Time elapsed till an event of interest is often modeled using the survival analysis methodology, which estimates a survival score based on the input features. There is a resurgence of interest in developing more accurate prediction models for time-to-event prediction in personalized healthcare using modern tools such as neural networks. Higher quality features and more frequent observations improve the predictions for a patient, however, the impact of including a patient's geographic location-based public health statistics on individual predictions has not been studied. This paper proposes a complementary improvement to survival analysis models by incorporating public health statistics in the input features. We show that including geographic location-based public health information results in a statistically significant improvement in the concordance index evaluated on the Surveillance, Epidemiology, and End Results (SEER) dataset containing nationwide cancer incidence data. The improvement holds for both the standard Cox proportional hazards model and the state-of-the-art Deep Survival Machines model. Our results indicate the utility of geographic location-based public health features in survival analysis.

翻译：在生存分析方法中，通常基于输入特征估计生存评分，以建模直至感兴趣事件发生的时间。近年来，利用神经网络等现代工具开发个性化医疗中事件时间预测的高精度模型再次引起关注。更高质量的特征和更频繁的观测能改善对患者的预测，但将患者地理位置的公共卫生统计数据纳入个体预测的影响尚未被研究。本文提出一种通过将公共卫生统计数据融入输入特征来对生存分析模型进行补充改进的方法。我们证明，纳入基于地理位置的公共卫生信息后，在包含全国癌症发病率数据的监测、流行病学与最终结果（SEER）数据集上评估的一致性指数有统计学显著提升。这一改进对标准Cox比例风险模型和最新的Deep Survival Machines模型均适用。研究结果表明，基于地理位置的公共卫生特征在生存分析中具有实用价值。