X-CAL: Explicit Calibration for Survival Analysis
Authors: Mark Goldstein, Xintian Han, Aahlad Puli, Adler Perotte, Rajesh Ranganath
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we fit a variety of shallow and deep models on simulated data, a survival dataset based on MNIST, on lengthof-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. |
| Researcher Affiliation | Academia | Mark Goldstein New York University EMAIL Xintian Han New York University EMAIL Aahlad Puli New York University EMAIL Adler J. Perotte Columbia University EMAIL Rajesh Ranganath New York University EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/rajesh-lab/X-CAL |
| Open Datasets | Yes | We simulate a survival dataset conditionally on the MNIST dataset [Le Cun et al., 2010]. predict the length of stay (in number of hours) in the ICU, using data from the MIMIC-III dataset [Johnson et al., 2016], the glioma (a type of brain cancer) dataset 4 collected as part of the TCGA program and studied in [Network, 2015]. 4https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/studiedcancers/glioma |
| Dataset Splits | Yes | We sample a train/validation/test sets with 100k/50k/50k datapoints, respectively. We use Py Torch s MNIST with test split into validation/test. There are 2, 925, 434 and 525, 912 instances in the training and test sets. We split the training set in half for train and validation. The train/validation/test sets are made of 552/276/277 datapoints respectively |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch s MNIST' and 'Lifelines package' but does not specify version numbers for these software components. |
| Experiment Setup | No | The paper states 'We use γ = 10000.' and 'We use 20 D-CALIBRATION bins disjoint over [0, 1] for all experiments except for the cancer data, where we use 10 bins as in Haider et al. [2020].' and 'All reported results are an average of three seeds.' but does not include other specific hyperparameters like learning rates, batch sizes, or optimizer details. |