Medical ontology code mapping

Skill rbr7/MedClawMini/skills/medical-ontology-code-mapping

A focused, production-minded library of 197 clinical-AI and healthcare data-science skills for the OpenClaw agent platform featuring data quality, clinical NLP, big-data ML, explainable AI, drug safety, and regulatory.

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npx -y skills add rbr7/MedClawMini --skill medical-ontology-code-mapping

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Normalize and map free-text or inconsistently coded medical data to standard healthcare ontologies and code systems ICD-10-CM/PCS, CPT/HCPCS, SNOMED CT, RxNorm, NDC, LOINC, and UMLS CUIs. Provides text-to-code normalization, code-to-code crosswalks (e.g., NDC↔RxNorm, ICD-9↔ICD-10, SNOMED↔ICD-10), validity checking, and ontology-graph navigation (hierarchy, synonyms, relationships). Use to standardize dirty diagnosis/procedure/drug fields, harmonize vocabularies across sources, or build a healthcare business ontology layer.

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SKILL.md

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Medical Ontology & Code Mapping

Overview

Healthcare data is only as interoperable as its vocabularies. The same condition may be a free-text string in one feed, an ICD-9 code in another, and a SNOMED CT concept in a third. This skill builds the terminology normalization layer mapping messy inputs to canonical concepts and crosswalking between code systems so that records become joinable, groupable, and analyzable. It is the "healthcare-specific business ontology" backbone for data-quality and analytics work.

When to Use This Skill

Standardizing dirty diagnosis, procedure, or drug free-text into coded concepts.
Crosswalking between vocabularies: NDC↔RxNorm, ICD-9↔ICD-10, SNOMED↔ICD-10, local codes → standard codes.
Validating that codes exist, are billable/active, and are used in the right context.
Grouping codes into clinical categories (CCSR, ATC drug classes, value sets).
Navigating an ontology graph parents, children, synonyms, relationships.

Code Systems Covered

System	Domain	Typical use
ICD-10-CM / PCS	Diagnoses / inpatient procedures	Claims, risk adjustment
CPT / HCPCS	Professional procedures / supplies	Claims
SNOMED CT	Clinical concepts (broad)	EHR, interoperability
RxNorm	Normalized drug names	Medication reconciliation
NDC	Packaged drug products	Pharmacy claims
LOINC	Labs & observations	Results data
UMLS CUI	Cross-vocabulary concept hub	Mapping across systems

Workflow

Detect the field type (dx vs. procedure vs. drug vs. lab) and current state (free-text vs. coded, which system).
Normalize text lowercase, expand abbreviations, strip dose/route noise; for drugs parse ingredient/strength/form.
Map to canonical concepts: exact/synonym lookup first, then fuzzy/lexical match (e.g., RxNorm approximateTerm, UMLS MetaMap-style), keeping a confidence score.
Crosswalk via UMLS CUI or official maps (GEMs for ICD-9↔10, RxNorm relations for NDC↔ingredient, SNOMED→ICD-10 map).
Validate code exists, is active for the service date, billable, and context- appropriate; flag retired or non-billable codes.
Group into analytic categories (CCSR, ATC, custom value sets) for rollups.

Example

# RxNorm + ICD via public APIs (no key required for RxNav / Clinical Tables)
import requests

def ndc_to_rxnorm(ndc):
    r = requests.get(f"https://rxnav.nlm.nih.gov/REST/ndcstatus.json?ndc={ndc}").json()
    return r.get("ndcStatus", {}).get("rxcui")

def normalize_drug(text):
    r = requests.get("https://rxnav.nlm.nih.gov/REST/approximateTerm.json",
                     params={"term": text, "maxEntries": 1}).json()
    cand = r["approximateGroup"]["candidate"][0]
    return cand["rxcui"], cand["score"]

def search_icd10(query):  # NLM Clinical Tables typeahead
    r = requests.get("https://clinicaltables.nlm.nih.gov/api/icd10cm/v3/search",
                     params={"sf": "code,name", "terms": query}).json()
    return r[3]      # [[code, name], ...]

rxcui, conf = normalize_drug("lipitor 20 mg tab")   # -> ('617312', 95)
codes = search_icd10("type 2 diabetes")             # -> [['E11.9','Type 2 diabetes...'], ...]

Outputs

mapped_codes.parquet input value → canonical code, system, display, confidence.
crosswalk.parquet source code/system → target code/system with map provenance.
unmapped_review.csv low-confidence or unresolved inputs for clerical review.
mapping_report.md coverage %, confidence distribution, top unmapped terms.

Healthcare Context

This is the vocabulary layer that makes claims/EHR data trustworthy and joinable. It feeds healthcare-data-quality-profiling (validity rules), patient-record-entity- resolution (standardized coded fields), and downstream modeling. Map confidence should be retained end-to-end so low-confidence mappings can be audited.

References

UMLS Metathesaurus & RxNav APIs https://rxnav.nlm.nih.gov / https://uts.nlm.nih.gov
NLM Clinical Tables API https://clinicaltables.nlm.nih.gov
CMS GEMs (ICD-9↔ICD-10); AHRQ CCSR; OHDSI OMOP vocabularies https://athena.ohdsi.org