Medical Concept Representation Learning from Multi-source Data
Authors: Tian Bai, Brian L. Egleston, Richard Bleicher, Slobodan Vucetic
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results indicate that vector representations of codes learned by the proposed approach provide superior crossreferencing when compared to several existing approaches. |
| Researcher Affiliation | Collaboration | 1Department of Computer and Information Sciences, Temple University, USA 2Fox Chase Cancer Center, USA |
| Pseudocode | No | The paper describes the methods using prose and mathematical equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to the source code for the methodology described. |
| Open Datasets | Yes | Medical claims used in our experiments come from SEERMedicare Linked Database [Warren et al., 2002]. |
| Dataset Splits | No | The paper states the total dataset size and details about the training iterations, but it does not provide specific information about train/validation/test splits by percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions algorithms like word2vec, Skip-gram, CBOW, and Glove, but it does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | In all the experiments we used 60 iterations, as we empirically observed that it is sufficient for the vector representation to stabilize. The number of negative samples was set to 5. |