MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital
  Readmission Prediction
Yan Miao Lequan Yu
Hospital readmission prediction is considered an essential approach to
decreasing readmission rates, which is a key factor in assessing the quality
and efficacy of a healthcare system. Previous studies have extensively utilized
three primary modalities, namely electronic health records (EHR), medical
images, and clinical notes, to predict hospital readmissions. However, the
majority of these studies did not integrate information from all three
modalities or utilize the spatiotemporal relationships present in the dataset.
This study introduces a novel model called the Multimodal Spatiotemporal
Graph-Transformer (MuST) for predicting hospital readmissions. By employing
Graph Convolution Networks and temporal transformers, we can effectively
capture spatial and temporal dependencies in EHR and chest radiographs. We then
propose a fusion transformer to combine the spatiotemporal features from the
two modalities mentioned above with the features from clinical notes extracted
by a pre-trained, domain-specific transformer. We assess the effectiveness of
our methods using the latest publicly available dataset, MIMIC-IV. The
experimental results indicate that the inclusion of multimodal features in MuST
improves its performance in comparison to unimodal methods. Furthermore, our
proposed pipeline outperforms the current leading methods in the prediction of
hospital readmissions.