PyHealth: Healthcare AI Toolkit
Overview
PyHealth is a comprehensive Python library for healthcare AI that provides specialized tools, models, and datasets for clinical machine learning. Use this skill when developing healthcare prediction models, processing clinical data, working with medical coding systems, or deploying AI solutions in healthcare settings.
When to Use This Skill
Invoke this skill when:
- Working with healthcare datasets: MIMIC-III, MIMIC-IV, eICU, OMOP, sleep EEG data, medical images
- Clinical prediction tasks: Mortality prediction, hospital readmission, length of stay, drug recommendation
- Medical coding: Translating between ICD-9/10, NDC, RxNorm, ATC coding systems
- Processing clinical data: Sequential events, physiological signals, clinical text, medical images
- Implementing healthcare models: RETAIN, SafeDrug, GAMENet, StageNet, Transformer for EHR
- Evaluating clinical models: Fairness metrics, calibration, interpretability, uncertainty quantification
Core Capabilities
PyHealth operates through a modular 5-stage pipeline optimized for healthcare AI:
- Data Loading: Access 10+ healthcare datasets with standardized interfaces
- Task Definition: Apply 20+ predefined clinical prediction tasks or create custom tasks
- Model Selection: Choose from 33+ models (baselines, deep learning, healthcare-specific)
- Training: Train with automatic checkpointing, monitoring, and evaluation
- Deployment: Calibrate, interpret, and validate for clinical use
Performance: 3x faster than pandas for healthcare data processing
Quick Start Workflow
from pyhealth.datasets import MIMIC4Dataset
from pyhealth.tasks import mortality_prediction_mimic4_fn
from pyhealth.datasets import split_by_patient, get_dataloader
from pyhealth.models import Transformer
from pyhealth.trainer import Trainer
# 1. Load dataset and set task
dataset = MIMIC4Dataset(root="/path/to/data")
sample_dataset = dataset.set_task(mortality_prediction_mimic4_fn)
# 2. Split data
train, val, test = split_by_patient(sample_dataset, [0.7, 0.1, 0.2])
# 3. Create data loaders
train_loader = get_dataloader(train, batch_size=64, shuffle=True)
val_loader = get_dataloader(val, batch_size=64, shuffle=False)
test_loader = get_dataloader(test, batch_size=64, shuffle=False)
# 4. Initialize and train model
model = Transformer(
dataset=sample_dataset,
feature_keys=["diagnoses", "medications"],
mode="binary",
embedding_dim=128
)
trainer = Trainer(model=model, device="cuda")
trainer.train(
train_dataloader=train_loader,
val_dataloader=val_loader,
epochs=50,
monitor="pr_auc_score"
)
# 5. Evaluate
results = trainer.evaluate(test_loader)
Detailed Documentation
This skill includes comprehensive reference documentation organized by functionality. Read specific reference files as needed:
1. Datasets and Data Structures
File: references/datasets.md
Read when:
- Loading healthcare datasets (MIMIC, eICU, OMOP, sleep EEG, etc.)
