A machine learning approach to identify predictors of adverse events in posterior lamellar keratoplasty: a nationwide registry study

Keyphrases

• Nationwide Registry 100%
• Machine Learning Approach 100%
• Graft Failure 100%
• Posterior Lamellar Keratoplasty 100%
• Predictors of Adverse Outcomes 100%
• Predictor Variables 60%
• Practice Patterns 60%
• Graft Detachment 60%
• Rebubbling 60%
• Least Absolute Shrinkage and Selection Operator (LASSO) 40%
• Classification Tree Analysis 40%
• Dutch 20%
• Explained Variance 20%
• Ultrathin 20%
• Prediction Model 20%
• Classification Tree 20%
• Clinical Course 20%
• Adverse Events 20%
• Postoperative Complications 20%
• Specific Practices 20%
• Subsequent Effects 20%
• Machine Learning Analysis 20%
• Model Tree 20%
• Practice Variability 20%
• Unsupervised Machine Learning 20%
• Corneal Transplantation 20%
• Transplant Registry 20%
• Donor Graft 20%
• Protective Variables 20%
• Operator Model 20%
• Random Undersampling 20%
• Patient Practice 20%
• Clinical Outcomes 20%

Medicine and Dentistry

• Adverse Event 100%
• Graft Failure 100%
• Lamellar Keratoplasty 100%
• Postoperative Complication 20%
• Disease Course 20%
• Effect Size 20%
• Transplantation 20%
• Corneal Transplantation 20%