Predicting 24-hour intraocular pressure peaks and averages with machine learning
The study developed machine learning models that predict 24-hour peak and average IOP. Specific time point combinations and the RFR algorithm were identified, which improved the accuracy of predicting 24-hour peak and average intraocular pressure. These findings provide the potential for more effective management and treatment strategies for glaucoma patients.
Authors
- Yiping Jin (ORCID: https://orcid.org/0000-0001-6620-4274)
- Hong Li (ORCID: https://orcid.org/0000-0001-6930-4772)
- Xue Wang (ORCID: https://orcid.org/0000-0002-7436-8233)
- Danping Wu
- Haohao Zhu (ORCID: https://orcid.org/0000-0003-1848-2354)
- Ranran Chen (ORCID: https://orcid.org/0000-0003-3216-2373)
- Yanlong Bi (ORCID: https://orcid.org/0000-0001-8214-4355)
- Xiao-Mei Li
- Jinming Lei
- Yujie Liao
Institutions
- Tongji University (CN)
- Fudan University (CN)
- Shanghai East Hospital (CN)
- Tongji Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2024-10-07
- DOI
- https://doi.org/10.3389/fmed.2024.1459629
- Citations
- 3
- Primary Topic
- Glaucoma and retinal disorders
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- Fudan University