DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks
Authors: Changhee Lee, William Zame, Jinsung Yoon, Mihaela van der Schaar
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comparisons with previous models on the basis of real and synthetic datasets demonstrate that Deep Hit achieves large and statistically significant performance improvements over previous state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1 Department of Electrical and Computer Engineering, University of California, Los Angeles, USA 2 Department of Economics, University of California, Los Angeles, USA 3 Department of Engineering Science, University of Oxford, UK 4 Alan Turing Institute, London, UK |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide a link or explicit statement for the open-sourcing of its own Deep Hit code. It only references a GitHub link for a baseline model (Deep Surv) in a footnote. |
| Open Datasets | Yes | The United Network for Organ Sharing (UNOS) database4 consists of patients who underwent heart transplantation in the period 1985-2015. 4https://www.unos.org/data/; The Surveillance, Epidemiology, and End Results Program (SEER)5 dataset provides information on breast cancer patients during the years 1992-2007. 5https://seer.cancer.gov/causespecific/; The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset... for details see (Bilal et al. 2013); We also created a synthetic dataset with two competing risks, in the spirit of (Alaa and van der Schaar 2017). |
| Dataset Splits | Yes | For evaluation, we applied 5-fold cross validation: we randomly separated the data into training set (80%) and testing set (20%). We reserved 20% of the training set as a validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper only states "Deep Hit was implemented in a Tensorflow environment." without specifying the version of TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | Deep Hit is a 4-layer network consisting of 1 fully-connected layer for the shared sub-network and 2 fullyconnected layers for each cause-specific sub-network and a softmax layer as the output layer. For hidden layers, the number of nodes were set as 3, 5, and 3 times of the covariate dimension for the layer 1, 2, and 3, respectively, with Re Lu activation function. The network was trained by back-propagation via Adam optimizer with a batch size of 50 and a learning rate of 10 4. Dropout probability of 0.6 and Xavier initialization was applied for all the layers (Deep Hit was implemented in a Tensorflow environment). |