Health Data Connectors¶

OpenML Crawler provides specialized health data connectors that access medical, clinical, and public health data from various authoritative sources. These connectors handle sensitive health information with proper privacy controls and compliance measures.

Supported Health Data Sources¶

CDC (Centers for Disease Control and Prevention)¶

Official US public health data from the Centers for Disease Control and Prevention.

Features:

Disease surveillance data
Vaccination statistics
Health outcome metrics
Demographic health data
Environmental health indicators
Injury and violence data
Chronic disease statistics

Usage:

from openmlcrawler.connectors.health import CDCConnector

connector = CDCConnector()

# Get disease surveillance data
surveillance_data = connector.get_disease_data(
    disease="COVID-19",
    geography="United States",
    date_range=("2020-01-01", "2023-12-31")
)

# Get vaccination statistics
vaccination_data = connector.get_vaccination_data(
    vaccine_type="COVID-19",
    geography="states",
    date_range=("2021-01-01", "2023-12-31")
)

# Get health outcome metrics
outcomes = connector.get_health_outcomes(
    indicators=["mortality", "morbidity"],
    demographics=["age", "gender", "race"]
)

WHO (World Health Organization)¶

Global health data and statistics from the World Health Organization.

Features:

Global health indicators
Disease outbreak data
Health system performance
Mortality and morbidity statistics
Environmental health data
Health workforce data
Universal health coverage metrics

Configuration:

connectors:
  health:
    who:
      base_url: "https://ghoapi.who.int/api"
      format: "json"
      language: "en"
      cache_enabled: true

Usage:

from openmlcrawler.connectors.health import WHOConnector

connector = WHOConnector()

# Get global health indicators
indicators = connector.get_health_indicators(
    indicator_codes=["WHOSIS_000001", "WHOSIS_000002"],
    countries=["USA", "GBR", "DEU"],
    year_range=(2015, 2023)
)

# Get disease outbreak data
outbreaks = connector.get_outbreak_data(
    disease="Ebola",
    region="AFRICA",
    date_range=("2014-01-01", "2023-12-31")
)

# Get health system data
health_systems = connector.get_health_system_data(
    countries=["USA", "CAN", "GBR"],
    indicators=["health_expenditure", "physicians_per_1000"]
)

PubMed¶

Medical research literature from the National Library of Medicine's PubMed database.

Features:

Medical research articles
Clinical trial data
Systematic reviews
Meta-analyses
Case studies
Medical guidelines
Drug information

Usage:

from openmlcrawler.connectors.health import PubMedConnector

connector = PubMedConnector(api_key="your_key", email="your_email")

# Search medical literature
articles = connector.search_articles(
    query="machine learning AND medical diagnosis",
    max_results=100,
    date_range=("2020/01/01", "2023/12/31")
)

# Get article details
article_details = connector.get_article_details(
    pmids=["12345678", "87654321"]
)

# Search clinical trials
trials = connector.search_clinical_trials(
    condition="diabetes",
    intervention="machine learning",
    status="completed"
)

# Get systematic reviews
reviews = connector.get_systematic_reviews(
    topic="AI in healthcare",
    max_results=50
)

ClinicalTrials.gov¶

Clinical trial registry maintained by the US National Library of Medicine.

Features:

Clinical trial protocols
Trial status and results
Patient recruitment data
Study designs and outcomes
Investigator information
Sponsor details
Regulatory information

Usage:

from openmlcrawler.connectors.health import ClinicalTrialsConnector

connector = ClinicalTrialsConnector()

# Search clinical trials
trials = connector.search_trials(
    condition="cancer",
    intervention="immunotherapy",
    status="Recruiting",
    max_results=100
)

# Get trial details
trial_details = connector.get_trial_details(
    nct_ids=["NCT12345678", "NCT87654321"]
)

# Get recruitment data
recruitment = connector.get_recruitment_data(
    condition="COVID-19",
    location="United States"
)

# Search by sponsor
sponsor_trials = connector.search_by_sponsor(
    sponsor="Pfizer",
    condition="COVID-19"
)

Data Types and Categories¶

Public Health Data¶

Category	Description	Sources	Update Frequency
Disease Surveillance	Outbreak tracking and monitoring	CDC, WHO	Daily/Weekly
Vital Statistics	Births, deaths, life expectancy	CDC, WHO	Monthly/Yearly
Health Behaviors	Smoking, diet, exercise patterns	CDC	Annual
Health Outcomes	Disease incidence and prevalence	CDC, WHO	Quarterly
Healthcare Access	Insurance coverage, provider access	CDC	Annual
Environmental Health	Air/water quality health impacts	CDC, WHO	Monthly

Clinical Research Data¶

Clinical Trials: Study protocols, results, and outcomes
Medical Literature: Research articles, reviews, and meta-analyses
Drug Information: Medication data, interactions, and approvals
Medical Devices: Device approvals, recalls, and safety data
Healthcare Quality: Hospital performance and patient outcomes
Health Economics: Cost-effectiveness and resource allocation

