主题：DEEP LEARNING OF SEMI-COMPETING RISK DATA VIA A NEW NEURAL EXPECTATION-MAXIMIZATION ALGORITHM
主讲人：美国密歇根大学公共卫生学院生物统计学系李颐（YI LI）教授
主持人：统计与数据科学学院林华珍教授
时间：2026年7月14日（周二）下午4:00-5:00
地点：柳林校区弘远楼408会议室
主办单位：统计与数据科学学院和统计研究中心 国际交流与合作处 科研处
主讲人简介：
李颐教授，现任美国密歇根大学公共卫生学院生物统计学系教授、M. Anthony Schork 讲席教授，并担任公共卫生学院中国项目主任及全球统计核心中心联合主任。他是国际知名的生物统计学家，在生存分析、高维数据、测量误差模型、空间统计及统计遗传学等领域做出了重要贡献。他的研究致力于发展面向复杂生物医学数据的创新统计方法，特别聚焦于存在删失、测量误差、高维协变量与空间相关性的建模问题。其方法论成果广泛应用于癌症预后、肾脏疾病、呼吸系统健康及传染病研究等重要公共卫生与临床领域。他已在国际顶尖统计学期刊发表论文270余篇，包括 Journal of the American Statistical Association、Biometrika、Biometrics 等。他的学术成果被广泛引用（Google Scholar 引用19316次，h指数60，i10指数 183。
内容提要：
Prognostication for lung cancer, a leading cause of mortality, remains a complex task, as it needs to quantify the associations of risk factors and health events spanning a patient’s entire life. One challenge is that an individual’s disease course involves non-terminal (e.g., disease progression) and terminal (e.g., death) events, which form semi-competing relationships. Our motivation comes from the Boston Lung Cancer Study, a large lung cancer survival cohort, which investigates how risk factors influence a patient’s disease trajectory. Following developments in the prediction of time-to-event outcomes with neural networks, deep learning has become a focal area for the development of risk prediction methods in survival analysis. However, limited work has been done to predict multi-state or semi-competing risk outcomes, where a patient may experience adverse events such as disease progression prior to death. We propose a neural expectation-maximization algorithm for semi-competing risks to bridge the gap between classical semi-competing survival models and deep learning. Our algorithm enables estimation of the nonparametric baseline hazards of each state transition, risk functions of predictors, and the degree of dependence among different transitions, via a multitask deep neural network with transition-specific sub-architectures. We apply our method to the Boston Lung Cancer Study and investigate the impact of clinical and genetic predictors on disease progression and mortality.
