GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs
Authors: Pengcheng Jiang, Cao Xiao, Adam Richard Cross, Jimeng Sun
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On two public datasets, MIMIC-III and MIMIC-IV, GRAPHCARE surpasses baselines in four vital healthcare prediction tasks: mortality, readmission, length of stay (LOS), and drug recommendation. We evaluated the effectiveness of GRAPHCARE using two widely-used EHR datasets, MIMIC-III (Johnson et al., 2016) and MIMIC-IV (Johnson et al., 2020). Through extensive experimentation, we found that GRAPHCARE outperforms several baselines... |
| Researcher Affiliation | Collaboration | Pengcheng Jiang Cao Xiao Adam Cross Jimeng Sun University of Illinois Urbana-Champaign GE Health Care OSF Health Care |
| Pseudocode | Yes | Algorithm 1 Subgraph Sampling |
| Open Source Code | Yes | Our code is available at https://github.com/pat-jj/Graph Care. |
| Open Datasets | Yes | For the EHR data, we use the publicly available MIMIC-III (Johnson et al., 2016) and MIMIC-IV (Johnson et al., 2020) datasets. Both datasets are under Physio Net Credentialed Health Data License 1.5.0 |
| Dataset Splits | Yes | We split the dataset by 8:1:1 for training/validation/testing data, and we use Adam (Kingma & Ba, 2014) as the optimizer. |
| Hardware Specification | Yes | Hardware. All experiments are conducted on a machine equipped with two AMD EPYC 7513 32-Core Processors, 528GB RAM, eight NVIDIA RTX A6000 GPUs, and CUDA 11.7. |
| Software Dependencies | Yes | We implement GRAPHCARE using Python 3.8.13, Py Torch 1.12.0 (Paszke et al., 2019), and Py Torch Geometric 2.3.0 (Fey & Lenssen, 2019). |
| Experiment Setup | Yes | Based on our hyperparameter study in Appendix G.2, we set learning rate 1e-5, weight decay 1e-5, batch size 4, and hidden dimension 128. All models are trained via 50 epochs over all patient samples, and the early stopping strategy monitored by AUROC with 10 epochs is applied. |