Mapping To Snomed

Mapping OpenMed spans to SNOMED CT

Ground clinical concept spans that OpenMed extracts — disorders, findings, procedures, body structures, substances — to SNOMED CT, the comprehensive clinical reference terminology. The atom is the SCTID (a SNOMED CT concept identifier), organized into a description-logic hierarchy you can query with ECL (Expression Constraint Language).

Hard licensing boundary — read first. SNOMED CT is license-restricted. OpenMed and this skill never bundle, ship, cache, or redistribute any SNOMED CT content. All mapping happens out-of-process against a terminology server the user supplies and is licensed for — their own Ontoserver, Snowstorm, the NLM's UTS/UMLS FHIR endpoint, or a national release server. SNOMED International requires an Affiliate License (free in member territories like the US via the NLM; check your country). Your code receives a base URL + credentials from the user; it must work with any compliant FHIR terminology server and store nothing but the returned codes.

When to use

• You need rich, hierarchy-aware clinical codes (more granular than ICD-10) for problems, procedures, or body sites.
• You want to translate an existing code (ICD-10-CM, local code) to SNOMED CT via a ConceptMap/$translate.
• You need subsumption/ECL queries ("is this a descendant of Diabetes mellitus?") for cohorting or decision support.

For billing codes use coding-icd10; for drugs normalizing-rxnorm; for labs mapping-loinc. SNOMED CT is the clinical-meaning layer.

Quick start (user-supplied FHIR terminology server)

Configuration is injected, never hardcoded. The operations are standard FHIR R4.

pythonCopy code
import os, requests

# Provided by the USER — their licensed server. Nothing bundled.
TX = os.environ["FHIR_TX_URL"]              # e.g. https://snowstorm.example.org/fhir
TOKEN = os.environ.get("FHIR_TX_TOKEN")     # if the server requires auth
SNOMED = "http://snomed.info/sct"
HDRS = {"Accept": "application/fhir+json"}
if TOKEN:
    HDRS["Authorization"] = f"Bearer {TOKEN}"

def lookup(code: str) -> dict:
    """$lookup: fully specified name + properties for an SCTID."""
    r = requests.get(f"{TX}/CodeSystem/$lookup",
                     params={"system": SNOMED, "code": code},
                     headers=HDRS, timeout=15)
    r.raise_for_status()
    return r.json()

def find_concepts(text: str, ecl: str = "<<404684003", count: int = 10):
    """Text search constrained by ECL (default: descendants of Clinical finding)."""
    vs = f"{SNOMED}?fhir_vs=ecl/{ecl}"
    r = requests.get(f"{TX}/ValueSet/$expand",
                     params={"url": vs, "filter": text, "count": count},
                     headers=HDRS, timeout=20)
    r.raise_for_status()
    return r.json().get("expansion", {}).get("contains", [])

def translate(code: str, source_system: str, conceptmap_url: str):
    """$translate an existing code to SNOMED CT via a ConceptMap."""
    r = requests.get(f"{TX}/ConceptMap/$translate",
                     params={"url": conceptmap_url, "system": source_system,
                             "code": code, "targetsystem": SNOMED},
                     headers=HDRS, timeout=20)
    r.raise_for_status()
    return r.json()

# ECL examples: 64572001=disease, 71388002=procedure, 123037004=body structure
print(find_concepts("type 2 diabetes", ecl="<<64572001"))

Workflow

1. Extract spans with OpenMed (Disease, Anatomy, Pharmaceutical models).
2. Pick a semantic constraint (ECL) from the OpenMed label so you search the right hierarchy: disorder span → <<64572001; anatomy span → <<123037004; substance/drug → <<105590001; procedure → <<71388002.
3. Search with ValueSet/$expand?filter=<span> under that ECL.
4. Rank & disambiguate by display match and confidence; prefer the most specific concept whose meaning is fully entailed by the text (do not over-code).
5. Validate with $validate-code; $lookup to capture the FSN and any needed properties.
6. Translate instead of searching when you already hold an ICD-10/local code and the user's server has the relevant ConceptMap.
7. Emit {system: "http://snomed.info/sct", code, display} — the SCTID plus the OpenMed source offsets for traceability.

Hand-off from OpenMed

openmed.analyze_text(..., output_format="dict") returns entities, each a dict with text, label, confidence, start, end. Route each label to an ECL hierarchy and map out-of-process:

pythonCopy code
import openmed

note = "Assessment: type 2 diabetes mellitus with diabetic nephropathy."
result = openmed.analyze_text(
    note,
    model_name="disease_detection_superclinical",   # Disease category
    output_format="dict",
)

ECL_FOR_LABEL = {
    "DISEASE":   "<<64572001",     # | Disease |
    "CONDITION": "<<64572001",
    "PATHOLOGY": "<<64572001",
    "ANATOMY":   "<<123037004",    # | Body structure |
    "ORGAN":     "<<123037004",
}

for ent in result["entities"]:
    ecl = ECL_FOR_LABEL.get(ent["label"], "<<404684003")  # fallback: Clinical finding
    candidates = find_concepts(ent["text"], ecl=ecl, count=5)
    print(ent["text"], ent["start"], ent["end"], "->",
          [(c["code"], c["display"]) for c in candidates[:3]])

Carry OpenMed's start/end offsets next to each SCTID so every code is auditable back to its span. Persist codes and offsets only — never the raw note, and never a local copy of SNOMED content.

Edge cases & gotchas

• Never bundle SNOMED CT. Do not vendor a release, embed an export, or cache descriptions to disk for reuse. If you find yourself shipping SNOMED data, stop — the design must call the user's licensed server live, out-of-process.
• Affiliate licensing. Confirm the user holds (or their territory grants) a SNOMED International Affiliate License. In the US it is free via the NLM/UMLS; elsewhere it varies. Surface this requirement; do not assume entitlement.
• Pre- vs post-coordination. Some clinical meanings need a post-coordinated expression (e.g. finding + body site + severity). Prefer a single pre-coordinated concept when one exists; only post-coordinate when your server and downstream systems support SNOMED CT expressions.
• Edition/version drift. SCTIDs are stable but content differs across editions (International vs US vs UK) and monthly releases. Record the edition the server reports; do not mix codes across editions silently.
• Negation/uncertainty stays in OpenMed. A span "no evidence of pneumonia" must not be coded as present pneumonia. Resolve assertion/negation with OpenMed's clinical-context layer before mapping.
• Don't over-specify. Map to the concept actually supported by the text; inventing severity or laterality the note never stated is a coding error.
• Local-first. OpenMed NER runs on-device; only de-identified concept strings reach the terminology server. No PHI over the wire.

Standards & references

• SNOMED CT (SNOMED International): https://www.snomed.org/
• SNOMED CT licensing & Affiliate program: https://www.snomed.org/get-snomed
• NLM SNOMED CT (US, via UMLS/UTS): https://www.nlm.nih.gov/healthit/snomedct/index.html
• Expression Constraint Language (ECL): https://confluence.ihtsdotools.org/display/DOCECL
• FHIR $translate / $lookup / $validate-code: https://hl7.org/fhir/terminology-service.html
• Snowstorm (reference terminology server): https://github.com/IHTSDO/snowstorm
• Ontoserver: https://ontoserver.csiro.au/