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 in differences in patient population characteristics and 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 unique property of GCNN where data encoded as graph edges is only implicitly used during 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. These results support our hypothesis that carefully designed GCNN-based models can overcome generalization challenges faced by prediction models.

The authors have declared no competing interest.

This study did not receive any funding.

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IRB of Emory University, GA gave ethical approval for this work. IRB of Mayo Clinic gave ethical approval for this work.

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