Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers
Authors: Jingge Xiao, Leonie Basso, Wolfgang Nejdl, Niloy Ganguly, Sandipan Sikdar
AAAI 2024 | Venue PDF | 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. |