Multi-Task Learning based Survival Analysis for Predicting Alzheimer's Disease Progression with Multi-Source Block-wise Missing Data
Yan Li, Tao Yang, Jiayu Zhou, Jieping Ye · Society for Industrial and Applied Mathematics eBooks · 2018
There are many major diseases that remain incurable, e.g., cancer and Alzheimer's disease (AD). Hence, the prevention for these diseases has more impact than diagnosis and treatment. Survival analysis aims at predicting the time of occurrence of specific events of interest. It can be used to identify patients of high risk, which helps healthcare system to effectively allocate limited medical resources. In many real-world applications, such as healthcare analysis, a lot of datasets are collected from multiple data sources and exhibit a block-wise missing pattern, i.e., each patient takes different types of tests and receives various treatments, and each test/treatment associates with a corresponding set of features. However, all the existing survival analysis methods are designed for fully observed datasets and may not be directly applied when such block-wise missing presented. The proposed work in this paper aims at addressing aforementioned research challenges. Specifically, we employ a partition method that decomposes the multi-source block-wise missing data into the multiple completed sub-matrix; thus, transforms the original problem into a series of related multi-source survival analysis problems. To deal with these problems, we propose a two-layer multi-task learning model that achieves both feature-level and source-level analysis, and the proposed model is able to take advantage of the structure information in the block-wise missing pattern. We apply the proposed method in a real-world AD dataset to study the stage conversion of AD patients. Our experimental results show that the proposed method outperforms the other state-of-the-art methods.