Pre-training of Graph Augmented Transformers for Medication Recommendation
Authors: Junyuan Shang, Tengfei Ma, Cao Xiao, Jimeng Sun
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We used EHR data from MIMIC-III [Johnson et al., 2016] and conducted all our experiments on a cohort where patients have more than one visit. ... Table. 3 compares the performance on the medication recommendation task. |
| Researcher Affiliation | Collaboration | Junyuan Shang1,3 , Tengfei Ma2 , Cao Xiao1 and Jimeng Sun3 1Analytics Center of Excellence, IQVIA, Cambridge, MA, USA 2IBM Research AI, Yorktown Heights, NY, USA 3Georgia Institute of Technology, Atlanta, GA, USA |
| Pseudocode | No | The paper describes the method using mathematical equations and descriptive text, but it does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | 1https://github.com/jshang123/G-Bert |
| Open Datasets | Yes | We used EHR data from MIMIC-III [Johnson et al., 2016] |
| Dataset Splits | Yes | We randomly divide the dataset into training, validation and testing set in a 0.6 : 0.2 : 0.2 ratio. |
| Hardware Specification | Yes | All methods are implemented in Py Torch [Paszke et al., 2017] and trained on an Ubuntu 16.04 with 8GB memory and Nvidia 1080 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Ubuntu 16.04' but does not specify version numbers for PyTorch or any other key software libraries. |
| Experiment Setup | Yes | For G-BERT, the hyperparameters are adjusted on evaluation set: (1) GAT part: input embedding dimension as 75, number of attention heads as 4; (2) BERT part: hidden dimension as 300, dimension of position-wise feed-forward networks as 300, 2 hidden layers with 4 attention heads for each layer. ... Training is done through Adam [Kingma and Ba, 2014] at learning rate 5e-4. |