You can deploy this model and start earning money today!

genzeonplatform/
healthcare-brain-vitals-ner

Name: healthcare-brain-vitals-ner
Availability: InStock

Views

No views yet

Healthcare Brain Vitals NER -- Vitals & Measurements Extraction by Genzeon Platform

Healthcare Brain Vitals NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of vital signs, body measurements, and physiological parameters from clinical text. Built on Bio_ClinicalBERT and fine-tuned on healthcare corpora, this model delivers production-grade entity recognition across 15 vital sign and measurement categories.

Model Details

Property	Value
Developed by	Genzeon Platforms
Base model	Bio_ClinicalBERT
Architecture	BertForTokenClassification
Parameters	~110M
Tagging scheme	BIO (31 labels)
Max sequence length	512 tokens
License	Apache-2.0

Intended Use

Healthcare Brain Vitals NER is designed for healthcare AI pipelines that need to extract structured vital sign data from unstructured clinical text. Primary use cases include:

Vital signs extraction -- automatically identifying blood pressure, heart rate, temperature, SpO2, and other vital measurements from nursing notes, ED triage notes, and progress notes.
Clinical data structuring -- converting free-text vital documentation into structured data for analytics and clinical decision support.
EHR data enrichment -- enhancing electronic health records with extracted measurement values and units.
Clinical research -- extracting vital sign trends from large corpora of clinical narratives for retrospective studies.

Entity Types

The model recognizes 15 vital sign and measurement entity types using BIO tagging (31 labels total):

Category	Entity Type	Description	Examples
Vital Signs	`BLOOD_PRESSURE`	Systolic/diastolic BP reading	120/80, BP 142/88 mmHg
Vital Signs	`HEART_RATE`	Heart rate / pulse	HR 76 bpm, pulse 88
Vital Signs	`RESPIRATORY_RATE`	Respiratory rate	RR 18, 22 breaths/min
Vital Signs	`TEMPERATURE`	Body temperature	98.6 F, 37.2 C, afebrile
Vital Signs	`SPO2`	Oxygen saturation	SpO2 97%, O2 sat 94% on RA
Body	`WEIGHT`	Patient weight	82.3 kg, 175 lbs
Body	`HEIGHT`	Patient height	175 cm, 5'10"
Body	`BMI`	Body mass index	BMI 26.9, 24.1 kg/m2
Scores	`PAIN_SCORE`	Pain assessment score	7/10, pain score 4
Scores	`GCS`	Glasgow Coma Scale	GCS 15, E4 V5 M6
Scores	`BLOOD_GLUCOSE`	Point-of-care glucose	245 mg/dL, glucose 110
Temporal	`VITAL_DATE`	Date of measurement	03/15/2024, hospital day 3
Temporal	`VITAL_TIME`	Time of measurement	0600, 14:30, this morning
Measurement	`MEASUREMENT_UNIT`	Unit of measurement	mmHg, bpm, kg, cm, F, C
Measurement	`MEASUREMENT_VALUE`	Numeric measurement value	120, 76, 98.6, 82.3

Performance

Overall Metrics

Metric	Precision	Recall	F1
Micro avg	0.9518	0.9573	0.9545
Macro avg	0.9441	0.9489	0.9464

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

Entity	Precision	Recall	F1	Support
BLOOD_PRESSURE	0.9734	0.9812	0.9773	1,847
HEART_RATE	0.9689	0.9756	0.9722	1,823
RESPIRATORY_RATE	0.9612	0.9678	0.9645	1,504
TEMPERATURE	0.9587	0.9634	0.9610	1,638
SPO2	0.9653	0.9701	0.9677	1,528
WEIGHT	0.9534	0.9589	0.9561	839
HEIGHT	0.9478	0.9412	0.9445	564
BMI	0.9423	0.9367	0.9395	531
PAIN_SCORE	0.9501	0.9545	0.9523	805
GCS	0.9312	0.9256	0.9284	415
BLOOD_GLUCOSE	0.9389	0.9423	0.9406	527
VITAL_DATE	0.9267	0.9345	0.9306	746
VITAL_TIME	0.9356	0.9412	0.9384	892
MEASUREMENT_UNIT	0.9487	0.9534	0.9510	1,612
MEASUREMENT_VALUE	0.9412	0.9478	0.9445	1,589

Usage

python

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_name = "genzeonplatform/healthcare-brain-vitals-ner"

# Option 1: Use the transformers pipeline (recommended)
nlp = pipeline("token-classification", model=model_name, aggregation_strategy="simple")
text = "Vitals: BP 132/84 mmHg, HR 76 bpm, RR 18, Temp 98.4 F, SpO2 97%. Pain 3/10."
entities = nlp(text)
for ent in entities:
    print(f"  {ent['entity_group']:20s} {ent['word']:30s} (score: {ent['score']:.3f})")

# Option 2: Manual inference
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

import torch
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=2)
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
for token, pred in zip(tokens, predictions[0]):
    label = model.config.id2label[str(pred.item())]
    if label != "O":
        print(f"  {token:20s} -> {label}")

Training Details

Developed by: Genzeon Platforms
Base model: Bio_ClinicalBERT (domain-specialized BERT for clinical text)
Training data: Genzeon Platform's proprietary clinical vital signs NER dataset
Epochs: 15 (with early stopping, patience=3)
Learning rate: 3e-5 (linear schedule with warmup)
Batch size: 16 (train) / 32 (eval)
Max sequence length: 512 tokens
Optimizer: AdamW (weight decay 0.01)
Best model selection: By entity-level F1 score

Limitations

English only: Currently optimized for English clinical text. Multilingual support is on the Genzeon Platforms roadmap.
Clinical context: Trained on clinical nursing notes, ED triage, and progress notes. Performance may vary on non-clinical text containing numbers.
Entity coverage: Covers 15 common vital sign and measurement types. Rare or specialty-specific measurements may require custom fine-tuning -- contact Genzeon Platform for enterprise support.
Context window: Limited to 512 tokens per input. Longer documents should be chunked with overlap for best results.

Related Genzeon Platforms models - Healthcare Brain NER

<Healthcare Brain NER is a 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 clinical text.>

About Genzeon Platforms

Genzeon Platforms 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 (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. 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 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.