| Name | ConMedRL JSON |
| Version |
1.0.6
JSON |
| download |
| home_page | https://github.com/smt970913/ConMED-RL |
| Summary | An Offline Constrained Reinforcement Learning Toolkit for Medical Decision Making |
| upload_time | 2025-10-09 13:52:54 |
| maintainer | None |
| docs_url | None |
| author | Maotong Sun, Jingui Xie |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 Maotong Sun, Jingui Xie
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
reinforcement learning
constrained reinforcement learning
offline reinforcement learning
clinical decision support
healthcare
icu
critical care
machine learning
artificial intelligence
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
flask
scikit-learn
torch
numpy
pandas
tqdm
celery
gunicorn
pillow
requests
dask
threadpoolctl
joblib
jupyter
jupyterlab
notebook
matplotlib
seaborn
plotly
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# ConMED-RL: An OCRL-Based Toolkit for Medical Decision Support
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://badge.fury.io/py/conmedrl)
**ConMED-RL** is an **Offline Constrained Reinforcement Learning (OCRL)** toolkit designed for critical care decision support. The toolkit provides an OCRL-based policy learning framework for medical decision-making tasks under single or multiple constraints.
This toolkit builds upon our research on OCRL applications in critical care: a published study in *IISE Transactions on Healthcare Systems Engineering* addressing ICU discharge decision-making, and ongoing work under revision in *Health Care Management Science* on ICU extubation decision-making.
## 🚀 Quick Start
### Installation
Install ConMED-RL using pip:
```bash
pip install conmedrl
```
### Brief Usage Instruction
- Set Hyparameter
```python
from ConMedRL import RLConfigurator, RLConfig_custom
dm_configuration = RLConfigurator()
dm_configuration.choose_config_method()
dm_configuration.config.memory_capacity
```
- Load data into the training procedure (discharge decision-making case)
```python
from ConMedRL import FQE, FQI, TrainDataLoader, ValTestDataLoader
# Load your clinical data
train_data_loader = TrainDataLoader(cfg = dm_configuration.config,
outcome_table = outcome_table_train,
state_var_table = state_var_table,
terminal_state = terminal_state)
train_data_loader.data_buffer_train(action_name = 'discharge_action',
done_condition = True,
num_constraint = 2)
val_data_loader = ValTestDataLoader(cfg = dm_configuration.config,
outcome_table_select = outcome_table_val_select,
state_var_table_select = state_var_table_val_select,
outcome_table = outcome_table_val,
state_var_table = state_var_table_val,
terminal_state = terminal_state)
val_data_loader.data_buffer(action_name = 'discharge_action',
done_condition = True,
num_constraint = 2)
```
- Initialize the training of OCRL-based policy learning framework (discharge decision-making case)
```python
# Set the training of the model
ocrl_training = RLTraining(cfg = dm_configuration.config,
state_dim = state_var_table.shape[1],
action_dim = 2,
train_data_loader = train_data_loader.data_torch_loader_train,
val_data_loader = val_data_loader.data_torch_loader)
# Building the FQI agent
fqi_agent = ocrl_training.fqi_agent_config(...)
# Building the FQE agents
fqe_agent_obj = ocrl_training.fqe_agent_config(...)
fqe_agent_con_0 = ocrl_training.fqe_agent_config(...)
fqe_agent_con_1 = ocrl_training.fqe_agent_config(...)
ocrl_training.train(agent_fqi = fqi_agent,
agent_fqe_obj = fqe_agent_obj,
agent_fqe_con_list = [fqe_agent_con_0, fqe_agent_con_1],
constraint = True,
save_num = 100,
z_value = 1.96)
```
## 📦 Core Components
### Offline Constrained Reinforcement Learning (OCRL) Algorithms
- **Fitted Q-Evaluation (FQE)**: Policy evaluation method for offline data
- **Fitted Q-Iteration (FQI)**: Value-based offline RL algorithm
- **Replay Buffer**: Efficient data management for training
- **Custom RL Configurator**: Flexible configuration for different clinical scenarios
### Data Processing
- **TrainDataLoader**: Handles training data preparation and batch generation
- **ValTestDataLoader**: Manages validation and testing data processing
- Support for custom done conditions and constraint cost functions
## 🏥 Key Features
- **Offline Learning**: Train models on historical clinical data without online interaction
- **Constraint Handling**: Built-in support for clinical safety/efficiency constraints
- **Flexible Architecture**: Easy integration with existing clinical datasets
- **Medical Focus**: Specifically designed for critical care decision-making scenarios
- **Research-Backed**: Based on peer-reviewed methodologies
## 📊 Use Cases
ConMED-RL has been successfully applied to:
- **ICU Discharge Decision-Making**: Optimizing timing and safety of patient discharge
- **ICU Mechanical Ventilation Weaning**: Supporting extubation decisions with constraint satisfaction
- **Multi-Constraint Clinical Decisions**: Balancing multiple clinical objectives requirements
## 🔧 Hyperparameter Configuration
```python
from ConMedRL import RLConfig_custom
custom_config = RLConfig_custom(
algo_name = 'OCRL policy learning for ...'
