Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm

• URL: http://arxiv.org/abs/2212.12028v1
• Date: Thu, 22 Dec 2022 20:38:57 GMT
• Title: Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm
• Authors: Stephen Salerno and Yi Li
• Abstract summary: Our motivation comes from the Boston Lung Cancer Study, which investigates how risk factors influence a patient's disease trajectory. We propose a novel neural expectation-maximization algorithm to bridge the gap between classical statistical approaches and machine learning.
• Score: 5.253100011321437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 novel neural expectation-maximization algorithm to bridge the gap between classical statistical approaches and machine learning. Our algorithm enables estimation of the non-parametric baseline hazards of each state transition, risk functions of predictors, and the degree of dependence among different transitions, via a multi-task 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.

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