Context-Aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs
Authors: Chang Lu, Tian Han, Yue Ning4567-4574
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two real-world EHR datasets show that the proposed model outperforms state of the art in predicting health events. |
| Researcher Affiliation | Academia | Chang Lu, Tian Han, Yue Ning Stevens Institute of Technology {clu13, tian.han, yue.ning}@stevens.edu |
| Pseudocode | No | The paper describes the model and its components using equations and descriptive text, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of Chet can be found at https://github.com/Lu Chang-CS/Chet/. |
| Open Datasets | Yes | We use MIMIC-III (Johnson et al. 2016) and MIMIC-IV (Johnson et al. 2021) to validate the predictive power of Chet. |
| Dataset Splits | Yes | We further randomly split the two datasets based on patients into training/validation/test sets, which contain 6,000/ 493/1,000 patients for MIMIC-III and 8,000/1,000/1,000 for MIMIC-IV, respectively. |
| Hardware Specification | Yes | All programs are implemented using Python 3.8.6 and Py Torch 1.7.1 with CUDA 11.1 on a machine with Intel i9-9900K CPU, 64GB memory, and Geforce RTX 2080 Ti GPU. |
| Software Dependencies | Yes | All programs are implemented using Python 3.8.6 and Py Torch 1.7.1 with CUDA 11.1 |
| Experiment Setup | Yes | The hyper-parameters as well as activation functions are tuned on the validation set. Specifically, we set the threshold δ as 0.01. The embedding size s for M, N is 48, s for R is 32. The attention size a is also 32. The hidden units p of M-GRU and GRU are 256 on MIMICIII and 350 on MIMIC-IV for the diagnosis prediction task. For the heart failure prediction task, p is 100 on MIMICIII and 150 on MIMIC-IV. When training Chet, we use 200 epochs and the Adam (Kingma and Ba 2015) optimizer. The learning rate is set as 0.01. |