Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks
Authors: Changhee Lee, William Zame, Jinsung Yoon, Mihaela van der Schaar
AAAI 2018 | Venue PDF | 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). |