A transformer-based framework for temporal health event prediction with graph-enhanced representations

Objective:

Deep learning approaches have demonstrated significant potential in predicting temporal health events in recent years. However, existing methods have not fully leveraged the complex interactions among comorbidities and have overlooked imbalances and temporal irregularities in admission records.

Methods:

This study proposes GLT-Net, a deep learning approach that combines Graph Learning with Transformer framework to tackle these challenges. GLT-Net begins by constructing a patient association graph to generate unique representations for each individual. At the same time, the hierarchical structure of diagnosis codes is utilized to pre-train the diagnosis code embeddings. Subsequently, a comorbidity association matrix is created to illustrate the relationships between comorbidities, and graph neural networks are employed to enhance the feature representations of diagnosis codes. Finally, a Transformer-Encoder framework captures the dependencies in historical admission records by incorporating time information.

Results:

We demonstrate our approach on two tasks in temporal health event predcition. Experimental results on real-world datasets show that GLT-Net outperforms baseline models in forecasting temporal health events. Additionally, a case study demonstrates the effectiveness of GLT-Net in predicting health events.

Conclusion:

Understanding progression patterns over time, comorbidity associations, and patient characterization is essential for predicting temporal health events. Our study provides new insights and methods for a deeper understanding of patient health status and disease trends. Moreover, our model can be extended to other data sources, enhancing its versatility.