Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Dynamic Measurement Scheduling for Event Forecasting using Deep RL
Authors: Chun-Hao Chang, Mingjie Mai, Anna Goldenberg
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,405 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,448 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our simulation, our policy outperforms heuristic-based scheduling with higher predictive gain and lower cost. In a real-world ICU mortality prediction task (MIMIC3), our policies reduce the total number of measurements by 31% or improve predictive gain by a factor of 3 as compared to physicians, under the off-policy policy evaluation. |
| Researcher Affiliation | Academia | 1University of Toronto, Toronto, ON, Canada 2Vector Institute, Toronto, ON, Canada 3The Hospital for Sick Children, Toronto, ON, Canada. |
| Pseudocode | Yes | Algorithm 1 Running policy, Algorithm 2 Generate experience for a patient at time t, Algorithm 3 Training sequential DQN |
| Open Source Code | Yes | Our data preprocessing and code are available online at https://github.com/zzzace2000/autodiagnosis. |
| Open Datasets | Yes | We then test it on MIMIC3, a real ICU temporal dataset. Johnson, A. E., Pollard, T. J., Shen, L., Li-wei, H. L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., and Mark, R. G. Mimic-iii, a freely accessible critical care database. Scientific data, 3:160035, 2016. |
| Dataset Splits | No | The paper states 'select the best performing policies based on validation set', confirming the use of a validation set. However, it does not provide specific details on the dataset splits (e.g., exact percentages or sample counts for training, validation, and test sets) for either the simulated data or MIMIC3 dataset. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper discusses various models and techniques like 'multi-layer LSTM classifier' and 'dueling deep Q-learning network (DQN)', but it does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | We list all the hyperparameters in appendix B. All the training hyperparameters are listed in Table 4. |