IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers
Authors: Jingge Xiao, Leonie Basso, Wolfgang Nejdl, Niloy Ganguly, Sandipan Sikdar
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-world datasets show that the proposed method can systematically outperform its predecessors, achieve state-of-the-art results, and have significant advantages in terms of data efficiency. |
| Researcher Affiliation | Academia | 1L3S Research Center, Leibniz Universit at Hannover 2Indian Institute of Technology Kharagpur |
| Pseudocode | Yes | Algorithm 1: IVP-VAE. The same IVP solver works for both encoder and decoder by solving IVPs in opposite directions. |
| Open Source Code | Yes | Source code is at https://github.com/jingge326/ivpvae. |
| Open Datasets | Yes | We evaluate our model on three real-world public EHR datasets from the Physio Net platform (Goldberger et al. 2000): MIMIC-IV (Johnson et al. 2020, 2023), Physio Net 2012 (Silva et al. 2012) and e ICU (Pollard et al. 2018, 2019). |
| Dataset Splits | Yes | Each dataset is randomly split into 80% for training, 10% for validation and 10% for testing. |
| Hardware Specification | Yes | All models were tested in the same computing environment with NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper does not explicitly list key software components with specific version numbers for reproducibility (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | Hyperparameter settings are described in Appendix A.2. |