Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models
Time-to-event data is widespread across the life sciences and engineering, but it is typically encountered together with censoring, which complicates the application of standard machine learning methods. Deep Cox models have emerged as a popular method for analyzing time-to-event data because they gracefully handle censoring and can be used with unstructured data such as clinical text reports, genomic sequences, and pathology images. However, their predicted survival probabilities are often poorly calibrated, thus limiting their practical utility. In this paper, we propose a novel post hoc calibration method for Deep Cox models that uses isotonic regression to refine predicted survival pr...
| Authors | Anchit Jain, Kevin Zhang, Stephen Bates |
|---|---|
| Primary category | stat.ML |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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