gamma = 0.99,
batch_size = 256,
train_eps = int(8e6),
...
)
# Use custom configuration in training
trainer = RLTraining(
config = custom_config,
...
)
```
## Data Preprocessing
ConMED-RL expects data in MDP format suitable for offline RL training:
- **State Table**: Physiological measurements and clinical variables
- **Outcome Table**: Actions, costs/rewards, and terminal indicators
See the [full documentation](https://github.com/smt970913/ConMED-RL) for data preprocessing examples.
## 📖 Documentation and Examples
For comprehensive guides, tutorials, and examples:
- **GitHub Repository**: [https://github.com/smt970913/ConMED-RL](https://github.com/smt970913/ConMED-RL)
- **Example Notebooks**: Interactive Jupyter notebooks for MIMIC-IV datasets
- **Web Application Demo**: Clinical decision support interface
## 🔬 Research and Citation
This toolkit is based on research published in academic journals. If you use ConMED-RL in your research, please cite:
```bibtex
@misc{sun2025comedRL,
author = {Maotong Sun and Jingui Xie},
title = {ConMED-RL: An OCRL-Based Toolkit for Medical Decision Support},
year = {2025},
howpublished = {\url{https://github.com/smt970913/ConMED-RL}},
note = {Accessed: xxxx-xx-xx},
}
```
## 🛠️ Requirements
- Python 3.8 or higher
- PyTorch
- NumPy
- Pandas
- scikit-learn
All dependencies are automatically installed with the package.
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](https://github.com/smt970913/ConMED-RL/blob/main/LICENSE) file for details.
## 👥 Authors and Contact
- **Maotong Sun** - maotong.sun@tum.de
- **Jingui Xie** - jingui.xie@tum.de
School of Management, Technical University of Munich
## 🤝 Contributing
We welcome contributions! For major changes, please open an issue first to discuss what you would like to change.
For development setup and contributing guidelines, visit the [GitHub repository](https://github.com/smt970913/ConMED-RL).
## 🔗 Links
- **PyPI**: [https://pypi.org/project/conmedrl/](https://pypi.org/project/conmedrl/)
- **GitHub**: [https://github.com/smt970913/ConMED-RL](https://github.com/smt970913/ConMED-RL)
- **Issues**: [https://github.com/smt970913/ConMED-RL/issues](https://github.com/smt970913/ConMED-RL/issues)
- **Documentation**: [https://github.com/smt970913/ConMED-RL#readme](https://github.com/smt970913/ConMED-RL#readme)
## ⚠️ Disclaimer
This toolkit is intended for research purposes. Clinical deployment requires appropriate validation, regulatory approval, and should only be used by qualified healthcare professionals in accordance with institutional guidelines and applicable regulations.
