mirror of
https://github.com/NicolasBohn/NexQuant.git
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Suhan Cui
129 lines
4.1 KiB
ReStructuredText
129 lines
4.1 KiB
ReStructuredText
.. _model_agent_med:
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=======================
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Medical Model Agent
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=======================
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**🤖 Automated Medical Predtion Model Evolution**
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------------------------------------------------------------------------------------------
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📖 Background
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~~~~~~~~~~~~~~
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In this scenario, we consider the problem of risk prediction from patients' ICU monitoring data. We use the a public EHR dataset - MIMIC-III and extract a binary classification task for evaluating the framework.
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In this task, we aim at predicting the whether the patients will suffer from Acute Respiratory Failure (ARF) based their first 12 hours ICU monitoring data.
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🎥 `Demo <https://rdagent.azurewebsites.net/dmm>`_
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. raw:: html
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<div style="display: flex; justify-content: center; align-items: center;">
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<video width="600" controls>
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<source src="https://rdagent.azurewebsites.net/media/65bb598f1372c1857ccbf09b2acf5d55830911625048c03102291098.mp4" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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</div>
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🌟 Introduction
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~~~~~~~~~~~~~~~~
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In this scenario, our automated system proposes hypothesis, constructs model, implements code, receives back-testing, and uses feedbacks.
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Hypothesis is iterated in this continuous process.
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The system aims to automatically optimise performance metrics of medical prediction thereby finding the optimised code through autonomous research and development.
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Here's an enhanced outline of the steps:
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**Step 1 : Hypothesis Generation 🔍**
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- Generate and propose initial hypotheses based on previous experiment analysis and domain expertise, with thorough reasoning and justification.
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**Step 2 : Model Creation ✨**
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- Transform the hypothesis into a model.
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- Develop, define, and implement a machine learning model, including its name, description, and formulation.
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**Step 3 : Model Implementation 👨💻**
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- Implement the model code based on the detailed description.
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- Evolve the model iteratively as a developer would, ensuring accuracy and efficiency.
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**Step 4 : Backtesting with MIMIC-III 📉**
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- Conduct backtesting using the newly developed model on the extracted task from MIMIC-III.
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- Evaluate the model's effectiveness and performance in terms of AUROC score.
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**Step 5 : Feedback Analysis 🔍**
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- Analyze backtest results to assess performance.
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- Incorporate feedback to refine hypotheses and improve the model.
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**Step 6 :Hypothesis Refinement ♻️**
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- Refine hypotheses based on feedback from backtesting.
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- Repeat the process to continuously improve the model.
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⚡ Quick Start
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~~~~~~~~~~~~~~~~~
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Please refer to the installation part in :doc:`../installation_and_configuration` to prepare your system dependency.
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You can try our demo by running the following command:
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- 🐍 Create a Conda Environment
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- Create a new conda environment with Python (3.10 and 3.11 are well tested in our CI):
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.. code-block:: sh
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conda create -n rdagent python=3.10
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- Activate the environment:
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.. code-block:: sh
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conda activate rdagent
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- 📦 Install the RDAgent
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- You can install the RDAgent package from PyPI:
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.. code-block:: sh
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pip install rdagent
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- 📦 Request PhysioNet Account
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- Apply for an account at `PhysioNet <https://physionet.org/>`_.
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- Request access to FIDDLE preprocessed data: `FIDDLE Dataset <https://physionet.org/content/mimic-eicu-fiddle-feature/1.0.0/>`_.
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- Place your username and password in `.env`.
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.. code-block:: bash
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cat << EOF >> .env
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DM_USERNAME=<your_username>
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DM_PASSWORD=<your_password>
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EOF
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- 🚀 Run the Application
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- You can directly run the application by using the following command:
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.. code-block:: sh
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rdagent med_model
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🛠️ Usage of modules
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~~~~~~~~~~~~~~~~~~~~~
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.. _Env Config:
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- **Env Config**
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The following environment variables can be set in the `.env` file to customize the application's behavior:
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.. autopydantic_settings:: rdagent.app.data_mining.conf.PropSetting
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:settings-show-field-summary: False
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:exclude-members: Config
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