Core API Reference

EHDSAnalyzer

The main entry point for analyzing the EHDS systematic review dataset.

from ehdslens import EHDSAnalyzer

Class Definition

class EHDSAnalyzer:
    """Main analyzer class providing unified interface to EHDS SLR data."""

    def __init__(self):
        """Initialize the analyzer."""

    def load_default_data(self) -> None:
        """Load the default 52-study database."""

    def get_statistics(self) -> Dict[str, Any]:
        """Get comprehensive statistics about the database."""

    def analyze_axis(self, axis: ThematicAxis) -> Dict[str, Any]:
        """Analyze studies within a specific thematic axis."""

    def search_studies(self, query: str) -> List[Study]:
        """Search studies by keyword."""

    def filter_studies(
        self,
        axis: Optional[ThematicAxis] = None,
        year_start: Optional[int] = None,
        year_end: Optional[int] = None,
        min_quality: Optional[QualityRating] = None,
        study_type: Optional[StudyType] = None
    ) -> List[Study]:
        """Filter studies by multiple criteria."""

    def get_grade_cerqual_summary(self) -> List[Dict[str, Any]]:
        """Get GRADE-CERQual confidence assessments."""

    def get_testable_hypotheses(self) -> Dict[str, List[str]]:
        """Get testable hypotheses by category."""

    def get_research_gaps(self) -> List[str]:
        """Get identified research gaps."""

Methods

load_default_data()

Load the built-in database of 52 studies from the systematic review.

analyzer = EHDSAnalyzer()
analyzer.load_default_data()
print(f"Loaded {len(analyzer.db)} studies")

get_statistics()

Returns a dictionary with:

• total: Total number of studies
• year_range: Tuple of (min_year, max_year)
• by_axis: Count per thematic axis
• by_quality: Count per quality rating
• by_type: Count per study type

stats = analyzer.get_statistics()

analyze_axis(axis)

Analyze a specific thematic axis.

Parameters: - axis: ThematicAxis enum value

Returns: Dictionary with: - total_studies: Number of studies in axis - themes: List of key themes - quality_distribution: Quality counts - year_distribution: Year counts

analysis = analyzer.analyze_axis(ThematicAxis.GOVERNANCE_RIGHTS_ETHICS)

search_studies(query)

Search across authors, titles, and journals.

Parameters: - query: Search string (case-insensitive)

Returns: List of matching Study objects

results = analyzer.search_studies("federated learning")

filter_studies(...)

Filter studies by multiple criteria.

Parameters: - axis: Optional ThematicAxis - year_start: Optional minimum year - year_end: Optional maximum year - min_quality: Optional minimum QualityRating - study_type: Optional StudyType

Returns: List of matching Study objects

filtered = analyzer.filter_studies(
    axis=ThematicAxis.SECONDARY_USE_PETS,
    year_start=2024,
    min_quality=QualityRating.HIGH
)

get_grade_cerqual_summary()

Get GRADE-CERQual confidence assessments.

Returns: List of dictionaries with: - finding: The finding statement - confidence: Confidence level (high/moderate/low/very_low) - studies: Number of contributing studies

findings = analyzer.get_grade_cerqual_summary()
for f in findings:
    print(f"[{f['confidence']}] {f['finding']}")

get_testable_hypotheses()

Get testable hypotheses organized by category.

Returns: Dictionary mapping categories to lists of hypotheses

hypotheses = analyzer.get_testable_hypotheses()
for category, hyps in hypotheses.items():
    print(f"{category}: {len(hyps)} hypotheses")

get_research_gaps()

Get identified research gaps.

Returns: List of research gap descriptions

gaps = analyzer.get_research_gaps()
for gap in gaps:
    print(f"• {gap}")