Compositional and Hierarchical Semantic Learning Model for Hospital Readmission Prediction

Clinical notes provide a wealth of patient information that is valuable for predicting clinical outcomes. In particular, predicting hospital 30-day readmission is important to improve healthcare outcomes and reduce cost. Previous works on outcome prediction using clinical notes overlook complex semantic compositions and syntactic structure when learning the note level embedding, which may fail to capture the note semantics and make accurate predictions. To address these limitations, we propose a Compositional and Hierarchical Semantic Learning Model (CHSLM). It formulates the semantic learning of clinical notes into three hierarchies: word, composition, and note, and aggregates the semantics in a bottom-up manner. To aggregate the semantics from words to compositions, we construct heterogeneous medical-composition graphs to represent word interactions within and between medical compositions and use Graph Neural Networks to learn the composition embedding. To aggregate the semantics from composition- to note-level, we incorporate a mutual BiAffine transformation process. The experimental results on 30-day readmission prediction using two types of clinical notes demonstrate the effectiveness of our method over the state-of-the-art clinical prediction models.