Estimating Calibrated Individualized Survival Curves with Deep Learning
Authors: Fahad Kamran, Jenna Wiens240-248
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared to state-of-the-art approaches across two publicly available datasets, our proposed training scheme leads to significant improvements in calibration, while maintaining good discriminative performance. |
| Researcher Affiliation | Academia | Fahad Kamran, Jenna Wiens Computer Science and Engineering University of Michigan, Ann Arbor, MI fhdkmrn, wiensj@umich.edu |
| Pseudocode | No | The paper describes methodologies mathematically and verbally but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All deep models were built in Py Torch 1, while MTLR was implemented using the corresponding R package (Paszke et al. 2019; Haider 2019). [Footnote 1 points to https://github.com/MLD3/Calibrated-Survival-Analysis] |
| Open Datasets | Yes | We consider two publicly available datasets: the Northern Alberta Cancer Dataset (NACD) consists of 2,402 individuals with various forms of cancer (Haider et al. 2020; Yu et al. 2011). [...] CLINIC records the survival status of 6,036 patients in a hospital, with 13.2% being censored (Knaus et al. 1995). |
| Dataset Splits | Yes | We separate our data into training/validation/test sets using a 60/20/20% split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch 1' and 'R package' for MTLR, but it does not provide specific version numbers for these software components. For example, 'Py Torch 1' is not a precise version like '1.9' or '1.10'. |
| Experiment Setup | Yes | Across experiments, we use the same DRSA architecture: a one-layer LSTM with hidden size 100 and a single feed-forward layer with a sigmoid activation on the output for each time-step (Ren et al. 2019). We separate our data into training/validation/test sets using a 60/20/20% split. For training, we use Adam and a batch size of 50 (Kingma and Ba 2015). We train for 100 epochs (which, empirically, was enough for models to converge) and select the best model based on a validation set. |