Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Authors: Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |