Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
The paper presents an innovative approach to learning the latent structure of Electronic Health Records (EHR) using a model referred to as Graph Convolutional Transformer (GCT). By leveraging the graphical nature of EHR data, the authors aim to improve the performance on a variety of prediction tasks such as readmission prediction and graph reconstruction, especially in scenarios where explicit structure information is incomplete or unavailable.
Problem Context and Motivation
EHR data are inherently structured with complex interrelations between diagnoses, treatments, and outcomes. Conventional methods often simplify this data by treating individual encounters as bags of features, which may overlook important relational information. This simplification can undermine the performance of predictive models. Previous efforts, such as the MiME model, highlighted the importance of leveraging encounter structures but assumed the availability of complete structural information, which is typically not the case in practice.
Introducing Graph Convolutional Transformer (GCT)
The GCT aims to jointly learn the hidden structure of EHR from limited structural information while performing predictive tasks. The model is built upon the Transformer architecture, renowned for its prowess in sequence modeling, and extends it by integrating graph convolution techniques to better handle the structural properties of EHR data. The authors introduce several key modifications:
- Attention Masks: These masks are crafted based on domain knowledge to limit possible connections, ensuring physically implausible ones are ignored.
- Prior Conditional Probabilities: These probabilities guide the self-attention mechanism, steering it towards likely connections based on statistical relationships between medical codes.
- KL-Divergence Penalty: This term penalizes deviation from established probabilities, ensuring the self-attention mechanism does not stray far from medically plausible relations.
Experimental Evaluation
Experiments conducted on both synthetic and real-world data (eICU dataset) demonstrate superior performance of GCT compared to baseline models that either disregard structural information or rely entirely on data statistics. Across tasks like graph reconstruction and readmission prediction, GCT consistently outperforms models such as shallow and deep neural networks as well as traditional graph convolutional networks without the enhancements of the GCT.
- Synthetic Data Evaluation: The synthetic dataset mimics the structured form of EHR, verifying GCT's ability to reconstruct the underlying graph accurately and to distinguish between subtle differences, as seen in diagnosis-treatment classification tasks.
- Real-world Data Evaluation: On the eICU dataset, GCT improved prediction tasks like readmission and mortality, showcasing its practical utility in clinical settings where structural information is sparse.
Practical and Theoretical Implications
The introduction of GCT opens up new avenues for enhancing EHR data processing. Practically, the model provides healthcare research with a more nuanced toolset for predicting patient outcomes by capitalizing on the implicit structures in EHR data. Theoretically, it bridges the gap between graph-based and sequence-based modeling methods and highlights the potential of dynamic structure learning in domains beyond healthcare.
Future Directions
The paper suggests integrating GCT with sequence aggregators like RNNs for patient-level predictions, paving the way for broader applications such as chronic disease modeling and acute event prediction. Further research may focus on refining attention mechanisms to extract more specific medical insights, potentially enhancing interpretability alongside predictive performance.
In conclusion, this work signifies a substantial step forward in medical informatics, enabling more effective utilization of EHR data by recognizing and exploiting their implicit graphical structure through advanced transformer-based techniques.