---
**Keywords**: reinforcement learning, constrained reinforcement learning, offline reinforcement learning, clinical decision support, healthcare, ICU, critical care, machine learning, artificial intelligence
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"description": "# ConMED-RL: An OCRL-Based Toolkit for Medical Decision Support\r\n\r\n[](https://opensource.org/licenses/MIT)\r\n[](https://www.python.org/downloads/)\r\n[](https://badge.fury.io/py/conmedrl)\r\n\r\n**ConMED-RL** is an **Offline Constrained Reinforcement Learning (OCRL)** toolkit designed for critical care decision support. The toolkit provides an OCRL-based policy learning framework for medical decision-making tasks under single or multiple constraints.\r\n\r\nThis toolkit builds upon our research on OCRL applications in critical care: a published study in *IISE Transactions on Healthcare Systems Engineering* addressing ICU discharge decision-making, and ongoing work under revision in *Health Care Management Science* on ICU extubation decision-making.\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Installation\r\n\r\nInstall ConMED-RL using pip:\r\n\r\n```bash\r\npip install conmedrl\r\n```\r\n\r\n### Brief Usage Instruction\r\n- Set Hyparameter\r\n```python\r\nfrom ConMedRL import RLConfigurator, RLConfig_custom \r\n\r\ndm_configuration = RLConfigurator()\r\ndm_configuration.choose_config_method()\r\ndm_configuration.config.memory_capacity\r\n```\r\n\r\n- Load data into the training procedure (discharge decision-making case)\r\n\r\n```python\r\nfrom ConMedRL import FQE, FQI, TrainDataLoader, ValTestDataLoader\r\n\r\n# Load your clinical data\r\ntrain_data_loader = TrainDataLoader(cfg = dm_configuration.config, \r\n outcome_table = outcome_table_train, \r\n state_var_table = state_var_table, \r\n terminal_state = terminal_state)\r\n\r\ntrain_data_loader.data_buffer_train(action_name = 'discharge_action', \r\n done_condition = True, \r\n num_constraint = 2)\r\n\r\nval_data_loader = ValTestDataLoader(cfg = dm_configuration.config, \r\n outcome_table_select = outcome_table_val_select, \r\n state_var_table_select = state_var_table_val_select, \r\n outcome_table = outcome_table_val, \r\n state_var_table = state_var_table_val, \r\n terminal_state = terminal_state)\r\n\r\nval_data_loader.data_buffer(action_name = 'discharge_action', \r\n done_condition = True, \r\n num_constraint = 2)\r\n```\r\n\r\n- Initialize the training of OCRL-based policy learning framework (discharge decision-making case)\r\n```python\r\n# Set the training of the model\r\nocrl_training = RLTraining(cfg = dm_configuration.config, \r\n state_dim = state_var_table.shape[1], \r\n action_dim = 2, \r\n train_data_loader = train_data_loader.data_torch_loader_train,\r\n val_data_loader = val_data_loader.data_torch_loader)\r\n\r\n# Building the FQI agent\r\nfqi_agent = ocrl_training.fqi_agent_config(...) \r\n\r\n# Building the FQE agents\r\nfqe_agent_obj = ocrl_training.fqe_agent_config(...) \r\n\r\nfqe_agent_con_0 = ocrl_training.fqe_agent_config(...) \r\n\r\nfqe_agent_con_1 = ocrl_training.fqe_agent_config(...) \r\n\r\nocrl_training.train(agent_fqi = fqi_agent, \r\n agent_fqe_obj = fqe_agent_obj, \r\n agent_fqe_con_list = [fqe_agent_con_0, fqe_agent_con_1], \r\n constraint = True,\r\n save_num = 100,\r\n z_value = 1.96)\r\n```\r\n\r\n## \ud83d\udce6 Core Components\r\n\r\n### Offline Constrained Reinforcement Learning (OCRL) Algorithms\r\n\r\n- **Fitted Q-Evaluation (FQE)**: Policy evaluation method for offline data\r\n- **Fitted Q-Iteration (FQI)**: Value-based offline RL algorithm \r\n- **Replay Buffer**: Efficient data management for training\r\n- **Custom RL Configurator**: Flexible configuration for different clinical scenarios\r\n\r\n### Data Processing\r\n\r\n- **TrainDataLoader**: Handles training data preparation and batch generation\r\n- **ValTestDataLoader**: Manages validation and testing data processing\r\n- Support for custom done conditions and constraint cost functions\r\n\r\n## \ud83c\udfe5 Key Features\r\n\r\n- **Offline Learning**: Train models on historical clinical data without online interaction\r\n- **Constraint