Clinical Evidence Extraction

Identifies and classifies clinical concepts within unstructured text using deep learning models fine-tuned on biomedical corpora. The system detects medications, diagnoses, procedures, laboratory values, and anatomical sites with high precision.

• Extracts RxNorm codes for medications mentioned in narrative notes
• Identifies SNOMED CT concepts for clinical findings
• Detects LOINC codes for laboratory test names
• Recognizes temporal expressions and dosage information

Distinguishes between affirmed, negated, and uncertain clinical findings to prevent false positive extractions. The system applies contextual rules and transformer-based classifiers to determine assertion status.

• Detects negation triggers: 'patient denies', 'no evidence of', 'ruled out'
• Identifies uncertainty markers: 'suspected', 'possible', 'cannot exclude'
• Differentiates historical from current conditions
• Handles complex syntactic negation scope resolution

Maps extracted clinical terms to standard terminologies, enabling consistent computable matching against payer policies. The system resolves lexical variants, abbreviations, and synonyms to canonical concept identifiers.

• Normalizes 'HTN', 'high BP', 'elevated blood pressure' to SNOMED CT 38341003
• Maps brand names to generic RxNorm ingredients
• Resolves acronyms using contextual disambiguation
• Links lab results to LOINC codes with unit standardization

Establishes chronological relationships between clinical events to construct a coherent patient timeline. The system identifies dates, durations, and temporal connectives to sequence findings accurately.

• Extracts admission dates, procedure dates, and onset timing
• Identifies temporal expressions: '3 days post-op', 'since 2019'
• Sequences medication start and stop events
• Builds chronological context for disease progression analysis

Synthesizes clinical evidence from disparate document types and EHR sections into a unified patient view. The system reconciles information across progress notes, discharge summaries, radiology reports, and lab results.

• Cross-references findings across encounter types
• Resolves conflicting information using source reliability weighting
• Aggregates cumulative lab trends over time
• Merges structured EHR data with unstructured narrative extraction

Resolves ambiguous clinical abbreviations using contextual embeddings to prevent documentation errors. The system leverages surrounding clinical context and document type to select the correct expansion.

• Disambiguates 'CA' as cancer, calcium, or cardiac arrest based on context
• Resolves 'MS' as multiple sclerosis, mitral stenosis, or morphine sulfate
• Handles specialty-specific abbreviation conventions
• Maintains an auditable trail of disambiguation decisions