Clinical Safety Incident Taxonomy Performance on C4.5 Decision Tree and Random Forest

The paper applies an artificial intelligence centered method to classify 12 clinical safety incident (CSI) classes. The paper aims to establish a taxonomy that classifies the CSI reports into their correct classes automatically and with high accuracy. The study investigates feasibility of applying the C4.5 decision tree (DT) classifier and the random forest (RF) classifier for this purpose. The classifiers were trained using randomly selected 3600 CSIs from an Incident Information Management System (IIMS) used by seven hospitals. The taxonomies investigated were the Generic Reference Model (GRM) and the World Health Organization (WHO) patient safety classification. The classifiers trained 13 GRM CSI classes and 9 WHO CSI classes using a bag-of-words approach. The overall taxonomies performance on the RF classifier was better than on the DT classifier. The performance achieved by the classifier applying the WHO taxonomy was better than the GRM taxonomy. Four of the five poorly performing classes in the GRM taxonomy significantly improved their performance on changing the taxonomy. To improve the WHO taxonomy performance the improved WHO (WHO-I) taxonomy was built by adding a new class that did not exist in WHO but existed in GRM. The performance of the RF classifier applied to the WHO-I taxonomy further improved.