Predicting patient returns due to complications and recommending follow-up appointments after a dental extraction using machine learning

Abstract

Purpose: After a dental extraction, knowing which patients are at higher risk of return due to complications may help plan a better treatment approach and lead to targeted follow-up appointments. The objective of this study was to predict which patients are at higher risk of return due to complications from a dental extraction using data from electronic dental records (EDRs). Methods: Structured and unstructured data in EDRs of 14,541 patients who had a dental extraction were used to train three types of machine learning models. Methods to address an anticipated class imbalance were also investigated. The primary evaluation measure was recall. Other measures included area under the receiver ..