[论文解读] Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels

Li‐Chin Chen, Yu Tsao+1 more|arXiv (Cornell University)|Jun 24, 2020

Machine Learning in Healthcare参考文献 35被引用 4

一句话总结

本研究利用深度学习与可解释人工智能（XAI）方法，基于全国性医保数据预测患者在不同医院层级间的选择，即使在标签高度不平衡的情况下仍取得了优异性能（AUC：0.90，准确率：0.90）。研究识别出社会认可度与医师密度为最重要的预测特征，并表明数据表示的改变可提升模型准确率。

ABSTRACT

In countries that enabled patients to choose their own providers, a common problem is that the patients did not make rational decisions, and hence, fail to use healthcare resources efficiently. This might cause problems such as overwhelming tertiary facilities with mild condition patients, thus limiting their capacity of treating acute and critical patients. To address such maldistributed patient volume, it is essential to oversee patients choices before further evaluation of a policy or resource allocation. This study used nationwide insurance data, accumulated possible features discussed in existing literature, and used a deep neural network to predict the patients choices of hospital levels. This study also used explainable artificial intelligence methods to interpret the contribution of features for the general public and individuals. In addition, we explored the effectiveness of changing data representations. The results showed that the model was able to predict with high area under the receiver operating characteristics curve (AUC) (0.90), accuracy (0.90), sensitivity (0.94), and specificity (0.97) with highly imbalanced label. Generally, social approval of the provider by the general public (positive or negative) and the number of practicing physicians serving per ten thousand people of the located area are listed as the top effecting features. The changing data representation had a positive effect on the prediction improvement. Deep learning methods can process highly imbalanced data and achieve high accuracy. The effecting features affect the general public and individuals differently. Addressing the sparsity and discrete nature of insurance data leads to better prediction. Applications using deep learning technology are promising in health policy making. More work is required to interpret models and practice implementation.

研究动机与目标

• 为解决允许患者自主选择医疗服务提供者的医疗系统中因患者非理性选择导致的医疗资源利用效率低下问题。
• 基于真实世界全国性医保数据，建立患者医院层级选择决策的模型。
• 利用可解释人工智能（XAI）方法对模型预测结果进行解释，分别提供面向公众的总体洞察与面向个体的分析。
• 评估数据表示变换对不平衡医疗数据中模型性能的影响。
• 通过数据驱动且可解释的患者行为洞察，支持卫生政策制定。

提出的方法

• 基于现有文献提取特征，利用全国性医保索赔数据训练深度神经网络。
• 采用技术手段处理患者医院层级选择中高度不平衡的标签分布问题。
• 应用可解释人工智能方法（如SHAP或类似LIME的解释方法），评估在人群层面与个体层面上各特征的贡献度。
• 探索数据表示的变换，以提升模型的泛化能力与预测性能。
• 采用标准指标（AUC、准确率、灵敏度、特异性）评估模型性能。
• 通过训练模型的特征重要性分析，识别出最主要的贡献特征。

实验结果

研究问题

• RQ1 尽管存在高度不平衡的数据，深度学习模型能否准确预测患者在不同医院层级间的选择？
• RQ2 哪些与患者及医疗服务提供者相关的特征对医院层级选择决策具有最显著的影响？
• RQ3 社会认知与本地医疗人力资源的可及性在多大程度上影响患者的选择？
• RQ4 在此情境下，改变数据表示在多大程度上能提升预测性能？
• RQ5 可解释人工智能方法能否有效解释模型决策，以支持公共政策制定与个体层面的理解？

主要发现

• 深度学习模型的受试者工作特征曲线下面积（AUC）达到0.90，表明具有强大的判别能力。
• 模型准确率达到0.90，灵敏度为0.94，特异性为0.97，表明在不平衡数据上表现稳健。
• 公众对医院的社会认可度被识别为最重要的影响特征之一。
• 本地每10,000人中执业医师的数量是另一项关键预测特征。
• 对数据表示的调整带来了模型预测性能的可测量提升。
• 特征重要性分析揭示了在公众层面与个体决策中特征影响模式的显著差异。