pyhealth - Healthcare AI toolkit for clinical machine learning
Develop, test, evaluate, and deploy healthcare machine learning models with clinical data, coding systems, signals, images, and text.
Tags
Updated: 2026-10-03Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Load healthcare datasets
- Define clinical prediction tasks
- Select healthcare models
- Preprocess clinical data
- Train machine learning models
- Evaluate clinical models
- Translate medical codes
- Calibrate model predictions
- Interpret model predictions
- Assess model fairness
Inputs
- Healthcare datasets
- Clinical prediction objectives
- Medical coding systems
- Physiological signals
- Clinical text
- Medical images
- Model configurations
- Task-specific labels
Outputs
- Trained machine learning models
- Clinical predictions
- Evaluation metrics
- Calibration results
- Fairness assessments
- Interpretable predictions
- Uncertainty estimates
Requirements
- Python 3.7 or later
- PyTorch 1.8 or later
- NumPy
- pandas
- scikit-learn