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genzeonplatform/
healthcare-brain-procedure-surgery-ner

Name: healthcare-brain-procedure-surgery-ner
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Healthcare Brain Procedure Surgery NER — Procedure & Surgery Entity Extraction by Genzeon Platforms

Healthcare Brain Procedure Surgery NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of surgical procedures, diagnostic tests, interventions, and procedural details from unstructured clinical text. Built on Bio_ClinicalBERT and fine-tuned on clinical procedural corpora, this model delivers production-grade entity recognition across 11 procedure and surgery entity categories.

Model Details

Property	Value
Developed by	Genzeon Platforms
Base model	Bio_ClinicalBERT (emilyalsentzer/Bio_ClinicalBERT)
Architecture	BERT Token Classification (BIO tagging) + Rule-based CPT/date extraction
Parameters	~110M
Tagging scheme	BIO (23 labels)
Max sequence length	512 tokens
Framework	HuggingFace Transformers
License	Apache-2.0

Intended Use

Healthcare Brain Procedure Surgery NER is designed for healthcare AI pipelines that need to extract structured procedural information from unstructured clinical text. Primary use cases include:

Operative report parsing — extracting procedure names, surgical approaches, anesthesia types, and outcomes from operative notes.
Procedure tracking — structuring procedure dates, statuses, and CPT codes for billing and quality reporting.
Surgical device surveillance — identifying devices and implants used during procedures for post-market surveillance.
Clinical research — extracting procedural data from large clinical corpora for outcomes research and surgical quality improvement.
Prior authorization support — extracting procedure details to support automated prior authorization workflows.

Entity Types

The model recognizes 11 procedure and surgery entity types using BIO tagging (23 labels total):

Category	Entity Type	Description	Examples
Procedure	`PROCEDURE_NAME`	Name of the surgical or diagnostic procedure	total knee replacement, colonoscopy, CABG
Type	`SURGERY_TYPE`	Classification of surgery urgency/category	elective, emergent, urgent, minimally invasive
Date	`PROCEDURE_DATE`	When the procedure was performed or scheduled	03/15/2024, intraoperatively, post-op day 2
Status	`PROCEDURE_STATUS`	Current status of the procedure	completed, scheduled, cancelled, in progress
Outcome	`PROCEDURE_OUTCOME`	Result or outcome of the procedure	successful, uncomplicated, failed, aborted
Site	`PROCEDURE_SITE`	Anatomical location of the procedure	left knee, right upper lobe, abdomen
Approach	`SURGICAL_APPROACH`	Surgical technique or access method	laparoscopic, open, robotic, endoscopic
Anesthesia	`ANESTHESIA_TYPE`	Type of anesthesia administered	general, spinal, epidural, local, MAC
CPT	`CPT_CODE`	CPT procedure code	27447, 43239, 33533
Device	`DEVICE_USED`	Surgical device or instrument used	harmonic scalpel, da Vinci system, 14-French catheter
Implant	`IMPLANT_NAME`	Implanted device or prosthesis	Zimmer NexGen, Medtronic pacemaker, titanium plate

Note: External dataset loaders (MIMIC-III PROCEDURES, i2b2 2010 Treatment entities) are architecturally supported and included in this release. These datasets require Data Use Agreements from PhysioNet and i2b2.org respectively. Contact Genzeon Platforms for enterprise models trained with full real-world clinical data coverage.

Performance

Overall Metrics

Metric	Precision	Recall	F1
Micro avg	0.9356	0.9404	0.9380
Macro avg	0.9291	0.9268	0.9278

Per-Entity Metrics (Strict: Exact Span + Exact Type)

Entity	Precision	Recall	F1	Support
PROCEDURE_NAME	0.9578	0.9612	0.9595	1,342
PROCEDURE_SITE	0.9534	0.9567	0.9550	1,187
SURGICAL_APPROACH	0.9498	0.9523	0.9510	934
PROCEDURE_STATUS	0.9467	0.9501	0.9484	876
PROCEDURE_OUTCOME	0.9423	0.9456	0.9439	812
ANESTHESIA_TYPE	0.9389	0.9412	0.9400	789
SURGERY_TYPE	0.9356	0.9378	0.9367	734
PROCEDURE_DATE	0.9312	0.9287	0.9299	698
DEVICE_USED	0.9198	0.9134	0.9166	523
CPT_CODE	0.9067	0.8989	0.9028	467
IMPLANT_NAME	0.8823	0.8687	0.8754	312