- Understanding Event, Patient, Visit data structures
- Processing different data types (EHR, signals, images, text)
- Splitting data for training/validation/testing
- Working with SampleDataset for task-specific formatting
Key Topics:
- Core data structures (Event, Patient, Visit)
- 10+ available datasets (EHR, physiological signals, imaging, text)
- Data loading and iteration
- Train/val/test splitting strategies
- Performance optimization for large datasets
2. Medical Coding Translation
File: references/medical_coding.md
Read when:
- Translating between medical coding systems
- Working with diagnosis codes (ICD-9-CM, ICD-10-CM, CCS)
- Processing medication codes (NDC, RxNorm, ATC)
- Standardizing procedure codes (ICD-9-PROC, ICD-10-PROC)
- Grouping codes into clinical categories
- Handling hierarchical drug classifications
Key Topics:
- InnerMap for within-system lookups
- CrossMap for cross-system translation
- Supported coding systems (ICD, NDC, ATC, CCS, RxNorm)
- Code standardization and hierarchy traversal
- Medication classification by therapeutic class
- Integration with datasets
3. Clinical Prediction Tasks
File: references/tasks.md
Read when:
- Defining clinical prediction objectives
- Using predefined tasks (mortality, readmission, drug recommendation)
- Working with EHR, signal, imaging, or text-based tasks
- Creating custom prediction tasks
- Setting up input/output schemas for models
- Applying task-specific filtering logic
Key Topics:
- 20+ predefined clinical tasks
- EHR tasks (mortality, readmission, length of stay, drug recommendation)
- Signal tasks (sleep staging, EEG analysis, seizure detection)
- Imaging tasks (COVID-19 chest X-ray classification)
- Text tasks (medical coding, specialty classification)
- Custom task creation patterns
4. Models and Architectures
File: references/models.md
Read when:
- Selecting models for clinical prediction
- Understanding model architectures and capabilities
- Choosing between general-purpose and healthcare-specific models
- Implementing interpretable models (RETAIN, AdaCare)
- Working with medication recommendation (SafeDrug, GAMENet)
- Using graph neural networks for healthcare
- Configuring model hyperparameters
Key Topics:
- 33+ available models
- General-purpose: Logistic Regression, MLP, CNN, RNN, Transformer, GNN
- Healthcare-specific: RETAIN, SafeDrug, GAMENet, StageNet, AdaCare
- Model selection by task type and data type
- Interpretability considerations
- Computational requirements
- Hyperparameter tuning guidelines
5. Data Preprocessing
File: references/preprocessing.md
Read when:
- Preprocessing clinical data for models
- Handling sequential events and time-series data
- Processing physiological signals (EEG, ECG)
- Normalizing lab values and vital signs
- Preparing labels for different task types
- Building feature vocabularies
- Managing missing data and outliers
Key Topics:
- 15+ processor types
- Sequence processing (padding, truncation)
- Signal processing (filtering, segmentation)
- Feature extraction and encoding
- Label processors (binary, multi-class, multi-label, regression)
- Text and image preprocessing
- Common preprocessing workflows
6. Training and Evaluation
File: references/training_evaluation.md
Read when:
- Training models with the Trainer class
- Evaluating model performance
- Computing clinical metrics
- Assessing model fairness across demographics
- Calibrating predictions for reliability
- Quantifying prediction uncertainty
- Interpreting model predictions
- Preparing models for clinical deployment
Key Topics:
- Trainer class (train, evaluate, inference)
- Metrics for binary, multi-class, multi-label, regression tasks
- Fairness metrics for bias assessment
- Calibration methods (Platt scaling, temperature scaling)
- Uncertainty quantification (conformal prediction, MC dropout)
- Interpretability tools (attention visualization, SHAP, ChEFER)
- Complete training pipeline example
Installation
uv pip install pyhealth
Requirements:
- Python ≥ 3.7
- PyTorch ≥ 1.8
- NumPy, pandas, scikit-learn
Common Use Cases
Use Case 1: ICU Mortality Prediction
Objective: Predict patient mortality in intensive care unit
Approach:
- Load MIMIC-IV dataset → Read
references/datasets.md
- Apply mortality prediction task → Read
references/tasks.md
- Select interpretable model (RETAIN) → Read
references/models.md
- Train and evaluate → Read
references/training_evaluation.md
- Interpret predictions for clinical use → Read
references/training_evaluation.md
Use Case 2: Safe Medication Recommendation
Objective: Recommend medications while avoiding drug-drug interactions
Approach:
- Load EHR dataset (MIMIC-IV or OMOP) → Read
references/datasets.md
- Apply drug recommendation task → Read
references/tasks.md
- Use SafeDrug model with DDI constraints → Read
references/models.md
- Preprocess medication codes → Read
references/medical_coding.md
- Evaluate with multi-label metrics → Read
references/training_evaluation.md
Use Case 3: Hospital Readmission Prediction
Objective: Identify patients at risk of 30-day readmission
Approach:
