Clinical NLP entity recognition on medical documents

Clinical NLP Annotation by Licensed Physicians

Structured extraction from clinical narratives, discharge summaries, and operative notes. Physician-verified output your pipeline can ingest directly. No post-processing, no ambiguity, no crowd-worker guesswork.

NLP Annotation Capabilities

Named Entity Recognition (NER)

Medications, diagnoses, procedures, anatomical sites, and lab values extracted from clinical text with physician-level understanding of context and abbreviations.

Relation Extraction

Drug-disease, symptom-diagnosis, and procedure-outcome relationships identified from clinical narratives with semantic accuracy that reflects real clinical reasoning.

Discharge Summaries

Structured extraction of diagnoses, medications, follow-up plans, and clinical findings from discharge documentation, the most information-dense clinical text type.

Operative Notes

Procedure identification, surgical findings, complications, and implant details extracted from operative reports by surgeons who understand procedural terminology.

Clinical Coding

ICD-10, CPT, and SNOMED CT mapping from unstructured clinical text. Physician annotators bridge the gap between clinical language and standardized code systems.

Radiology Reports

Findings extraction, impression summarization, and follow-up recommendation classification from structured and semi-structured radiology narratives.

Why Physician-Led NLP Annotation Matters

Clinical text is not general-purpose text. A discharge summary contains dense, domain-specific language with abbreviations, implied negation, temporal reasoning, and context-dependent meaning. The phrase "denies chest pain" contains a negation that changes the entire clinical picture. "CA" means cancer in oncology notes and calcium in a metabolic panel. "Unremarkable" is a finding, not the absence of one.

Our physicians understand the medicine behind the text. A cardiologist extracting cardiac findings from an echo report recognizes that "preserved EF" means something fundamentally different than "reduced EF", and annotates the relationship between ejection fraction and clinical significance, not just the surface-level text span.

This clinical depth is why LabelCore annotations produce NLP models that perform in production, not just on benchmarks. When your training data is annotated by people who have written the same types of notes themselves, the resulting model inherits clinical reasoning, not pattern matching.

Quality Tiers

Tier	Accuracy	Method	Best For
Gold Standard	99%+	Physician-only, dual review	FDA submissions, clinical trials, published research
Hybrid Intelligence	93%+	AI pre-annotation + physician review	Production NLP models, clinical NER, relation extraction
Ready Datasets	85%+	Pre-labeled, physician-sampled QA	Prototyping, feasibility studies, model benchmarking

Frequently Asked Questions

What clinical document types can you annotate?+

We annotate the full range of clinical documentation: EHR notes, discharge summaries, operative notes, radiology reports, pathology reports, progress notes, nursing notes, referral letters, and consultation reports. Our physicians are experienced across all major clinical text types and can adapt to custom document formats used by your institution or EHR system.

Do physicians do the NLP annotation?+

Yes. Every annotation is performed or verified by a licensed physician with clinical experience. Our annotators understand context, abbreviations, implied negation, and medical terminology at a level that non-clinical annotators cannot match. This is critical for tasks like NER where "CA" could mean calcium, cancer, or California depending on context.

What NLP annotation schemas do you support?+

We support all major annotation formats including custom schemas, BRAT standoff format, CoNLL (BIO/BILOU tagging), spaCy training format, and custom JSON/XML schemas. If you have an existing annotation guideline, we adopt it. If you need one built, our team designs schemas optimized for your downstream NLP pipeline.

How do you handle de-identification?+

We offer two workflows: annotation on pre-de-identified data (your team removes PHI before sharing), or HIPAA-compliant PHI removal as part of our pipeline before annotation begins. All data handling follows HIPAA Security Rule requirements with BAA agreements, encrypted transfer, and access-controlled annotation environments.

Can NLP annotations train large language models?+

Yes. Physician-annotated clinical text provides the highest-quality training data for medical LLMs, clinical decision support systems, and domain-specific fine-tuning. Our structured annotations serve as gold-standard supervision signals for tasks like clinical entity extraction, relation classification, and clinical text generation.

Start Your Clinical NLP Project

Tell us about your clinical text, annotation schema, and timeline. We'll match you with physicians who specialize in your document type and deliver production-ready annotations.

Book a Consultation Email sales@labelcore.ai