Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Medical Concept Representation Learning from Multi-source Data
Authors: Tian Bai, Brian L. Egleston, Richard Bleicher, Slobodan Vucetic
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,609 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,776 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 suf๏ฌcient for the vector representation to stabilize. The number of negative samples was set to 5. |