The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    DataFilesNotFoundError
Message:      No (supported) data files found in masonhargrave/epicare
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 72, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1904, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1885, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1270, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 597, in infer_module_for_data_files
                  raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
              datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in masonhargrave/epicare

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EpiCare Dataset README

Overview

This repository contains a dataset of 64 .hdf5 files following the D4RL format standard, designed for use in testing offline reinforcement learning (RL) and off-policy evaluation (OPE) methods. The dataset is split into train and test files for two behavior policies (SMART and SoC) on the EpiCare environment.

The EpiCare environment, described in detail in our project repository, is a benchmark designed to mimic the challenges associated with applying RL to longitudinal healthcare settings. This dataset is useful for evaluating the performance of RL models in these complex scenarios.

Directory Structure

The dataset is organized into the following directory structure:

smart/
β”œβ”€β”€ test_seed_1.hdf5
β”œβ”€β”€ test_seed_2.hdf5
β”œβ”€β”€ test_seed_3.hdf5
β”œβ”€β”€ test_seed_4.hdf5
β”œβ”€β”€ test_seed_5.hdf5
β”œβ”€β”€ test_seed_6.hdf5
β”œβ”€β”€ test_seed_7.hdf5
β”œβ”€β”€ test_seed_8.hdf5
β”œβ”€β”€ train_seed_1.hdf5
β”œβ”€β”€ train_seed_2.hdf5
β”œβ”€β”€ train_seed_3.hdf5
β”œβ”€β”€ train_seed_4.hdf5
β”œβ”€β”€ train_seed_5.hdf5
β”œβ”€β”€ train_seed_6.hdf5
β”œβ”€β”€ train_seed_7.hdf5
β”œβ”€β”€ train_seed_8.hdf5
soc/
β”œβ”€β”€ test_seed_1.hdf5
β”œβ”€β”€ test_seed_2.hdf5
β”œβ”€β”€ test_seed_3.hdf5
β”œβ”€β”€ test_seed_4.hdf5
β”œβ”€β”€ test_seed_5.hdf5
β”œβ”€β”€ test_seed_6.hdf5
β”œβ”€β”€ test_seed_7.hdf5
β”œβ”€β”€ test_seed_8.hdf5
β”œβ”€β”€ train_seed_1.hdf5
β”œβ”€β”€ train_seed_2.hdf5
β”œβ”€β”€ train_seed_3.hdf5
β”œβ”€β”€ train_seed_4.hdf5
β”œβ”€β”€ train_seed_5.hdf5
β”œβ”€β”€ train_seed_6.hdf5
β”œβ”€β”€ train_seed_7.hdf5
β”œβ”€β”€ train_seed_8.hdf5

Behavior Policies

SMART Policy

The Sequential Multiple Assignment Randomized Trial (SMART) policy models treatment selection for a simulated clinical trial. This policy adheres to a weighted random selection process where each treatment's likelihood of selection is based on its expected reward. The SMART policy is widely used in clinical trials to balance exploration and exploitation, providing synthetic clinical trial data for training RL models.

SoC Policy

The Standard of Care (SoC) policy aims to simulate the performance of a hypothetical clinician following best practices without access to latent disease states. This policy models a conservative approach to treatment selection, avoiding actions that would exacerbate any current symptoms beyond a safe threshold. The SoC policy serves as a baseline to compare the performance and safety of RL algorithms.

Seeds and Environment Generation

The seed number in each file name refers to the random seed used to generate the EpiCare environment. Each seed creates a distinct EpiCare environment, which can be thought of as representing a completely different disease population. This variability allows researchers to evaluate the generalizability of their models across diverse simulated patient cohorts.

Dataset Details

Each .hdf5 file contains 131,072 episodes of data, effectively representing that many patients. Each episode consists of a maximum of 8 time steps.

Usage

This dataset can be used to test and benchmark offline RL and OPE methods. The .hdf5 files are compatible with the D4RL format, making them easy to integrate with existing RL frameworks and libraries. Researchers can use this dataset to train and evaluate their models, ensuring reproducibility and comparability of results.

Reference

For more details on the EpiCare environment and the design considerations behind this dataset, please refer to the accompanying paper and visit our project repository.

Contact

For questions or support, please contact:

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