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pyhealth Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
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name pyhealth description Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN). license MIT license metadata {"skill-author":"K-Dense Inc."} verified false lastVerifiedAt "2026-02-19T05:29:09.098Z" source builtin trust_score 100 provenance_sha 33626e0e5864d408
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 imagesClinical prediction tasks : Mortality prediction, hospital readmission, length of stay, drug recommendationMedical coding : Translating between ICD-9/10, NDC, RxNorm, ATC coding systemsProcessing clinical data : Sequential events, physiological signals, clinical text, medical imagesImplementing healthcare models : RETAIN, SafeDrug, GAMENet, StageNet, Transformer for EHREvaluating 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 interfacesTask Definition : Apply 20+ predefined clinical prediction tasks or create custom tasksModel Selection : Choose from 33+ models (baselines, deep learning, healthcare-specific)Training : Train with automatic checkpointing, monitoring, and evaluationDeployment : 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
dataset = MIMIC4Dataset(root="/path/to/data"
0.7
0.1
0.2
64
True
64
False
64
False
"diagnoses"
"medications"
"binary"
128
"cuda"
50
"pr_auc_score"
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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())
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 performanceTemporal consistency : Ensure train/test split respects temporal ordering when needed
Clinical Validation
External validation : Test on data from different hospitals/systemsProspective evaluation : Validate in real clinical settings before deploymentClinical review : Have clinicians review predictions and interpretationsEthical considerations : Address privacy (HIPAA/GDPR), fairness, and safety
Computational Resources
GPU recommended : For training deep learning models efficientlyMemory requirements : Large datasets may require 16GB+ RAMStorage : Healthcare datasets can be 10s-100s of GB
Troubleshooting
Common Issues
Ensure dataset files are downloaded and path is correct
Check PyHealth version compatibility
Reduce batch size
Reduce sequence length (max_seq_length)
Use gradient accumulation
Process data in chunks
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
Use GPU (device="cuda")
Increase batch size (if memory allows)
Reduce sequence length
Use more efficient model (CNN vs Transformer)
Getting Help
Example: Complete 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 RETAIN
from pyhealth.trainer import Trainer
print ("Loading MIMIC-IV dataset..." )
dataset = MIMIC4Dataset(root="/data/mimic4" )
print (dataset.stats())
print ("Setting mortality prediction task..." )
sample_dataset = dataset.set_task(mortality_prediction_mimic4_fn)
print (f"Generated {len (sample_dataset)} samples" )
print ("Splitting data..." )
train_ds, val_ds, test_ds = split_by_patient(
sample_dataset, ratios=[0.7 , 0.1 , 0.2 ], seed=42
)
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 )
print ("Initializing RETAIN model..." )
model = RETAIN(
dataset=sample_dataset,
feature_keys=["diagnoses" , "procedures" , "medications" ],
mode="binary" ,
embedding_dim=128 ,
hidden_dim=128
)
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" ,
monitor_criterion="max" ,
save_path="./checkpoints/mortality_retain"
)
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:.4 f} " )
predictions = trainer.inference(
test_loader,
additional_outputs=["visit_attention" , "feature_attention" ],
return_patient_ids=True
)
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]:.3 f} " )
print (f"Most influential visit: {visit_attn.argmax()} " )
print (f"Most important features: {feature_attn[visit_attn.argmax()].argsort()[-5 :]} " )
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.
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