ArXiv Preprint
In this study, we introduce ExBEHRT, an extended version of BEHRT (BERT
applied to electronic health records), and apply different algorithms to
interpret its results. While BEHRT considers only diagnoses and patient age, we
extend the feature space to several multimodal records, namely demographics,
clinical characteristics, vital signs, smoking status, diagnoses, procedures,
medications, and laboratory tests, by applying a novel method to unify the
frequencies and temporal dimensions of the different features. We show that
additional features significantly improve model performance for various
downstream tasks in different diseases. To ensure robustness, we interpret
model predictions using an adaptation of expected gradients, which has not been
previously applied to transformers with EHR data and provides more granular
interpretations than previous approaches such as feature and token importances.
Furthermore, by clustering the model representations of oncology patients, we
show that the model has an implicit understanding of the disease and is able to
classify patients with the same cancer type into different risk groups. Given
the additional features and interpretability, ExBEHRT can help make informed
decisions about disease trajectories, diagnoses, and risk factors of various
diseases.
Maurice Rupp, Oriane Peter, Thirupathi Pattipaka
2023-03-22
