pyhealth

What this skill does

# 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

```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:**
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 `r