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.
翻译:目标:改善地理准入仍然是在卫生政策设计期间确定区域医疗资源是否充足的一个关键问题,但病人选择可能是各种因素复杂互动的结果。本研究的目的是提出一种深神经网络方法,以模拟病人选择远途旅行以获得护理的复杂决定,这是分配资源的一个重要指标。方法:我们使用台湾4年全国保险数据,并积累了先前文献中讨论的可能特点。本研究提议使用以动态神经网络为基础的框架作出预测。模型性能是用其他机器学习方法测试的。对模型性能进行了测试。对拟议框架作了进一步解释,使用了综合梯度分析特征加权数。结果:我们成功地展示了使用CNN框架预测病人旅行距离的有效性,实现了0.968、AUC的准确度0.969、0.960的敏感性和0.989的特性。以CNN为基础的框架超越了所有其他方法。在这项研究中,IG的权重是可以解释的;然而,对拟议框架作了进一步解释,使用了综合梯度分析。结果:我们成功地展示了使用CNN框架预测病人旅行距离的准确性,实现了0.969的敏感度和0.989的特性。基于CNN的框架超越了所有其他方法。在研究中,该模型中可以解释;但是,这种关系并没有显示我们所了解的远距分析的远路标值与我们所了解的远路标。