Abstract
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.
Recommended Citation
N. Seidi et al., "Using Geographic Location-Based Public Health Features in Survival Analysis," Proceedings - 2023 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2023, pp. 80 - 91, Association for Computing Machinery (ACM), Jan 2023.
The definitive version is available at https://doi.org/10.1145/3580252.3586972
Department(s)
Computer Science
Keywords and Phrases
Deep Neural Networks; Machine Learning; Medical Cyber-Physical System; Smart Health; Survival Analysis
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Association for Computing Machinery, All rights reserved.
Publication Date
01 Jan 2023
Comments
National Cancer Institute, Grant None