https://www.selleckchem.com/pr....oducts/santacruzamat
Patient "no-shows" are missed appointments resulting in clinical inefficiencies, revenue loss, and discontinuity of care. Using secondary electronic health record (EHR) data, we used machine learning to predict patient no-shows in follow-up and new patient visits in pediatric ophthalmology and to evaluate features for importance. The best model, XGBoost, had an area under the receiver operating characteristics curve (AUC) score of 0.90 for predicting no-shows in follow-up visits. The key findings from this study are (1
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