Interpretable Caries Development Prediction with Event Intervals

This paper presents a novel approach to predict caries development in dental patients by analyzing event interval sequences extracted from electronic health records (EHRs). Leveraging a subset of the SKaPa dataset, comprising 1,500 patients aged 30 to 70, and encompassing 14,870 tooth-wise event interval sequences, our method surpasses baseline models and state-of-the-art deep learning approaches. By assessing temporal relations between event intervals and utilizing interpretable classification models such as decision trees (DTs) and random forests (RFs), our approach achieves higher recall rates and area under the precision-recall curve (AUPRC) scores in identifying cases of caries development. Notably, our methods demonstrate superior performance in learning the minority class (i.e., caries development), underscoring the effectiveness of the event interval representation in capturing predictive features. These findings underscore the potential of our approach to improve caries prognosis and enable targeted interventions in dental healthcare.