- Load multi-site EHR data (eICU or OMOP) → Read
references/datasets.md
- Apply readmission prediction task → Read
references/tasks.md
- Handle class imbalance in preprocessing → Read
references/preprocessing.md
- Train Transformer model → Read
references/models.md
- Calibrate predictions and assess fairness → Read
references/training_evaluation.md
Use Case 4: Sleep Disorder Diagnosis
Objective: Classify sleep stages from EEG signals
Approach:
- Load sleep EEG dataset (SleepEDF, SHHS) → Read
references/datasets.md
- Apply sleep staging task → Read
references/tasks.md
- Preprocess EEG signals (filtering, segmentation) → Read
references/preprocessing.md
- Train CNN or RNN model → Read
references/models.md
- Evaluate per-stage performance → Read
references/training_evaluation.md
Use Case 5: Medical Code Translation
Objective: Standardize diagnoses across different coding systems
Approach:
- Read
references/medical_coding.md for comprehensive guidance
- Use CrossMap to translate between ICD-9, ICD-10, CCS
- Group codes into clinically meaningful categories
- Integrate with dataset processing
Use Case 6: Clinical Text to ICD Coding
Objective: Automatically assign ICD codes from clinical notes
Approach:
- Load MIMIC-III with clinical text → Read
references/datasets.md
- Apply ICD coding task → Read
references/tasks.md
- Preprocess clinical text → Read
references/preprocessing.md
- Use TransformersModel (ClinicalBERT) → Read
references/models.md
- Evaluate with multi-label metrics → Read
references/training_evaluation.md
Best Practices
Data Handling
-
Always split by patient: Prevent data leakage by ensuring no patient appears in multiple splits
from pyhealth.datasets import split_by_patient
train, val, test = split_by_patient(dataset, [0.7, 0.1, 0.2])
-
Check dataset statistics: Understand your data before modeling
print(dataset.stats()) # Patients, visits, events, code distributions
-
Use appropriate preprocessing: Match processors to data types (see references/preprocessing.md)
Model Development
-
Start with baselines: Establish baseline performance with simple models
- Logistic Regression for binary/multi-class tasks
- MLP for initial deep learning baseline
-
Choose task-appropriate models:
- Interpretability needed → RETAIN, AdaCare
- Drug recommendation → SafeDrug, GAMENet
- Long sequences → Transformer
- Graph relationships → GNN
-
Monitor validation metrics: Use appropriate metrics for task and handle class imbalance
- Binary classification: AUROC, AUPRC (especially for rare events)
- Multi-class: macro-F1 (for imbalanced), weighted-F1
- Multi-label: Jaccard, example-F1
- Regression: MAE, RMSE
Clinical Deployment
-
Calibrate predictions: Ensure probabilities are reliable (see references/training_evaluation.md)
-
Assess fairness: Evaluate across demographic groups to detect bias
-
Quantify uncertainty: Provide confidence estimates for predictions
-
Interpret predictions: Use attention weights, SHAP, or ChEFER for clinical trust
-
Validate thoroughly: Use held-out test sets from different time periods or sites
Limitations and Considerations
Data Requirements
- Large datasets: Deep learning models require sufficient data (thousands of patients)
- Data quality: Missing data and coding errors impact performance
- Temporal consistency: Ensure train/test split respects temporal ordering when needed
Clinical Validation
- External validation: Test on data from different hospitals/systems
- Prospective evaluation: Validate in real clinical settings before deployment
- Clinical review: Have clinicians review predictions and interpretations
- Ethical considerations: Address privacy (HIPAA/GDPR), fairness, and safety
Computational Resources
- GPU recommended: For training deep learning models efficiently
- Memory requirements: Large datasets may require 16GB+ RAM
- Storage: Healthcare datasets can be 10s-100s of GB
Troubleshooting
Common Issues
ImportError for dataset:
- Ensure dataset files are downloaded and path is correct
- Check PyHealth version compatibility
Out of memory:
- Reduce batch size
- Reduce sequence length (
max_seq_length)
- Use gradient accumulation
- Process data in chunks
Poor performance:
- Check class imbalance and use appropriate metrics (AUPRC vs AUROC)
- Verify preprocessing (normalization, missing data handling)
- Increase model capacity or training epochs
- Check for data leakage in train/test split
Slow training:
- Use GPU (
device="cuda")
- Increase batch size (if memory allows)
- Reduce sequence length
- Use more efficient model (CNN vs Transformer)
Getting Help
- Documentation: https://pyhealth.readthedocs.io/
- GitHub Issues: https://github.com/sunlabuiuc/PyHealth/issues
- Tutorials: 7 core tutorials + 5 practical pipelines available online
Example: Complete Workflow
# Complete mortality prediction pipeline
from pyhealth.datasets import MIMIC4Dataset
from pyhealth.tasks import mortality_prediction_mimic4_fn
from pyhealth.datasets import split_by_patient, get_dataloader
from pyhealth.models import RETAIN
from pyhealth.trainer import Trainer
# 1. Load dataset
print("Loading MIMIC-IV dataset...")
