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)