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

python

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

bash

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

python

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

python

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

python

# 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.

pyhealth

NPX Install

Tags

SKILL.md Content

PyHealth: Healthcare AI Toolkit

Overview

When to Use This Skill

Core Capabilities

Quick Start Workflow

Detailed Documentation

1. Datasets and Data Structures

2. Medical Coding Translation

3. Clinical Prediction Tasks

4. Models and Architectures

5. Data Preprocessing

6. Training and Evaluation

Installation

Common Use Cases

Use Case 1: ICU Mortality Prediction

Use Case 2: Safe Medication Recommendation

Use Case 3: Hospital Readmission Prediction

Use Case 4: Sleep Disorder Diagnosis

Use Case 5: Medical Code Translation

Use Case 6: Clinical Text to ICD Coding

Best Practices

Data Handling

Model Development

Clinical Deployment

Limitations and Considerations

Data Requirements

Clinical Validation

Computational Resources

Troubleshooting

Common Issues

Getting Help

Example: Complete Workflow

Resources