Genomic and Biomarker Data¶

Genetic Data: Gene variants and associations
Biomarker Data: Diagnostic and prognostic markers
Pharmacogenomics: Genetic factors in drug response
Rare Diseases: Orphan disease data and research
Population Genomics: Large-scale genetic studies

Data Collection Strategies¶

Public Health Surveillance¶

from openmlcrawler.connectors.health import HealthSurveillanceCollector

collector = HealthSurveillanceCollector()

# Collect disease surveillance data
surveillance_data = collector.collect_surveillance_data(
    diseases=["COVID-19", "Influenza", "Ebola"],
    regions=["GLOBAL", "AMERICA", "EUROPE"],
    date_range=("2020-01-01", "2023-12-31")
)

# Monitor health indicators
indicators = collector.monitor_health_indicators(
    indicators=["mortality_rate", "vaccination_coverage"],
    countries=["USA", "CAN", "MEX"],
    frequency="weekly"
)

# Generate health reports
reports = collector.generate_health_reports(
    data=surveillance_data,
    report_types=["epidemiological", "trends", "forecasts"]
)

Collecting Clinical Research Data¶

from openmlcrawler.connectors.health import ClinicalResearchCollector

collector = ClinicalResearchCollector()

# Collect clinical trial data
trial_data = collector.collect_trial_data(
    conditions=["cancer", "diabetes", "cardiovascular"],
    phases=["Phase 2", "Phase 3", "Phase 4"],
    status=["Completed", "Recruiting"],
    max_results=1000
)

# Collect medical literature
literature = collector.collect_medical_literature(
    topics=["machine learning", "AI", "deep learning"],
    publication_types=["systematic review", "randomized trial"],
    date_range=("2020-01-01", "2023-12-31")
)

# Analyze research trends
trends = collector.analyze_research_trends(
    data=literature,
    analysis_types=["publication_trends", "collaboration_networks"]
)

Batch Processing¶

from openmlcrawler.connectors.health import BatchHealthProcessor

processor = BatchHealthProcessor()

# Process multiple health data sources
results = processor.process_batch(
    sources=["cdc", "who", "pubmed"],
    categories=["surveillance", "clinical_trials", "literature"],
    date_range=("2020-01-01", "2023-12-31"),
    output_format="parquet"
)

Privacy and Compliance¶

HIPAA Compliance¶

Health Insurance Portability and Accountability Act compliance for US health data.

from openmlcrawler.connectors.health import HIPAACompliantConnector

connector = HIPAACompliantConnector(
    enable_deidentification=True,
    audit_logging=True,
    access_controls=True
)

# Collect HIPAA-compliant data
compliant_data = connector.collect_hipaa_compliant(
    data_sources=["clinical_trials", "medical_records"],
    deidentification_level="safe_harbor",
    purpose="research"
)

# Audit data access
audit_log = connector.get_audit_log(
    date_range=("2023-01-01", "2023-12-31"),
    user_id="researcher_123"
)

General Data Protection Regulation compliance for EU health data.

from openmlcrawler.connectors.health import GDPRCompliantConnector

connector = GDPRCompliantConnector(
    enable_consent_management=True,
    data_minimization=True,
    retention_limits=True
)

# Collect GDPR-compliant data
gdpr_data = connector.collect_gdpr_compliant(
    data_sources=["who", "eu_health_systems"],
    consent_verified=True,
    processing_purpose="public_health_research"
)

# Manage data subject rights
rights_response = connector.handle_data_subject_request(
    request_type="access",
    subject_id="patient_456",
    justification="medical_research"
)

Data De-identification¶

from openmlcrawler.connectors.health import HealthDataDeidentifier

deidentifier = HealthDataDeidentifier()

# De-identify health data
deidentified_data = deidentifier.deidentify_data(
    data=health_data,
    methods=[
        "remove_direct_identifiers",
        "generalize_dates",
        "mask_sensitive_values"
    ],
    risk_threshold="very_low"
)

# Validate de-identification
validation = deidentifier.validate_deidentification(
    original_data=health_data,
    deidentified_data=deidentified_data,
    reidentification_risk_threshold=0.0001
)

Data Quality and Validation¶

Quality Assurance¶

Source Verification: Validate data source authenticity and authority
Data Completeness: Check for missing values and data gaps
Temporal Consistency: Verify chronological data ordering
Cross-Source Validation: Compare data across multiple authoritative sources
Statistical Validation: Check for outliers and data anomalies

Validation Framework¶

from openmlcrawler.connectors.health import HealthDataValidator

validator = HealthDataValidator()

# Validate health data
validation_result = validator.validate_health_data(
    data=health_data,
    checks=[
        "source_authority",
        "data_completeness",
        "temporal_consistency",
        "statistical_validity",
        "privacy_compliance"
    ]
)

# Generate quality report
quality_report = validator.generate_quality_report(
    validation_results=validation_result,
    include_recommendations=True,
    compliance_frameworks=["HIPAA", "GDPR"]
)

Integration with ML Pipelines¶

Disease Prediction Models¶

from openmlcrawler.connectors.health import DiseasePredictionPipeline

pipeline = DiseasePredictionPipeline()

