Predicting treatment repetitions in the implant denture therapy process

Healthcare can be considerably expensive for both patients and insurance companies. In some cases, high costs in healthcare are an indirect outcome of a low quality of care, for example, when treatments have to be repeated. Unfortunately, identifying the factors that lead to such repetitions is a complex and challenging task. In this paper, we focus on the domain of dental healthcare and develop an approach that can predict treatment repetitions in the context of the implant denture therapy process. The challenges associated with predicting treatment repetitions in this setting are considerable. First, hardly any patient undergoes the exact same series of treatments like another. This results in a high degree of variation in the data. Second, only a few patients experience treatment repetitions. This lead to a highly imbalance in the data. To address these challenges, we develop a prediction technique that particularly exploits the process perspective. What is more, we apply so-called resampling methods to deal with the imbalance in the data. Our resulting model is able to predict treatment repetitions with an AUC value of 0.69.