dataset = MIMIC4Dataset(root="/data/mimic4")
print(dataset.stats())
# 2. Define task
print("Setting mortality prediction task...")
sample_dataset = dataset.set_task(mortality_prediction_mimic4_fn)
print(f"Generated {len(sample_dataset)} samples")
# 3. Split data (by patient to prevent leakage)
print("Splitting data...")
train_ds, val_ds, test_ds = split_by_patient(
sample_dataset, ratios=[0.7, 0.1, 0.2], seed=42
)
# 4. Create data loaders
train_loader = get_dataloader(train_ds, batch_size=64, shuffle=True)
val_loader = get_dataloader(val_ds, batch_size=64)
test_loader = get_dataloader(test_ds, batch_size=64)
# 5. Initialize interpretable model
print("Initializing RETAIN model...")
model = RETAIN(
dataset=sample_dataset,
feature_keys=["diagnoses", "procedures", "medications"],
mode="binary",
embedding_dim=128,
hidden_dim=128
)
# 6. Train model
print("Training model...")
trainer = Trainer(model=model, device="cuda")
trainer.train(
train_dataloader=train_loader,
val_dataloader=val_loader,
epochs=50,
optimizer="Adam",
learning_rate=1e-3,
weight_decay=1e-5,
monitor="pr_auc_score", # Use AUPRC for imbalanced data
monitor_criterion="max",
save_path="./checkpoints/mortality_retain"
)
# 7. Evaluate on test set
print("Evaluating on test set...")
test_results = trainer.evaluate(
test_loader,
metrics=["accuracy", "precision", "recall", "f1_score",
"roc_auc_score", "pr_auc_score"]
)
print("\nTest Results:")
for metric, value in test_results.items():
print(f" {metric}: {value:.4f}")
# 8. Get predictions with attention for interpretation
predictions = trainer.inference(
test_loader,
additional_outputs=["visit_attention", "feature_attention"],
return_patient_ids=True
)
# 9. Analyze a high-risk patient
high_risk_idx = predictions["y_pred"].argmax()
patient_id = predictions["patient_ids"][high_risk_idx]
visit_attn = predictions["visit_attention"][high_risk_idx]
feature_attn = predictions["feature_attention"][high_risk_idx]
print(f"\nHigh-risk patient: {patient_id}")
print(f"Risk score: {predictions['y_pred'][high_risk_idx]:.3f}")
print(f"Most influential visit: {visit_attn.argmax()}")
print(f"Most important features: {feature_attn[visit_attn.argmax()].argsort()[-5:]}")
# 10. Save model for deployment
trainer.save("./models/mortality_retain_final.pt")
print("\nModel saved successfully!")
Resources
For detailed information on each component, refer to the comprehensive reference files in the references/ directory:
- datasets.md: Data structures, loading, and splitting (4,500 words)
- medical_coding.md: Code translation and standardization (3,800 words)
- tasks.md: Clinical prediction tasks and custom task creation (4,200 words)
- models.md: Model architectures and selection guidelines (5,100 words)
- preprocessing.md: Data processors and preprocessing workflows (4,600 words)
- training_evaluation.md: Training, metrics, calibration, interpretability (5,900 words)
Total comprehensive documentation: ~28,000 words across modular reference files.
