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 [1].
Context-Aware Safe Medication Recommendations with Molecular Graph and DDI Graph Embedding
Authors: Qianyu Chen, Xin Li, Kunnan Geng, Mingzhong Wang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted extensive experiments for performance comparison between Carmen and several state-of-the-art methods. To further verify the reliability and effectiveness of our model, two analytical studies are also provided. |
| Researcher Affiliation | Academia | Qianyu Chen1, Xin Li *1, Kunnan Geng1, Mingzhong Wang2 1 Beijing Institute of Technology 2 University of the Sunshine Coast EMAIL, EMAIL |
| Pseudocode | No | The paper includes mathematical definitions and equations but no dedicated pseudocode block or algorithm description. |
| Open Source Code | Yes | 1Code is available at https://github.com/bit1029public/Carmen. |
| Open Datasets | Yes | Datasets. We evaluated our model on MIMIC-III [Johnson et al. 2016] and MIMC-IV [Johnson et al. 2018]. |
| Dataset Splits | No | The paper mentions a 'training set' and 'test dataset' but does not explicitly provide details about a separate validation set split (percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For fairness, we set 2 layers for message passing and 9 for message dispelling. |