2 countries.
Funding
Abstract
purposeTo (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research.
designDevelopment and evaluation of machine learning models.
methodsElectronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall.
resultsThe mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests).
conclusionsModels trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research.
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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.