`

Timezone: »

 
Poster
X-CAL: Explicit Calibration for Survival Analysis
Mark Goldstein · Xintian Han · Aahlad Puli · Adler Perotte · Rajesh Ranganath

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #976

Survival analysis models the distribution of time until an event of interest, such as discharge from the hospital or admission to the ICU. When a model’s predicted number of events within any time interval is similar to the observed number, it is called well-calibrated. A survival model’s calibration can be measured using, for instance, distributional calibration (D-CALIBRATION) [Haider et al., 2020] which computes the squared difference between the observed and predicted number of events within different time intervals. Classically, calibration is addressed in post-training analysis. We develop explicit calibration (X-CAL), which turns D-CALIBRATION into a differentiable objective that can be used in survival modeling alongside maximum likelihood estimation and other objectives. X-CAL allows us to directly optimize calibration and strike a desired trade-off between predictive power and calibration. In our experiments, we fit a variety of shallow and deep models on simulated data, a survival dataset based on MNIST, on length-of-stay prediction using MIMIC-III data, and on brain cancer data from The Cancer Genome Atlas. We show that the models we study can be miscalibrated. We give experimental evidence on these datasets that X-CAL improves D-CALIBRATION without a large decrease in concordance or likelihood.

Author Information

Mark Goldstein (New York University)
Xintian Han (New York University)
Aahlad Puli (NYU)
Adler Perotte (Columbia University)
Rajesh Ranganath (New York University)

More from the Same Authors