VITA: ‘Carefully Chosen and Weighted Less’ Is Better in Medication Recommendation
Authors: Taeri Kim, Jiho Heo, Hongil Kim, Kijung Shin, Sang-Wook Kim
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments using real-world datasets, we demonstrate the superiority of VITA (spec., up to 5.67% higher accuracy, in terms of Jaccard, than the best competitor) and the effectiveness of its two core ideas. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Hanyang University, South Korea 2Department of Artificial Intelligence, Hanyang University, South Korea 3Kim Jaechul Graduate School of AI & School of Electrical Engineering, KAIST, South Korea |
| Pseudocode | No | The paper describes its methods and components using textual explanations and mathematical equations, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | The code is available at https://github.com/jhheo0123/VITA. |
| Open Datasets | Yes | We used the MIMIC-III dataset (Johnson et al. 2016), widely used in medication recommendation studies (Shang et al. 2019b; Yang et al. 2021a,b; Wu et al. 2022b), and the MIMIC-IV dataset (Johnson et al. 2023), which is a follow-up dataset to MIMIC-III dataset. |
| Dataset Splits | Yes | We randomly split the patients in each dataset into training (4/6), validation (1/6), and test (1/6) sets as in (Shang et al. 2019b; Yang et al. 2021b; Wu et al. 2022b). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU specifications, memory). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, TensorFlow/PyTorch versions, or other libraries). |
| Experiment Setup | Yes | We randomly split the patients in each dataset into training (4/6), validation (1/6), and test (1/6) sets as in (Shang et al. 2019b; Yang et al. 2021b; Wu et al. 2022b). |