Bio Ontology Mapper

Source: https://github.com/aipoch/medical-research-skills

Bio-Ontology Mapper

When to Use

• Use this skill when the task is to Map unstructured biomedical text to standardized ontologies (SNOMED CT.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Map unstructured biomedical text to standardized ontologies (SNOMED CT.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• dataclasses: unspecified. Declared in requirements.txt.
• difflib: unspecified. Declared in requirements.txt.

Example Usage

cd "20260318/scientific-skills/Evidence Insight/bio-ontology-mapper"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Overview

Biomedical terminology normalization tool that maps free-text clinical and scientific concepts to standardized ontologies for semantic interoperability and data harmonization.

Key Capabilities:

• Multi-Ontology Support: SNOMED CT, MeSH, ICD-10, LOINC, RxNorm
• Entity Extraction: NER for diseases, symptoms, procedures, drugs
• Fuzzy Matching: Handle typos, abbreviations, and synonyms
• Confidence Scoring: Reliability metrics for each mapping
• Batch Processing: Normalize large datasets efficiently
• Cross-Mapping: Translate between ontology systems

Core Capabilities

1. Entity Recognition and Mapping

Extract and map biomedical entities to ontologies:

from scripts.mapper import BioOntologyMapper

mapper = BioOntologyMapper()

# Map clinical text
result = mapper.map_text(
    text="Patient has diabetes and hypertension, taking metformin",
    ontologies=["snomed", "mesh", "rxnorm"],
    confidence_threshold=0.7
)

for entity in result.entities:
    print(f"{entity.text} → {entity.concept_id} ({entity.ontology})")
    print(f"  Preferred: {entity.preferred_term}")
    print(f"  Confidence: {entity.confidence:.2f}")

Supported Ontologies:

2. Cross-Ontology Translation

Map concepts between different ontologies:

# Cross-map SNOMED to ICD-10
translation = mapper.cross_map(
    source_id="22298006",  # SNOMED: Myocardial infarction
    source_ontology="snomed",
    target_ontology="icd10"
)

print(f"ICD-10: {translation.target_id} - {translation.target_term}")

# Output: I21.9 - Acute myocardial infarction, unspecified

Cross-Mapping Coverage:

• SNOMED CT ↔ ICD-10-CM (clinical modifications)
• MeSH ↔ SNOMED CT (literature to clinical)
• RxNorm ↔ ATC (drug classifications)
• LOINC ↔ SNOMED (lab to clinical)

3. Batch Normalization

Process large datasets:

# Batch process CSV
results = mapper.batch_map(
    input_file="clinical_terms.csv",
    text_column="diagnosis_description",
    ontologies=["snomed", "icd10"],
    output_format="csv",
    max_workers=4
)

# Results include:

# - Original term

# - Mapped concept ID

# - Confidence score

# - Alternative mappings (if ambiguous)

Performance:

• ~100 terms/second (with caching)
• ~20 terms/second (API lookup)
• Parallel processing for large datasets

4. Confidence Scoring and Validation

Assess mapping reliability:

scoring = mapper.score_mapping(
    term="heart attack",
    candidate="22298006",  # Myocardial infarction
    factors=["string_similarity", "context_match", "frequency"]
)

print(f"Overall confidence: {scoring.confidence:.2f}")
print(f"Breakdown: {scoring.factors}")

Scoring Factors:

• String similarity: Levenshtein distance, n-grams
• Context match: Surrounding words alignment
• Frequency: Common usage in corpus
• Semantic similarity: Vector embeddings

Quality Checklist

Pre-Mapping:

• Text preprocessed (lowercase, punctuation handled)
• Abbreviations expanded where possible
• Language identified (multilingual support)

During Mapping:

• Confidence threshold appropriate (>0.7 for clinical)
• Multiple candidates considered for ambiguous terms
• Context used for disambiguation

Post-Mapping:

• Low-confidence mappings flagged for review
• Unmapped terms logged
• CRITICAL: Clinical expert validation for high-stakes use

Before Production:

• Mapping accuracy validated on gold standard
• False positive rate acceptable (<5%)
• Recall acceptable for use case (>90%)
• API rate limits respected

Common Pitfalls

Mapping Errors:

• ❌ Abbreviation ambiguity → "MI" = Myocardial infarction OR Michigan

  • ✅ Use context; flag for manual review

• ❌ Outdated terms → Old terminology not in current ontology

  • ✅ Use historical mappings; update terminology

• ❌ False confidence → High score for wrong concept

  • ✅ Always review top-3 candidates

Technical Issues:

• ❌ API failures → No local fallback

  • ✅ Implement caching; use local reference files

• ❌ Version mismatches → Different ontology versions

  • ✅ Track ontology version used

• ❌ PHI exposure → Sending patient data to external APIs

  • ✅ De-identify before API calls; use local processing when possible