2 research outputs found
Deep Cox Mixtures for Survival Regression
Survival analysis is a challenging variation of regression modeling because
of the presence of censoring, where the outcome measurement is only partially
known, due to, for example, loss to follow up. Such problems come up frequently
in medical applications, making survival analysis a key endeavor in
biostatistics and machine learning for healthcare, with Cox regression models
being amongst the most commonly employed models. We describe a new approach for
survival analysis regression models, based on learning mixtures of Cox
regressions to model individual survival distributions. We propose an
approximation to the Expectation Maximization algorithm for this model that
does hard assignments to mixture groups to make optimization efficient. In each
group assignment, we fit the hazard ratios within each group using deep neural
networks, and the baseline hazard for each mixture component
non-parametrically.
We perform experiments on multiple real world datasets, and look at the
mortality rates of patients across ethnicity and gender. We emphasize the
importance of calibration in healthcare settings and demonstrate that our
approach outperforms classical and modern survival analysis baselines, both in
terms of discriminative performance and calibration, with large gains in
performance on the minority demographics.Comment: Machine Learning for Healthcare Conference, 202