Deep Learning-based Time-to-event Analysis of Depression and Asthma using the All of Us Research Program

Presentation Time: 08:00 AM - 08:15 AM

Abstract Keywords: Deep Learning, Bioinformatics, Data Mining
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

While there is a growing recognition of the association between depression and asthma, few studies have leveraged deep learning-based (DL-based) models in analysis and prediction on a large sample size retrospective cohort study. We analyzed the association between depression and asthma among 239,161 participants of the All of Us Research Program through DL-based, logistic regression, and Cox Proportional Hazards (CoxPH) models. We used SHAP values to help interpret DL-based models and c-index to help model performance. Results suggest a significant odds ratio for depression in asthma. The c-indices for the CoxPH, DeepSurv, and DeepHit were 0.619, 0.625, and 0.596, respectively. SHAP indicated a different set of important variables when compared with CoxPH. In conclusion, we provide strong evidence of a positive relationship between depression and asthma. DL-based models did not outperform the CoxPH model on the c-index. Sex and income may play important roles in depression in asthma patients.

Speaker(s):
Xueting Wang, Master of Public Health
Yale University

Author(s):
Xueting Wang, Master of Public Health - Yale University; Lucila Ohno-Machado, MD, PhD - UC San Diego School of Medicine; Jose Gomez Villalobos, MD, MS - Yale University; Wen Gu, MD - Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine; Rongyi Sun, MD - NewYork-Presbyterian/Columbia University Irving Medical Center; Jihoon Kim, PhD - Yale University;