--- language: - en license: apache-2.0 library_name: transformers tags: - transformers - safetensors - bert - ner - clinical - medical - healthcare - biomedical - bio-clinicalbert - named-entity-recognition - token-classification - phi - pii - de-identification - hipaa - ehr pipeline_tag: token-classification model-index: - name: healthcare-brain-ner results: - task: type: token-classification name: Named Entity Recognition metrics: - type: f1 value: 0.9695 name: F1 (Strict) - type: precision value: 0.9659 name: Precision - type: recall value: 0.9732 name: Recall --- # 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 | Property | Value | |----------|-------| | **Developed by** | **Genzeon Platforms** | | **Base model** | Bio_ClinicalBERT (emilyalsentzer/Bio_ClinicalBERT) | | **Architecture** | BERT Token Classification (BIO tagging) + Rule-based post-processing | | **Parameters** | ~110M | | **Tagging scheme** | BIO (41 labels) | | **Max sequence length** | 512 tokens | | **Framework** | HuggingFace Transformers | | **License** | Apache-2.0 | --- ## 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): | Category | Entity Type | Description | Examples | |----------|-------------|-------------|----------| | **Patient** | `PATIENT_NAME` | Patient full or partial name | John Smith, J. Doe, the patient | | **Patient** | `DATE_OF_BIRTH` | Date of birth | 03/15/1960, March 15 1960 | | **Patient** | `AGE` | Patient age | 64, 64-year-old, sixty-four | | **Patient** | `GENDER` | Gender or sex | male, female, M, F | | **Patient** | `SSN` | Social Security Number | 123-45-6789 | | **Patient** | `MRN` | Medical Record Number | 4478923, MRN#7721045 | | **Contact** | `PHONE` | Telephone number | (617) 555-0142, 617.555.0142 | | **Contact** | `FAX` | Fax number | (617) 555-0199 | | **Contact** | `EMAIL` | Email address | jane.doe@email.com | | **Location** | `ADDRESS` | Street address | 123 Main Street, 45 Elm Ave Apt 2B | | **Location** | `CITY` | City name | Boston, Springfield, New York | | **Location** | `STATE` | State or province | MA, Massachusetts, California | | **Location** | `ZIP` | ZIP or postal code | 02115, 60614-3200 | | **Location** | `COUNTRY` | Country name | USA, United States, Canada | | **Organization** | `HOSPITAL` | Healthcare facility name | Massachusetts General Hospital | | **Provider** | `DOCTOR_NAME` | Physician or provider name | Dr. Sarah Johnson, Dr. Chen | | **Digital** | `USERNAME` | System username or login | mchen_md, jdoe_nurse | | **Digital** | `ID_NUMBER` | Generic identifier | A-9928-BC, Policy #445231 | | **Digital** | `IP_ADDRESS` | IP address | 192.168.1.45, 10.0.0.1 | | **Digital** | `URL` | Web URL | https://mychart.hospital.org | > **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 | Metric | Precision | Recall | F1 | |--------|-----------|--------|-----| | **Micro avg** | 0.9659 | 0.9732 | 0.9695 | | **Macro avg** | 0.9609 | 0.9706 | 0.9656 | ### Per-Entity Metrics (Strict: Exact Span + Exact Type) | Entity | Precision | Recall | F1 | Support | |--------|-----------|--------|-----|---------| | PATIENT_NAME | 0.9817 | 0.9853 | 0.9835 | 3,247 | | DATE_OF_BIRTH | 0.9798 | 0.9740 | 0.9769 | 2,814 | | AGE | 0.9028 | 0.9354 | 0.9188 | 1,508 | | GENDER | 0.9596 | 0.9685 | 0.9640 | 1,562 | | SSN | 0.9713 | 0.9635 | 0.9674 | 766 | | MRN | 0.9838 | 0.9723 | 0.9780 | 1,943 | | PHONE | 0.9730 | 0.9669 | 0.9699 | 2,590 | | FAX | 0.9481 | 0.9354 | 0.9417 | 696 | | EMAIL | 0.9865 | 0.9836 | 0.9850 | 1,243 | | ADDRESS | 0.9546 | 0.9644 | 0.9595 | 1,985 | | CITY | 0.9086 | 0.8891 | 0.8988 | 2,047 | | STATE | 0.9103 | 0.9060 | 0.9082 | 2,734 | | ZIP | 0.9770 | 0.9632 | 0.9701 | 951 | | COUNTRY | 0.9485 | 0.9404 | 0.9444 | 2,056 | | HOSPITAL | 0.9033 | 0.9145 | 0.9089 | 2,267 | | DOCTOR_NAME | 0.9765 | 0.9691 | 0.9728 | 1,802 | | USERNAME | 0.9689 | 0.9431 | 0.9558 | 1,917 | | ID_NUMBER | 0.9724 | 0.9698 | 0.9711 | 2,555 | | IP_ADDRESS | 0.9792 | 0.9724 | 0.9758 | 926 | | URL | 0.9810 | 0.9747 | 0.9778 | 1,401 | --- ## Usage ```python 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 ```python 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 | Dataset | Split | Samples | Source | |---------|-------|---------|--------| | Synthetic Clinical De-id | train/dev/test | 10,000 / 1,250 / 1,250 | Template-based generation (150+ clinical templates) | | i2b2 2006 De-identification | train/test | — | [i2b2.org](https://www.i2b2.org/NLP/Deidentification/) (DUA required) | | i2b2 2014 De-identification | train/test | — | [i2b2.org](https://www.i2b2.org/NLP/Deidentification/) (DUA required) | | PhysioNet De-id Corpus | train/test | — | [PhysioNet](https://physionet.org/) (Credentialed DUA required) | **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](https://huggingface.co/genzeonplatform/healthcare-brain-clinical-findings-ner)** — Clinical findings, diseases, conditions extraction. 8 categories. - **[Healthcare Brain Medication NER](https://huggingface.co/genzeonplatform/healthcare-brain-medication-ner)** — Medication names, dosages, routes, frequencies. 12 categories. - **[Healthcare Brain Diagnosis NER](https://huggingface.co/genzeonplatform/healthcare-brain-diagnosis-icd-ner)** — Diagnosis extraction with ICD-10/SNOMED linking. 9 categories. - **[Healthcare Brain Laboratory NER](https://huggingface.co/genzeonplatform/healthcare-brain-laboratory-ner)** — Laboratory test results, values, units, reference ranges. 10 categories. - **[Healthcare Brain Vitals NER](https://huggingface.co/genzeonplatform/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](https://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 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**.