Machine Learning Models for Predicting 24-Hour Intraocular Pressure Changes: A Comparative Study
Chen Ranran, Lei Jinming, Liao Yujie, Jin Yiping, Wang Xue, Li Hong, Bi Yanlong, Zhu Haohao · 2024
Read Machine Learning Models for Predicting 24-Hour Intraocular Pressure Changes: A Comparative Study by Chen Ranran, Lei Jinming, Liao Yujie, Jin Yiping, Wang Xue, Li Hong, Bi Yanlong, Zhu Haohao (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11624606.txt.
Description
Chen Ranran and co-authors present a 2024 study on forecasting a full day of eye pressure swings from daytime readings alone. Pressure inside the eye rises and falls over 24 hours, and big swings worsen glaucoma. Round-the-clock monitoring is cumbersome, so the team trained machine learning models to infer the swing class from routine measures.
Records from monitored patients supplied 22 features. Statistical screens sorted them into four subsets of rising strictness. Five classification algorithms were trained and compared on accuracy, sensitivity, and curve scores. The winning model was unpacked with an explanation method that ranks each feature's push on the prediction. A reliable forecast could spare patients overnight stays in pressure labs. It could also flag hidden swingers for closer care.
Catalog record
| CREATOR | Chen Ranran, Lei Jinming, Liao Yujie, Jin Yiping, Wang Xue, Li Hong, Bi Yanlong, Zhu Haohao |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |