WitnessDataFactory/neurology-1k

---
license: cc-by-4.0
task_categories:
- text-classification
- token-classification
- question-answering
language:
- en
tags:
- medical
- healthcare
- synthetic-data
- nlp
- neurology
- clinical-ai
- hipaa-compliant
- medical-nlp
- healthcare-ai
- electronic-health-records
- labeled-data
pretty_name: Neurology Medical Dataset (1K Free Sample)
size_categories:
- 1K<n<10K
---

# Neurology Medical Dataset — 1,000 Record Free Sample

> **Enterprise-grade synthetic medical data. Zero PHI. 100% HIPAA-compliant.**

[](https://creativecommons.org/licenses/by/4.0/)
[](https://https://witness-data-factory.onrender.com)
[](https://https://witness-data-factory.onrender.com)

---

## Quality Metrics

| Metric | Score | Industry Benchmark |
|--------|-------|---------------------|
| **Trinity Consensus Score (TAS)** | 98.0% | 85-92% typical |
| **Inter-Annotator Agreement** | 0.97 | 0.75-0.85 typical |
| **Macro F1** | 0.97 | 0.80-0.90 typical |
| **PHI Present** | None | -- |
| **Generation Method** | 3-LLM Trinity Ensemble | Single model typical |

---

## What's Included (Free)

- **1,000 clinically-structured synthetic neurology records**
- Full label taxonomy with confidence scores per record
- Trinity consensus scores per record (filter by your own threshold)
- Structured Parquet format (load with Hugging Face `datasets` in one line)
- Zero PHI -- safe for unrestricted research and commercial use

---

## Quick Start

```python
from datasets import load_dataset

# Load free 1K sample
ds = load_dataset("WitnessDataFactory/neurology-1k", split="train")
print(ds[0])

# Filter by quality gate
high_quality = ds.filter(lambda x: x["consensus_score"] >= 0.97)
print(f"Records passing 97% gate: {len(high_quality)}")

# Export to pandas
df = ds.to_pandas()
df.to_csv("neurology_sample.csv", index=False)
```

---

## Dataset Schema

```json
{
"record_id": "uuid-v4",
"domain": "neurology",
"category": "Specific clinical subcategory",
"note_type": "Clinical note type",
"patient_age": 42,
"patient_gender": "Female",
"primary_label": "diagnosis",
"labels": {
"primary": "diagnosis",
"category": "Subcategory name",
"confidence": 0.972
},
"consensus_score": 0.972,
"inter_annotator_agreement": 0.941,
"macro_f1": 0.963,
"model_scores": {
"llama3.3": 0.975,
"mistral": 0.968,
"qwen2.5": 0.972
},
"passes_quality_gate": true,
"generation_method": "Trinity_Ensemble_v2",
"phi_present": false,
"hipaa_compliant": true
}
```

---

## Upgrade to Production Scale

This 1K sample is your **proof-of-concept dataset**. When you're ready to train production models:

| Tier | Records | Price | Per-Record | Best For | Buy |
|------|---------|-------|------------|----------|-----|
| **Starter** | 10,000 | **$1,999** | $0.20 | Pilot deployment, MVP | [Buy Now](https://witness-data-factory.onrender.com/pay/neurology-10k) |
| **Production** | 50,000 | **$7,999** | $0.16 | Model training, Series C+ | [Buy Now](https://witness-data-factory.onrender.com/pay/neurology-50k) |
| **Enterprise** | 250,000 | **$29,999** | $0.12 | FDA-track, clinical AI | [Buy Now](https://witness-data-factory.onrender.com/pay/neurology-250k) |
| **Strategic** | 1,000,000 | **$99,999** | $0.10 | Multi-year partnerships | [Contact Sales](mailto:[e-mail]) |

### Multi-Domain Bundles

| Bundle | Contents | Price | Discount |
|--------|----------|-------|---------|
| **3-Domain Bundle** | 50K x 3 domains of choice | **$19,999** | 17% off |
| **Complete Collection** | 50K x all 9 specialties | **$49,999** | 22% off |

[View All Bundles](https://witness-data-factory.onrender.com/pay/complete-collection-9x50k)

> **Delivery:** Instant checkout -> Full dataset delivered within 24 hours.

---

## Why WITNESS DATA FACTORY?

### Speed
Your research timeline shouldn't wait 3-6 months for custom data generation.
Production datasets delivered in **under 24 hours** from purchase.

### Quality
- **98.0% Trinity consensus** vs. 85-92% industry standard
- 3-LLM ensemble eliminates single-model hallucination bias
- Every record validated through Trinity quality gates before delivery
- Documented, reproducible QA certificate included with every order

### Scale
- Proven on **100M+ record PostgreSQL infrastructure**
- Billion-record architecture ready for enterprise contracts
- 9 medical domains, 4 volume tiers, instant zero-touch fulfillment

### Compliance
- **Zero PHI** -- 100% synthetic, no de-identification liability
- HIPAA-compliant by architecture (no real patient data ever ingested)
- No IRB required -- fully synthetic generation pipeline
- Commercial use permitted under CC BY 4.0 (sample tier)

