Abstract
Figure: Graphical representation of the study; (a) Difference in comorbidities of similar patient populations (patients hospitalized with COVID-19 infection) from two different healthcare institution highlighting the challenge of model generalization across different institutions; (b) Sample graph formation involving several clinical data elements – edge features (e) are only used for edge formation based on similarity (Ø), features and similarity functions can be updated when trained graph convolutional neural network is shipped to outside institutions.
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While many machine learning and deep learning-based models for clinical event prediction leverage various data elements from electronic healthcare records such as patient demographics and billing codes, such models face severe challenges when tested outside of their institution of training. These challenges are rooted not only in differences in patient population characteristics, but medical practice patterns of different institutions.
We propose a solution to this problem through systematically adaptable design of graph-based convolutional neural networks (GCNN) for clinical event prediction. Our solution relies on the unique property of GCNN where data encoded as graph edges is only implicitly used during the prediction process and can be adapted after model training without requiring model re-training.
Our adaptable GCNN-based prediction models outperformed all comparative models during external validation for two different clinical problems, while supporting multimodal data integration. For prediction of hospital discharge and mortality, the comparative fusion baseline model achieved 0.58 [0.52–0.59] and 0.81[0.80–0.82] AUROC on the external dataset while the GCNN achieved 0.70 [0.68–0.70] and 0.91 [0.90–0.92] respectively. For prediction of future unplanned transfusion, we observed even more gaps in performance due to missing/incomplete data in the external dataset − late fusion achieved 0.44[0.31–0.56] while the GCNN model achieved 0.70 [0.62–0.84].
These results support our hypothesis that carefully designed GCNN-based models can overcome generalization challenges faced by prediction models.