DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis

Authors

  • Md Mahmudur Rahman University of Maryland Baltimore County
  • Koji Matsuo University of Southern California
  • Shinya Matsuzaki Osaka University
  • Sanjay Purushotham University of Maryland, Baltimore County

Keywords:

Healthcare, Medicine & Wellness, (Deep) Neural Network Algorithms, Applications

Abstract

Competing Risk Analysis (CRA) aims at the correct estimation of the marginal probability of occurrence of an event in the presence of competing events. Many of the statistical approaches developed for CRA are limited by strong assumptions about the underlying stochastic processes. To overcome these issues and to handle censoring, machine learning approaches for CRA have designed specialized cost functions. However, these approaches are not generalizable, and are computationally expensive. This paper formulates CRA as a cause-specific regression problem and proposes DeepPseudo models, which use simple and effective feed-forward deep neural networks, to predict the cumulative incidence function (CIF) using Aalen-Johansen estimator-based pseudo values. DeepPseudo models capture the time-varying covariate effect on CIF while handling the censored observations. We show how DeepPseudo models can address co-variate dependent censoring by using modified pseudo values. Experiments on real and synthetic datasets demonstrate that our proposed models obtain promising and statistically significant results compared to the state-of-the-art CRA approaches. Furthermore, we show that explainable methods such as Layer-wise Relevance Propagation can be used to interpret the predictions of our DeepPseudo models.

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Published

2021-05-18

How to Cite

Rahman, M. M., Matsuo, K., Matsuzaki, S., & Purushotham, S. (2021). DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 479-487. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16125

Issue

Section

AAAI Technical Track on Application Domains