Dataset 📊

The Dataset is a pandas DataFrame wrapper designed for healthcare ML workflows: it extracts ML-ready features from FHIR Bundles using schemas, validates data types, and converts model predictions back into clinical decision support resources (RiskAssessment).

Usage

The two most helpful methods in the Dataset class are:

• from_fhir_bundle(): Extract ML-ready features from a FHIR Bundle using a feature schema.
• to_risk_assessment(): Convert model predictions into FHIR RiskAssessment resources for clinical consumption.

Feature Schemas

Define features once in YAML and reuse across training, validation, and inference. See FHIR Feature Mapper for schema details.

from healthchain.io.containers import Dataset

# 1. Extract ML features from a FHIR Bundle using a feature schema
dataset = Dataset.from_fhir_bundle(bundle, schema="path/to/schema.yaml")

# 2. Inspect the features as a pandas DataFrame
print(dataset.data.head())
print("Columns:", dataset.columns)

# 3. Validate the dataset against the schema (checks for missing/invalid fields)
validation_result = dataset.validate(schema="path/to/schema.yaml")
print("Validation Result:", validation_result)

# 4. Run inference using your ML model
predictions = model.predict(dataset.data)
probabilities = model.predict_proba(dataset.data)[:, 1]

# 5. Convert predictions to FHIR RiskAssessment resources for downstream use
risk_assessments = dataset.to_risk_assessment(
    predictions=predictions,
    probabilities=probabilities,
    outcome_code="A41.9",
    outcome_display="Sepsis, unspecified",
    model_name="SepsisRiskModel",
    model_version="1.0"
)

This workflow lets you convert FHIR healthcare data into DataFrames for ML, and then easily package predictions as standardized FHIR artifacts.

Example RiskAssessment Output

{
    "resourceType": "RiskAssessment",
    "id": "hc-a1b2c3d4",
    "status": "final",
    "subject": {
        "reference": "Patient/123"
    },
    "method": {
        "coding": [{
            "system": "https://healthchain.github.io/ml-models",
            "code": "RandomForestClassifier",
            "display": "RandomForestClassifier v2.1"
        }]
    },
    "prediction": [{
        "outcome": {
            "coding": [{
                "system": "http://hl7.org/fhir/sid/icd-10",
                "code": "A41.9",
                "display": "Sepsis, unspecified"
            }]
        },
        "probabilityDecimal": 0.85,
        "qualitativeRisk": {
            "coding": [{
                "system": "http://terminology.hl7.org/CodeSystem/risk-probability",
                "code": "high",
                "display": "High Risk"
            }]
        }
    }],
    "note": [{
        "text": "ML prediction: Positive (probability: 85.00%, risk: high)"
    }]
}

Properties and Methods

Common Dataset operations:

# Metadata
print(dataset.columns)           # List of feature names
print(dataset.row_count())       # Number of samples
print(dataset.column_count())    # Number of features
print(dataset.describe())        # Summary statistics

# Data access
df = dataset.data                # Underlying pandas DataFrame
dtypes = dataset.dtypes          # Feature data types

# Data manipulation
dataset.remove_column('temp_feature')  # Drop a feature

Resource Documentation

• FHIR RiskAssessment
• FHIR Observation

API Reference

See the Dataset API Reference for detailed class documentation.