Pyhealth

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:

1. Data Loading: Access 10+ healthcare datasets with standardized interfaces
2. Task Definition: Apply 20+ predefined clinical prediction tasks or create custom tasks
3. Model Selection: Choose from 33+ models (baselines, deep learning, healthcare-specific)
4. Training: Train with automatic checkpointing, monitoring, and evaluation
5. 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

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:

1. Load MIMIC-IV dataset → Read references/datasets.md
2. Apply mortality prediction task → Read references/tasks.md
3. Select interpretable model (RETAIN) → Read references/models.md
4. Train and evaluate → Read references/training_evaluation.md
5. 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:

1. Load EHR dataset (MIMIC-IV or OMOP) → Read references/datasets.md
2. Apply drug recommendation task → Read references/tasks.md
3. Use SafeDrug model with DDI constraints → Read references/models.md
4. Preprocess medication codes → Read references/medical_coding.md
5. 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:

1. Load multi-site EHR data (eICU or OMOP) → Read references/datasets.md
2. Apply readmission prediction task → Read references/tasks.md
3. Handle class imbalance in preprocessing → Read references/preprocessing.md
4. Train Transformer model → Read references/models.md
5. Calibrate predictions and assess fairness → Read references/training_evaluation.md

Use Case 4: Sleep Disorder Diagnosis

Objective: Classify sleep stages from EEG signals

Approach:

1. Load sleep EEG dataset (SleepEDF, SHHS) → Read references/datasets.md
2. Apply sleep staging task → Read references/tasks.md
3. Preprocess EEG signals (filtering, segmentation) → Read references/preprocessing.md
4. Train CNN or RNN model → Read references/models.md
5. Evaluate per-stage performance → Read references/training_evaluation.md

Use Case 5: Medical Code Translation

Objective: Standardize diagnoses across different coding systems

Approach:

1. Read references/medical_coding.md for comprehensive guidance
2. Use CrossMap to translate between ICD-9, ICD-10, CCS
3. Group codes into clinically meaningful categories
4. Integrate with dataset processing

Use Case 6: Clinical Text to ICD Coding

Objective: Automatically assign ICD codes from clinical notes

Approach:

1. Load MIMIC-III with clinical text → Read references/datasets.md
2. Apply ICD coding task → Read references/tasks.md
3. Preprocess clinical text → Read references/preprocessing.md
4. Use TransformersModel (ClinicalBERT) → Read references/models.md
5. Evaluate with multi-label metrics → Read references/training_evaluation.md

Best Practices

Data Handling

1. 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])
   

2. Check dataset statistics: Understand your data before modeling

   print(dataset.stats())  # Patients, visits, events, code distributions
   

3. Use appropriate preprocessing: Match processors to data types (see references/preprocessing.md)

Model Development

1. Start with baselines: Establish baseline performance with simple models

   • Logistic Regression for binary/multi-class tasks
   • MLP for initial deep learning baseline

2. Choose task-appropriate models:

   • Interpretability needed → RETAIN, AdaCare
   • Drug recommendation → SafeDrug, GAMENet
   • Long sequences → Transformer
   • Graph relationships → GNN

3. 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

1. Calibrate predictions: Ensure probabilities are reliable (see references/training_evaluation.md)

2. Assess fairness: Evaluate across demographic groups to detect bias

3. Quantify uncertainty: Provide confidence estimates for predictions

4. Interpret predictions: Use attention weights, SHAP, or ChEFER for clinical trust

5. 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.