Usage

python

from transformers import pipeline

# Load the model
nlp = pipeline(
    "token-classification",
    model="genzeonplatform/healthcare-brain-procedure-surgery-ner",
    aggregation_strategy="simple",
)

# Process clinical text
text = """OPERATIVE REPORT: Patient underwent elective laparoscopic cholecystectomy
on 03/15/2024 under general anesthesia. CPT: 47562. A harmonic scalpel was used.
Procedure completed without complication. Site: right upper quadrant."""

entities = nlp(text)
for ent in entities:
    print(f"  [{ent['entity_group']:25s}] {ent['word']} (score: {ent['score']:.3f})")

Output:

  [PROCEDURE_NAME           ] laparoscopic cholecystectomy (score: 0.958)
  [SURGERY_TYPE              ] elective (score: 0.942)
  [PROCEDURE_DATE            ] 03/15/2024 (score: 0.935)
  [ANESTHESIA_TYPE           ] general anesthesia (score: 0.945)
  [CPT_CODE                  ] 47562 (score: 0.910)
  [DEVICE_USED               ] harmonic scalpel (score: 0.925)
  [PROCEDURE_STATUS          ] completed (score: 0.950)
  [PROCEDURE_OUTCOME         ] without complication (score: 0.940)
  [PROCEDURE_SITE            ] right upper quadrant (score: 0.952)

Structured Output

python

from src.inference.predictor import ProcedureSurgeryPredictor

predictor = ProcedureSurgeryPredictor("genzeonplatform/healthcare-brain-procedure-surgery-ner")
text = "Patient underwent laparoscopic cholecystectomy under general anesthesia. CPT: 47562."
procedures = predictor.extract_procedures(text)
for p in procedures:
    print(f"  {p['procedure_name']}: approach={p['surgical_approach']}, anesthesia={p['anesthesia_type']}")

Training Details

Developed by: Genzeon Platforms
Base model: Bio_ClinicalBERT (clinical domain BERT, pre-trained on MIMIC-III clinical notes)
NER architecture: BertForTokenClassification (768 → 23 linear head)
Training data: Synthetic clinical procedure corpus (125+ templates)
Epochs: 15 (early stopping, patience=3)
Learning rate: 3e-5 (linear schedule with warmup, 10% warmup ratio)
Batch size: 16 (train) / 32 (eval)
Optimizer: AdamW (weight decay 0.01, gradient clipping 1.0)
Max sequence length: 512 tokens
Best model selection: By entity-level F1 score
Seed: 42

Training Data

Dataset	Split	Samples	Source
Synthetic Clinical Procedures	train/dev/test	8,000 / 1,000 / 1,000	Template-based generation (125+ clinical templates)
MIMIC-III PROCEDURES	train/test	—	PhysioNet (Credentialed DUA required)
i2b2 2010 Treatment Entities	train/test	—	i2b2.org (DUA required)

Entity mapping: MIMIC-III PROCEDURES table and operative notes provide structured procedure data for distant supervision. i2b2 2010 Treatment entities provide annotated examples of procedure-related mentions in clinical narratives.

Limitations

English only: Currently optimized for English clinical and biomedical text. Multilingual support is on the Genzeon Platforms roadmap.
Synthetic training bias: Primarily trained on template-generated data. Performance on highly variable real-world clinical documentation may differ — contact Genzeon Platforms for enterprise models fine-tuned with restricted clinical datasets (MIMIC-III, i2b2).
CPT code recognition: CPT code extraction handles standard 5-digit format but does not validate code-to-procedure mapping. Rule-based post-processing supplements ML predictions for CPT extraction.
Human-in-the-loop recommended: For clinical decision-making and patient safety workflows, pair model predictions with expert clinician review.

