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..
Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Authors: Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical timeseries. (Abstract) and 5. Experimental Setup |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, ETH Z urich, Switzerland 2Amazon (most work was done when Francesco was at ETH Zurich and MPI-IS). |
| Pseudocode | No | The paper describes methods and processes in narrative text and diagrams (Figure 2), but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/ratschlab/ncl |
| Open Datasets | Yes | MIMIC-III Benchmark. The MIMIC-III dataset (Johnson et al., 2016) is the most commonly used dataset for tasks related to EHR data. (Section 5.1) |
| Dataset Splits | Yes | We used early stopping on validation set loss and an Adam optimizer. (Section 5.3) |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, or detailed computing infrastructure) used to run the experiments. |
| Software Dependencies | No | The paper mentions certain components like 'Adam optimizer' and 'Temporal Convolutional Networks (TCN)' but does not provide specific version numbers for any software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | We trained all unsupervised methods for 25k steps with a batch size of 2048. We used an Adam optimizer with a linear warm-up between 1e-5 and 1e-3 for 2.5k steps... We used a temperature of 0.1, a queue of size 65536, and an embedding size of 64 for all tasks... for NCL(nw) we chose α = 0.3 and w = 16 on MIMIC-III Benchmark and α = 0.4 and w = 12 on Physionet 2019. |