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..
Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources
Authors: Meng Xia, Jonathan Wilson, Benjamin Goldstein, Ricardo Henao
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments on two real-world datasets demonstrating that CLOPPS consistently outperforms strong baselines in several practical scenarios. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Duke University, Durham, US 2Department of Biostatistics and Bioinformatics, Duke University, Durham, US 3King Abdullah University of Science and Technology, Thuwal, KSA. |
| Pseudocode | No | The paper does not contain any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code used in the experiments is available at https://github.com/mx41-m/Contrastive-Learning.git. |
| Open Datasets | Yes | Given that the Private dataset is not readily publicly accessible, we also validate CLOPPS using the MIMIC-III clinical database (Johnson et al., 2016). |
| Dataset Splits | Yes | These sequences were then divided into training, validation, and test datasets following an 8 : 1 : 1 ratio. |
| Hardware Specification | No | The paper mentions that models were trained and experiments conducted, but it does not provide any specific details about the hardware used, such as GPU models, CPU specifications, or memory. |
| Software Dependencies | No | The paper mentions using the Hugging Face's transformers library, but it does not specify any version numbers for this or any other software dependencies, which is necessary for reproducibility. |
| Experiment Setup | Yes | The encoders for CLOPPS are trained for 50, 100 and 100 epochs on MMNIST, Private and MIMIC, respectively. The classifiers for CLOPPS are trained for 10, 5 and 5 epochs on MMNIST, Private and MIMIC, respectively. In CLOPPS, the values for τ, w and d are set to 0.1, 2 and 12, respectively, based on experimental results. For all models (excluding Elastic Net), Adam W (Loshchilov & Hutter, 2017) is employed as the optimizer. The values for the learning rate, beta, weight decay and batch size are set for all models to 10 4, (0.9, 0.999), 0.01, and 64, respectively. |