Risk-RAM: An Interpretable Deep Survival Prediction Model for COVID-19 Prognosis Using Chest X-Ray Images

COVID-19 has strained global public health, necessitating accurate diagnosis and intervention to control disease spread and reduce mortality rates. This paper introduces an inter-pretable method Risk-RAM for survival prediction model designed specifically for improved understanding and trust in prognosis using chest X-ray (CXR) images. Our approach produces regional interpretable outcomes that effectively capture essential disease features while focusing on rare but critical abnormal regions. The predictive results provide enhanced clarity and transparency through risk area localization, enabling clinicians to make informed decisions regarding COVID-19 diagnosis with a better understanding of prognostic insights. We evaluate the proposed method on a multi-center survival dataset and demonstrate its effectiveness via quantitative and qualitative assessments, achieving superior C-indexes (0.764 and 0.727) and time-dependent AUCs (0.799 and 0.691). These results suggest that our explainable deep survival prediction model surpasses traditional survival analysis methods in risk prediction, improving interpretability for clinical decision making and enhancing AI system trustworthiness. Our code is available at https://github.com/zzs95/RISK-RAM.