# Build prediction model
model = pipeline.build_prediction_model(
    training_data=surveillance_data,
    target_diseases=["COVID-19", "Influenza"],
    features=[
        "demographic_data",
        "environmental_factors",
        "healthcare_capacity",
        "vaccination_rates"
    ],
    model_type="xgboost"
)

# Generate predictions
predictions = pipeline.generate_predictions(
    model=model,
    input_data=current_data,
    prediction_horizon=30,  # days
    confidence_intervals=True
)

# Validate predictions
validation = pipeline.validate_predictions(
    predictions=predictions,
    actual_data=validation_data,
    metrics=["accuracy", "precision", "recall", "auc"]
)

Clinical Trial Analysis¶

from openmlcrawler.connectors.health import ClinicalTrialAnalyzer

analyzer = ClinicalTrialAnalyzer()

# Analyze trial outcomes
outcomes = analyzer.analyze_trial_outcomes(
    trial_data=clinical_trials,
    outcome_measures=[
        "efficacy",
        "safety",
        "adverse_events",
        "patient_reported_outcomes"
    ]
)

# Identify successful interventions
successful_trials = analyzer.identify_successful_interventions(
    trials=outcomes,
    success_criteria={
        "efficacy_threshold": 0.7,
        "safety_threshold": 0.8,
        "sample_size_min": 100
    }
)

# Generate research insights
insights = analyzer.generate_research_insights(
    trial_data=outcomes,
    insight_types=[
        "treatment_effectiveness",
        "adverse_event_patterns",
        "patient_subgroup_analysis"
    ]
)

Configuration Options¶

Global Configuration¶

health_connectors:
  default_sources: ["cdc", "who"]
  privacy_compliance:
    enable_hipaa: true
    enable_gdpr: true
    deidentification_required: true
  data_quality:
    enable_validation: true
    strict_mode: true
    authority_check: true
  caching:
    enable_cache: true
    cache_ttl_hours: 6
    max_cache_size_gb: 50
  rate_limiting:
    requests_per_minute: 30
    burst_limit: 10

Source-Specific Settings¶

cdc:
  base_url: "https://data.cdc.gov"
  api_key: "${CDC_API_KEY}"
  format: "json"
  timeout_seconds: 30

who:
  base_url: "https://ghoapi.who.int/api"
  format: "json"
  language: "en"
  cache_enabled: true

pubmed:
  api_key: "${PUBMED_API_KEY}"
  email: "${PUBMED_EMAIL}"
  tool: "OpenMLCrawler"
  max_results_per_query: 100

clinicaltrials:
  base_url: "https://clinicaltrials.gov/api"
  format: "json"
  timeout_seconds: 30
  max_results: 1000

Best Practices¶

Data Privacy and Security¶

De-identification: Always de-identify personal health information
Access Controls: Implement strict access controls and audit logging
Data Minimization: Collect only necessary health data fields
Retention Limits: Implement data retention policies
Secure Transmission: Use encrypted connections for data transfer

Data Quality Management¶

Source Verification: Use only authoritative health data sources
Cross-Validation: Validate data against multiple sources
Regular Updates: Monitor data freshness and update frequencies
Error Detection: Implement automated error detection and correction
Documentation: Maintain comprehensive data lineage and metadata

Performance Optimization¶

Intelligent Caching: Cache health data appropriately (short TTL)
Batch Processing: Use batch operations for large datasets
Selective Queries: Query only required data fields and time ranges
Rate Limiting: Respect API rate limits and implement backoff strategies
Data Compression: Compress large health datasets for storage

Troubleshooting¶

Common Issues¶

Privacy Compliance¶

Error: Data privacy violation
Solution: Implement proper de-identification and access controls

Data Access Restrictions¶

Error: Access denied to health data
Solution: Obtain proper authorization and credentials

Data Quality Issues¶

Error: Health data validation failed
Solution: Verify data sources and implement quality checks

API Rate Limiting¶

Error: API rate limit exceeded
Solution: Implement rate limiting and exponential backoff

Health Data Connectors¶

Supported Health Data Sources¶

CDC (Centers for Disease Control and Prevention)¶

WHO (World Health Organization)¶

PubMed¶

ClinicalTrials.gov¶

Data Types and Categories¶

Public Health Data¶

Clinical Research Data¶

Genomic and Biomarker Data¶

Data Collection Strategies¶

Public Health Surveillance¶

Collecting Clinical Research Data¶

Batch Processing¶

Privacy and Compliance¶

HIPAA Compliance¶

GDPR Compliance¶

Data De-identification¶

Data Quality and Validation¶

Quality Assurance¶

Validation Framework¶

Integration with ML Pipelines¶

Disease Prediction Models¶

Clinical Trial Analysis¶

Configuration Options¶

Global Configuration¶

Source-Specific Settings¶

Best Practices¶

Data Privacy and Security¶

Data Quality Management¶

Performance Optimization¶

Troubleshooting¶

Common Issues¶

Privacy Compliance¶

Data Access Restrictions¶

Data Quality Issues¶

API Rate Limiting¶

See Also¶