pyhealth - Healthcare AI Toolkit for Clinical Machine Learning
Develop, test, and deploy healthcare machine learning models using clinical data, medical codes, physiological signals, and healthcare datasets.
Tags
Updated: 2026-10-04Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Load healthcare datasets
- Define clinical prediction tasks
- Select healthcare models
- Preprocess clinical data
- Translate medical codes
- Train machine learning models
- Evaluate clinical models
- Calibrate model predictions
- Interpret model predictions
- Assess model fairness
- Quantify prediction uncertainty
- Validate clinical solutions
Inputs
- Healthcare datasets
- Clinical prediction tasks
- Medical coding systems
- Physiological signals
- Clinical text
- Medical images
- Model configurations
- Training parameters
Outputs
- Trained models
- Model checkpoints
- Prediction results
- Evaluation metrics
- Calibrated predictions
- Fairness assessments
- Interpretability results
- Uncertainty estimates
Requirements
- Python 3.7 or later
- PyTorch 1.8 or later
- NumPy
- pandas
- scikit-learn