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.

本研究提出一种改进的生存分析模型，它在输入特征中包括公共卫生统计数据，通过在美国的癌症发病率数据集中试验表明，包括地理位置的公共卫生数据可以显著提高预测性能。