Handling**: Built-in support for clinical safety/efficiency constraints\r\n- **Flexible Architecture**: Easy integration with existing clinical datasets\r\n- **Medical Focus**: Specifically designed for critical care decision-making scenarios\r\n- **Research-Backed**: Based on peer-reviewed methodologies\r\n\r\n## \ud83d\udcca Use Cases\r\n\r\nConMED-RL has been successfully applied to:\r\n\r\n- **ICU Discharge Decision-Making**: Optimizing timing and safety of patient discharge\r\n- **ICU Mechanical Ventilation Weaning**: Supporting extubation decisions with constraint satisfaction\r\n- **Multi-Constraint Clinical Decisions**: Balancing multiple clinical objectives requirements\r\n\r\n## \ud83d\udd27 Hyperparameter Configuration\r\n\r\n```python\r\nfrom ConMedRL import RLConfig_custom\r\n\r\ncustom_config = RLConfig_custom(\r\n algo_name = 'OCRL policy learning for ...'\r\n gamma = 0.99,\r\n batch_size = 256,\r\n train_eps = int(8e6),\r\n ...\r\n)\r\n\r\n# Use custom configuration in training\r\ntrainer = RLTraining(\r\n config = custom_config, \r\n ...\r\n)\r\n```\r\n\r\n## Data Preprocessing\r\n\r\nConMED-RL expects data in MDP format suitable for offline RL training:\r\n- **State Table**: Physiological measurements and clinical variables\r\n- **Outcome Table**: Actions, costs/rewards, and terminal indicators\r\n\r\nSee the [full documentation](https://github.com/smt970913/ConMED-RL) for data preprocessing examples.\r\n\r\n## \ud83d\udcd6 Documentation and Examples\r\n\r\nFor comprehensive guides, tutorials, and examples:\r\n\r\n- **GitHub Repository**: [https://github.com/smt970913/ConMED-RL](https://github.com/smt970913/ConMED-RL)\r\n- **Example Notebooks**: Interactive Jupyter notebooks for MIMIC-IV datasets\r\n- **Web Application Demo**: Clinical decision support interface\r\n\r\n## \ud83d\udd2c Research and Citation\r\n\r\nThis toolkit is based on research published in academic journals. If you use ConMED-RL in your research, please cite:\r\n\r\n```bibtex\r\n@misc{sun2025comedRL,\r\n author = {Maotong Sun and Jingui Xie},\r\n title = {ConMED-RL: An OCRL-Based Toolkit for Medical Decision Support},\r\n year = {2025},\r\n howpublished = {\\url{https://github.com/smt970913/ConMED-RL}},\r\n note = {Accessed: xxxx-xx-xx},\r\n}\r\n```\r\n\r\n## \ud83d\udee0\ufe0f Requirements\r\n\r\n- Python 3.8 or higher\r\n- PyTorch\r\n- NumPy\r\n- Pandas\r\n- scikit-learn\r\n\r\nAll dependencies are automatically installed with the package.\r\n\r\n## \ud83d\udcdd License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](https://github.com/smt970913/ConMED-RL/blob/main/LICENSE) file for details.\r\n\r\n## \ud83d\udc65 Authors and Contact\r\n\r\n- **Maotong Sun** - maotong.sun@tum.de\r\n- **Jingui Xie** - jingui.xie@tum.de\r\n\r\nSchool of Management, Technical University of Munich\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nWe welcome contributions! For major changes, please open an issue first to discuss what you would like to change.\r\n\r\nFor development setup and contributing guidelines, visit the [GitHub repository](https://github.com/smt970913/ConMED-RL).\r\n\r\n## \ud83d\udd17 Links\r\n\r\n- **PyPI**: [https://pypi.org/project/conmedrl/](https://pypi.org/project/conmedrl/)\r\n- **GitHub**: [https://github.com/smt970913/ConMED-RL](https://github.com/smt970913/ConMED-RL)\r\n- **Issues**: [https://github.com/smt970913/ConMED-RL/issues](https://github.com/smt970913/ConMED-RL/issues)\r\n- **Documentation**: [https://github.com/smt970913/ConMED-RL#readme](https://github.com/smt970913/ConMED-RL#readme)\r\n\r\n## \u26a0\ufe0f Disclaimer\r\n\r\nThis toolkit is intended for research purposes. Clinical deployment requires appropriate validation, regulatory approval, and should only be used by qualified healthcare professionals in accordance with institutional guidelines and applicable regulations.\r\n\r\n---\r\n\r\n**Keywords**: reinforcement learning, constrained reinforcement learning, offline reinforcement learning, clinical decision support, healthcare, ICU, critical care, machine learning, artificial intelligence\r\n",
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