Predicting postoperative visual acuity in epiretinal membrane patients and visualization of the contribution of explanatory variables in a machine learning model.
Akiko Irie-OtaYoshitsugu MatsuiKoki ImaiYoko MaseKeiichiro KonnoTaku SasakiShinichiro ChujoHisashi MatsubaraHiroharu KawanakaMineo KondoPublished in: PloS one (2024)
Predicting the postoperative visual acuity in ERM patients is possible using the preoperative clinical data and OCT images with LightGBM. The contribution of the explanatory variables can be visualized using the SHAP values, and the accuracy of the prediction models improved when the postoperative visual acuity is included as an explanatory variable. Our data-driven machine learning models reveal that preoperative visual acuity and the size of the EIFL significantly influence postoperative visual acuity. Early intervention may be crucial for achieving favorable visual outcomes in eyes with an ERM.
Keyphrases
- patients undergoing
- machine learning
- end stage renal disease
- ejection fraction
- chronic kidney disease
- newly diagnosed
- optical coherence tomography
- peritoneal dialysis
- randomized controlled trial
- prognostic factors
- deep learning
- big data
- artificial intelligence
- gene expression
- type diabetes
- electronic health record
- diabetic retinopathy
- single cell
- insulin resistance