Wang Xueting, Ohno-Machado Lucila, Gomez Jose L, Gu Wen, Sun Rongyi, Kim Jihoon
Section of Biomedical Informatics and Data Science.
Program in Computational Biology and Bioinformatics.
AMIA Annu Symp Proc. 2025 May 22;2024:1186-1195. eCollection 2024.
While there is a growing recognition of the association between depression and asthma, few studies have leveraged deep learning-based (DL-based) models in a retrospective cohort study with a large sample size. 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 evaluate model performance. Results suggest a significant odds ratio for depression in asthma. The c-indices for the CoxPH, DeepSurv, and DeepHit models were 0.619, 0.625, and 0.596, respectively. SHAP indicated a different set of important variables when compared with the CoxPH model. In conclusion, we provide strong evidence of a positive relationship between depression and asthma. Also, DL-based models did not outperform the CoxPH model on the c-index. Sex at birth and income may play important roles in occurrence of depression in asthma patients.
虽然抑郁症与哮喘之间的关联越来越受到认可，但在大样本量的回顾性队列研究中，很少有研究利用基于深度学习（DL）的模型。我们通过基于DL的模型、逻辑回归模型和Cox比例风险（CoxPH）模型，分析了“我们所有人研究计划”中239161名参与者的抑郁症与哮喘之间的关联。我们使用SHAP值来帮助解释基于DL的模型，并使用c指数来评估模型性能。结果表明，哮喘患者患抑郁症的比值比显著。CoxPH模型、DeepSurv模型和DeepHit模型的c指数分别为0.619、0.625和0.596。与CoxPH模型相比，SHAP显示出一组不同的重要变量。总之，我们提供了抑郁症与哮喘之间存在正相关关系的有力证据。此外，基于DL的模型在c指数上并未优于CoxPH模型。出生时的性别和收入可能在哮喘患者抑郁症的发生中起重要作用。