Related Genzeon Platforms Models

Healthcare Brain NER — PHI/PII detection and de-identification. 20 PHI categories.
Healthcare Brain Clinical Findings NER — Clinical findings, diseases, conditions extraction. 8 categories.
Healthcare Brain Medication NER — Medication names, dosages, routes, frequencies. 12 categories.
Healthcare Brain Diagnosis NER — Diagnosis extraction with ICD-10/SNOMED linking. 9 categories.
Healthcare Brain Laboratory NER — Laboratory test results, values, units, reference ranges. 10 categories.
Healthcare Brain Vitals NER — Vital signs extraction including BP, HR, SpO2, temperature. 8 categories.

About Genzeon Platforms

Genzeon Platforms is a healthcare technology company that is building the agentic AI decision infrastructure for healthcare. The company builds the Healthcare Brain — three production platforms (HIP One, PES One, CPS One) on a patented multi-agent substrate called Aether One™.

Production Deployment

Genzeon Platforms is a participant in the CMS WISeR Innovation Model (2026–2031), operating Medicare FFS prior authorization in New Jersey under MAC JL via Novitas Solutions. Live since January 1, 2026.

Q1 2026 production results:

15k+ cases processed
100% three-day TAT compliance
Zero auto-denials (every non-affirmation signed by a named licensed clinician)
42% reviewer productivity gain
Sub-three-minute median decision latency
85% portal channel adoption

Scale

50+ payer and provider clients across the Genzeon Platforms
1M+ Medicare FFS members served under WISeR

Patent Portfolio

12 USPTO provisional applications filed covering the Aether One™ architecture
Coverage: multi-agent orchestration, atomic criteria decomposition, knowledge containment, dual-channel pharmacy benefit prior authorization, agentic knowledge pack specification, ambient agent integration, and related primitives
~346 claims locked at provisional priority dates
USPTO portfolio anchor #226167

Compliance Posture

SOC 2 Type II
HIPAA compliant
Operates inside the customer perimeter
Supports on-premises, sovereign-cloud, and air-gapped deployments via the Knowledge Containment Architecture (KCA) reference design

Partnerships

10-year Microsoft partnership (5 partner designations, Microsoft Healthcare Agent Service integration, Dragon Copilot extension)
UiPath Platinum (Top 3 HLS)
Available on:
- Azure Marketplace
- AWS Marketplace
- Google Cloud Marketplace
- Salesforce AppExchange

Open Specifications

Genzeon Platforms publishes the Aether Knowledge Pack Specification (AKPS). AKPS enables healthcare coverage policies to be authored as structured markdown that is directly consumable as LLM prompt context.

See: github.com/genzeon/aether-akps

Model Policy

Genzeon Platforms builds on US- and EU-origin open-weight foundation models only (Llama, Gemma, Mistral families) for healthcare and federal deployment contexts. No Chinese-origin models are used in production, position papers, or patent dependent claims.

Headquarters

Exton, Pennsylvania, USA

Genzeon Platforms is a Genzeon company.

Where to Find More

Resource	Link
Company website	https://genzeon.one
Healthcare Brain overview	https://genzeon.one/healthcare-brain
HIP One (clinical reasoning / prior auth)	https://genzeon.one/hip-one
PES One (patient & member engagement)	https://genzeon.one/pes-one
CPS One (AI governance & compliance)	https://genzeon.one/cps-one
Aether One™ architecture	https://genzeon.one/aether-one
Patents	https://genzeon.one/patents
WISeR production deployment	https://genzeon.one/wiser
AKPS open spec	https://github.com/genzeon/aether-akps
Security & trust	https://genzeon.one/security
LinkedIn	https://www.linkedin.com/company/117124252
Contact	https://genzeon.one/contact

Citation

If you use this model or reference Genzeon Platforms in academic, regulatory, or industry work, please cite:

Genzeon Platforms (2026). Healthcare Brain Procedure Surgery NER is part of Genzeon Platform's suite of healthcare AI tools designed to accelerate clinical research and improve patient care.

For enterprise licensing, custom fine-tuning, or integration support, contact hi@genzeon.one.