Timely reporting and analysis of adverse events and medical errors is critical to driving forward programs in patient-safety, however, due to the large numbers of event reports accumulating daily in health institutions, manually finding and labeling certain types of errors or events is becoming increasingly challenging. We propose to automatically classify/label event reports via semi-supervised learning which utilizes labeled as well as unlabeled event reports to complete the classification task. We focused on classifying two types of event reports: patient mismatches and weight errors. We downloaded 9405 reports from the Connecticut Children's Medical Center reporting system. We generated two samples of labeled and unlabeled reports containing 3155 and 255 for the patient mismatch and the weight error use cases respectively. We developed feature based Laplacian Support Vector machine (FS-LapSVM), a hybrid framework that combines feature selection with Laplacian Support Vector machine classifier (LapSVM). Superior performance of FS-LapSVM in finding patient weight error reports compared to LapSVM. Also, FS-LapSVM classifier outperformed standard LapSVM in classifying patient mismatch reports across all metrics.
['Samah Jamal Fodeh', 'Andrea L. Benin', 'Perry L. Miller', 'Kyle Lee', 'Michele Koss', 'Cynthia Brandt']
Laplacian SVM Based Feature Selection Improves Medical Event Reports Classification