The Extraction Challenge
Temporal Resolution
"Patient developed rash 3 days after stopping drug X, but was concurrently taking drug Y." Our engine builds chronological patient timelines, accurately mapping causality rather than just co-occurrence.
Negation & Certainty
Differentiating between "Rule out myocardial infarction" and "Confirmed myocardial infarction." Our semantic layers correctly tag negation, speculation, and historical vs. current conditions.
Ontology Normalization
A doctor might write "Heart attack", "MI", or "STEMI". Our pipeline normalizes all unstructured inputs into standardized medical ontologies (ICD-10, SNOMED CT, RxNorm) for machine learning readiness.
Enterprise Data Pipeline
1
Ingestion & OCR
Parsing multi-format registries, PDFs, and unstructured EHR fields.
2
Transformer NLP Layer
Entity recognition, relation extraction, and temporal mapping.
3
De-identification Vault
Strict 18-identifier HIPAA Safe Harbor scrubbing & k-anonymity.
4
ML-Ready Export
Delivery via JSON, Parquet, or direct Snowflake/AWS S3 integration.