  Background:
  Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading
cause of death in the US, underlining the importance of accurate ADRD risk
prediction. While recent advancement in ADRD risk prediction have primarily
relied on imaging analysis, yet not all patients undergo medical imaging before
an ADRD diagnosis. Merging machine learning with claims data can reveal
additional risk factors and uncover interconnections among diverse medical
codes.
  Objective:
  Our goal is to utilize Graph Neural Networks (GNNs) with claims data for ADRD
risk prediction. Addressing the lack of human-interpretable reasons behind
these predictions, we introduce an innovative method to evaluate relationship
importance and its influence on ADRD risk prediction, ensuring comprehensive
interpretation.
  Methods:
  We employed Variationally Regularized Encoder-decoder Graph Neural Network
(VGNN) for estimating ADRD likelihood. We created three scenarios to assess the
model's efficiency, using Random Forest and Light Gradient Boost Machine as
baselines. We further used our relation importance method to clarify the key
relationships for ADRD risk prediction.
  Results:
  VGNN surpassed other baseline models by 10% in the area under the receiver
operating characteristic. The integration of the GNN model and relation
importance interpretation could potentially play an essential role in providing
valuable insight into factors that may contribute to or delay ADRD progression.
  Conclusions:
  Employing a GNN approach with claims data enhances ADRD risk prediction and
provides insights into the impact of interconnected medical code relationships.
This methodology not only enables ADRD risk modeling but also shows potential
for other image analysis predictions using claims data.
