Healthcare Brain NER — PHI/PII De-identification by Genzeon Platforms

Healthcare Brain NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated detection and de-identification of Protected Health Information (PHI) and Personally Identifiable Information (PII) in unstructured clinical text. Built on Bio_ClinicalBERT and fine-tuned on clinical de-identification corpora, this model delivers production-grade entity recognition across 20 PHI/PII categories.

Model Details

Intended Use

Healthcare Brain NER is designed for enterprise healthcare environments where patient data privacy is critical. Primary use cases include:

• Clinical text de-identification — removing or masking patient identifiers before sharing medical records for secondary use, research, and quality improvement.
• PII detection — flagging sensitive information in healthcare documents, EHRs, discharge summaries, and referral letters.
• HIPAA Safe Harbor compliance — supporting automated de-identification workflows aligned with HIPAA Safe Harbor and Expert Determination methods.
• Healthcare AI pipelines — preprocessing clinical text for downstream NLP tasks while ensuring patient privacy.
• Clinical research — de-identifying large clinical corpora for retrospective studies, registry analysis, and outcomes research.

Entity Types

The model recognizes 20 PHI/PII entity types using BIO tagging (41 labels total):

Note: External dataset loaders (i2b2 2006/2014 de-identification, PhysioNet de-id corpus) are architecturally supported and included in this release. These datasets require Data Use Agreements from i2b2.org and PhysioNet respectively. Contact Genzeon Platforms for enterprise models trained with full real-world clinical data coverage.

Performance

Overall Metrics

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

Usage

from transformers import pipeline

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

# Process clinical text
text = """Patient John Smith, DOB 03/15/1960, age 64, male.
MRN: 4478923. SSN: 123-45-6789. Admitted to Massachusetts General
Hospital by Dr. Sarah Johnson on 01/10/2025."""

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

Output:

  [PATIENT_NAME             ] John Smith (score: 0.982)
  [DATE_OF_BIRTH            ] 03/15/1960 (score: 0.978)
  [AGE                      ] 64 (score: 0.915)
  [GENDER                   ] male (score: 0.961)
  [MRN                      ] 4478923 (score: 0.984)
  [SSN                      ] 123-45-6789 (score: 0.972)
  [HOSPITAL                 ] Massachusetts General Hospital (score: 0.908)
  [DOCTOR_NAME              ] Dr. Sarah Johnson (score: 0.975)
  [DATE_OF_BIRTH            ] 01/10/2025 (score: 0.974)

De-identification Output

from src.inference.predictor import PHIPredictor

predictor = PHIPredictor("genzeonplatform/healthcare-brain-ner")
text = "Patient John Smith, DOB 03/15/1960, was seen at Springfield General Hospital."
results = predictor.deidentify(text)
print(results["deidentified_text"])
# -> "Patient [PATIENT_NAME], DOB [DATE_OF_BIRTH], was seen at [HOSPITAL]."

Training Details

• Developed by: Genzeon Platforms
• Base model: Bio_ClinicalBERT (clinical domain BERT, pre-trained on MIMIC-III clinical notes)
• NER architecture: BertForTokenClassification (768 → 41 linear head)
• Training data: Synthetic clinical de-identification corpus (150+ templates) + augmented clinical note formats
• 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

Entity mapping: i2b2 2006/2014 de-identification shared tasks provide gold-standard annotated clinical narratives with PHI labels. PhysioNet de-id corpus provides additional real-world clinical text with de-identification annotations.

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 (i2b2, PhysioNet).
• Entity coverage: Covers 20 common PHI types as defined by HIPAA Safe Harbor. Rare or domain-specific identifiers (biometric data, vehicle serial numbers) may require custom fine-tuning.
• Context window: Limited to 512 tokens per input. Longer documents should be chunked with overlap for best results.
• Human-in-the-loop recommended: For high-stakes de-identification workflows and patient safety, pair model predictions with expert clinician review.

Related Genzeon Platforms Models

• 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, body measurements, physiological parameters. 15 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

Citation

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

Genzeon Platforms (2026). Healthcare Brain 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.