Structured Intelligence for
Unstructured Clinical Data
Aevum Zenth Clinical NLP Ontologies transform free-text EHR notes, trial documents, and medical literature into queryable, standardized knowledge graphs with sub-50ms latency.
Ontology Architecture
Our ontology engine maps clinical terminology across 7 major standards into a unified vector-semantic space. Context-aware resolution handles negation, uncertainty, temporal progression, and patient-specific modifiers with clinically validated precision.
Terminology Fusion
Real-time alignment of SNOMED-CT, LOINC, RxNorm, ICD-10/11, UMLS, and MedDRA into a single queryable graph.
Cross-MappingContext Resolution
Entity linking with negation detection, hypothetical framing, and historical vs. current status classification.
Clinical NERTemporal Graphing
Longitudinal event tracking with onset, resolution, recurrence, and medication-adherence modeling.
Time-AwarePerformance Specifications
| Metric | Value | Benchmark / Notes |
|---|---|---|
| Concept Coverage (UMLS) | 94.7% | vs. 2024 CRAFT benchmark |
| Inference Latency (10k tokens) | < 42ms | GPU-accelerated, v3.8.2 |
| F1 Score (Entity Extraction) | 0.931 | Multi-disease cohort validation |
| Throughput | 12,000 docs/hr | Single node, optimized batching |
| Supported Formats | FHIR R4, HL7 v2, JSON-LD, DICOM | Native adapters included |
| Update Cadence | Monthly + Real-time delta | Aligns with IHE & HL7 releases |
Integration & API
Access the ontology engine via REST, gRPC, or native SDKs. All endpoints return structured JSON with confidence scoring, source offsets, and mapped concept IDs.
Content-Type: application/json
{ "text": "Patient denies chest pain, no history of MI. Started lisinopril 10mg daily.", "standards": ["SNOMED-CT", "RxNorm"], "context": "encounter" }
SDK Support
- Python (PyPI:
aevum-clinical-nlp) - R (CRAN & Bioconductor)
- Java / Kotlin (Maven Central)
- FHIR R4 Bundle Generator
- GraphQL Schema for Knowledge Graph
Compliance & Security
Built for regulated healthcare environments. All inference pipelines are deployed within isolated VPCs with zero data retention by default. Audit trails, PII redaction, and role-based access are enforced at the gateway level.
Primary Use Cases
Real-World Evidence (RWE)
Extract patient cohorts, adverse events, and treatment pathways from unstructured EHR notes for observational studies.
Prior Authorization Automation
Auto-map clinical indications to payer criteria using standardized ontology codes and evidence strength scoring.
Clinical Trial Enrichment
Identify eligible candidates across enterprise health networks by resolving eligibility criteria against longitudinal records.
Medical Literature Mining
Ingest PubMed, clinical registries, and trial reports to build up-to-date knowledge graphs for CDS systems.
Deploy Clinical NLP Ontologies
Request sandbox access, download the technical whitepaper, or schedule an architecture review with our Health Sciences AI team.