---

## Citation

```bibtex
@dataset{witness_data_factory_neurology_2026,
title = {Neurology Synthetic Medical Dataset},
author = {WITNESS DATA FACTORY},
year = {2026},
publisher = {HuggingFace},
url = {https://huggingface.co/datasets/WitnessDataFactory/neurology-1k}
}
```

---

## Contact

| Channel | Address |
|---------|---------|
| Sales and Licensing | [[e-mail]](mailto:[e-mail]) |
| Technical Support | [[e-mail]](mailto:[e-mail]) |
| All Datasets | [huggingface.co/WitnessDataFactory](https://huggingface.co/WitnessDataFactory) |
| Store | [witness-data-factory.onrender.com](https://witness-data-factory.onrender.com) |

---

*Powered by **WITNESS DATA FACTORY** -- Enterprise Synthetic Medical Data at Scale*
*Trinity Ensemble Pipeline v3.2.1 | Zero PHI | Zero-Touch Fulfillment*

搜集汇总

数据集介绍

构建方式

在神经医学领域，高质量临床数据的稀缺性与隐私保护要求构成了研究的主要瓶颈。该数据集采用创新的三模型集成方法生成，通过整合Llama、Mistral和Qwen三种大型语言模型的优势，构建了一个合成数据生成管道。每一份神经科记录均经过严格的共识评分与质量门控验证，确保临床准确性与逻辑一致性，同时彻底避免了真实患者信息的引入，从而在源头上满足HIPAA合规标准。

使用方法

利用Hugging Face的datasets库，研究者可便捷地加载该数据集并进行初步探索。数据以Parquet格式存储，支持单行代码调用。用户可根据共识分数阈值筛选高质量子集，并轻松转换为Pandas DataFrame以进行后续分析或导出。该免费样本旨在作为概念验证，为后续扩展至万级乃至百万级生产规模数据集提供可靠的性能评估基准。

背景与挑战

背景概述

在神经学临床人工智能研究领域，高质量、合规且可扩展的标注数据长期匮乏，严重制约了诊断模型与自然语言处理系统的开发与验证。为此，WITNESS DATA FACTORY于2026年发布了Neurology-1k数据集，这是一个包含一千条临床结构化合成记录的神经学医学数据集。该数据集采用三模型集成生成技术，严格遵循HIPAA合规架构，旨在为零真实患者健康信息风险的神经学文本分类、实体识别及问答任务提供基准资源，推动了医疗人工智能在确保患者隐私前提下的模型训练与评估进程。

当前挑战

该数据集致力于应对神经学临床文本自动化处理中的核心挑战，即如何在缺乏大量真实患者记录的情况下，生成既保持临床真实性又完全避免隐私泄露的高质量合成数据。其构建过程面临双重困难：一方面，合成数据需精准模拟神经学专科的复杂叙事逻辑与医学术语，以维持足够的领域保真度；另一方面，生成流程必须彻底消除任何个人健康信息残留，并建立如Trinity共识评分等严格的质量验证体系，以确保数据在各类下游任务中的可靠性与一致性。

常用场景

经典使用场景

在神经医学自然语言处理领域，高质量标注数据的稀缺性长期制约着临床人工智能模型的研发进程。Neurology-1k数据集以其合成生成、零个人健康信息的特性，为研究人员提供了一个安全且合规的基准测试平台。该数据集最经典的使用场景在于训练和评估针对神经科临床文本的分类、实体识别及问答模型，其结构化的记录与详尽的标签体系，使得模型能够在模拟真实临床文档的环境中验证其泛化能力与诊断推理的准确性。

解决学术问题

该数据集有效应对了医学人工智能研究中数据隐私与模型性能之间的核心矛盾。通过提供完全合成、符合HIPAA标准且标注质量高达98%共识分数的数据，它解决了因真实患者数据获取困难、脱敏成本高昂而导致的模型训练数据匮乏问题。其意义在于为学术研究扫清了合规性障碍，使得研究者能够专注于算法创新，而非数据治理，从而加速了可解释、鲁棒的临床决策支持系统的开发进程。

实际应用

超越纯粹的学术探索，该数据集在产业界具有直接的应用价值。医药科技公司可利用其快速构建神经科疾病辅助诊断系统的原型，或用于电子健康记录系统的智能信息抽取模块的预训练。由于其零个人健康信息的属性，该数据可无缝集成至商业产品开发流程中，用于模型冷启动、功能验证以及合规性演示，显著缩短了从研发到产品部署的周期，并降低了法律风险。

数据集最近研究

最新研究方向

在神经医学与临床人工智能的交叉领域，合成数据正成为推动模型研发的关键驱动力。Neurology-1k数据集凭借其零真实患者健康信息、符合HIPAA标准的架构，为研究者提供了安全且高质量的神经学文本资源。当前前沿探索聚焦于利用此类合成数据训练多任务临床自然语言处理模型，涵盖文本分类、实体识别与问答任务，以应对真实医疗数据稀缺与隐私合规的挑战。热点事件包括医疗人工智能监管框架的演进，促使合成数据在药物研发与诊断辅助系统中得到更广泛验证。其高共识评分与集成生成方法，为构建可靠且可扩展的临床决策支持系统奠定了坚实基础，显著加速了医疗人工智能从实验到部署的转化进程。