  Objective: Improving geographical access remains a key issue in determining
the sufficiency of regional medical resources during health policy design.
However, patient choices can be the result of the complex interactivity of
various factors. The aim of this study is to propose a deep neural network
approach to model the complex decision of patient choice in travel distance to
access care, which is an important indicator for policymaking in allocating
resources. Method: We used the 4-year nationwide insurance data of Taiwan and
accumulated the possible features discussed in earlier literature. This study
proposes the use of a convolutional neural network (CNN)-based framework to
make predictions. The model performance was tested against other machine
learning methods. The proposed framework was further interpreted using
Integrated Gradients (IG) to analyze the feature weights. Results: We
successfully demonstrated the effectiveness of using a CNN-based framework to
predict the travel distance of patients, achieving an accuracy of 0.968, AUC of
0.969, sensitivity of 0.960, and specificity of 0.989. The CNN-based framework
outperformed all other methods. In this research, the IG weights are
potentially explainable; however, the relationship does not correspond to known
indicators in public health, similar to common consensus. Conclusions: Our
results demonstrate the feasibility of the deep learning-based travel distance
prediction model. It has the potential to guide policymaking in resource
allocation.
