Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
Authors: Jannik Deuschel, Caleb Ellington, Yingtao Luo, Ben Lengerich, Pascal Friederich, Eric P. Xing
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We assess CPR through studies on simulated and real data, achieving state-of-the-art performance on predicting antibiotic prescription in intensive care units (+22% AUROC vs. previous SOTA) and predicting MRI prescription for Alzheimer s patients (+7.7% AUROC vs. previous SOTA). |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University 2Karlsruhe Institute of Technology 3Broad Institute of MIT and Harvard 4MIT 5MBZUAI 6Petuum, Inc. |
| Pseudocode | No | The paper describes the methods conceptually and mathematically but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for data processing, model training, and figure generation is available on Git Hub 1. 1https://github.com/JADEUSC/ contextualized_policy_recovery |
| Open Datasets | Yes | We look at 4195 patients in the intensive care unit over up to 6 timesteps extracted from the Medical Information Mart for Intensive Care III (Johnson et al., 2016) dataset and predict antibiotic prescription based on 7 observations temperature, hematocrit, potassium, white blood cell count (WBC), blood pressure, heart rate, and creatinine. |
| Dataset Splits | Yes | Each dataset is split up into a training set (70% of patients), validation set (15% of patients) for hyperparameter tuning, and test set (15% of patients) to report model performance. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using “the Adam optimizer (Kingma & Ba, 2014)” but does not specify version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | The initial learning rate chosen for CPR is 5e-4 and 1e-4 for the baseline RNNs. We select the dimensions of the hidden state for both CPR and the baseline RNNs from [16,32,64]. For CPR, λ is chosen from [0.0001,0.001,0.01,0.1]. The batch size is selected as 64 for all models. Table 4 shows the optimal hyperparameters chosen based on the validation set performance. |