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 [1].
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
Authors: Zhaozhi Qian, William Zame, Lucas Fleuren, Paul Elbers, Mihaela van der Schaar
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated LHM on synthetic data as well as real-world intensive care data of COVID-19 patients. LHM consistently outperforms previous works, especially when few training samples are available such as at the beginning of the pandemic. |
| Researcher Affiliation | Collaboration | Zhaozhi Qian University of Cambridge EMAIL William R. Zame UCLA EMAIL Lucas M. Fleuren Amsterdam UMC EMAIL Paul Elbers Amsterdam UMC EMAIL Mihaela van der Schaar University of Cambridge UCLA The Alan Turing Institute EMAIL |
| Pseudocode | No | The paper includes 'Figure 2: Illustration of the training and prediction procedure' which shows a diagram of steps, but it does not provide formal pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | The implementation of LHM and the experiment code are available at https://github.com/Zhaozhi QIAN/Hybrid-ODE-Neur IPS-2021 or https://github.com/ orgs/vanderschaarlab/repositories |
| Open Datasets | Yes | We used data from the Dutch Data Warehouse (DDW), a multicenter and full-admission anonymized electronic health records database of critically ill COVID-19 patients [27]. |
| Dataset Splits | Yes | We partition each dataset into a training set, a validation set, and a testing set. We consider training sets consisting of N0 = 10, 100, or 500 data points; each validation set has 100 data points and each testing set has 1000 data points. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU models, CPU types, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions the use of an 'Adam' optimizer, but it does not specify version numbers for any software dependencies such as programming languages, deep learning frameworks (e.g., PyTorch, TensorFlow), or specific libraries. |
| Experiment Setup | No | The paper states, 'The details of the optimization and hyper-parameter settings are reported in Appendix A.4.' However, Appendix A.4 is not included in the provided text of the research paper, so specific experimental setup details like hyperparameter values are not available. |