WitnessDataFactory/surgical-1k

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

# Surgical 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 surgical 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/surgical-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("surgical_sample.csv", index=False)
```

---

## Dataset Schema

```json
{
"record_id": "uuid-v4",
"domain": "surgical",
"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/surgical-10k) |
| **Production** | 50,000 | **$7,999** | $0.16 | Model training, Series C+ | [Buy Now](https://witness-data-factory.onrender.com/pay/surgical-50k) |
| **Enterprise** | 250,000 | **$29,999** | $0.12 | FDA-track, clinical AI | [Buy Now](https://witness-data-factory.onrender.com/pay/surgical-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_surgical_2026,
title = {Surgical Synthetic Medical Dataset},
author = {WITNESS DATA FACTORY},
year = {2026},
publisher = {HuggingFace},
url = {https://huggingface.co/datasets/WitnessDataFactory/surgical-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

在医疗人工智能领域，高质量且合规的数据集是推动临床自然语言处理研究的关键。Surgical-1k数据集采用创新的Trinity集成方法构建，通过融合Llama3.3、Mistral和Qwen2.5三种大型语言模型的生成能力，合成出结构化的外科医疗记录。每一份记录均经过严格的共识评分与质量门控验证，确保其临床合理性与逻辑一致性，同时完全避免了真实患者信息的引入，从架构层面实现了与HIPAA法规的对齐。

特点

该数据集的核心特征在于其卓越的质量保障与合规性设计。凭借高达98.0%的Trinity共识评分，其数据质量显著超越了行业平均水平。数据集提供了每一条记录的详细元数据，包括类别标签、置信度分数以及模型间一致性度量，为研究者提供了精细的质量过滤维度。尤为突出的是，所有数据均为合成生成，彻底消除了受保护健康信息存在的风险，使得该数据集能够安全地用于不受限制的学术研究与商业开发。

使用方法

对于希望利用该数据集的研究者，其使用流程极为便捷。通过Hugging Face的`datasets`库，仅需一行代码即可加载数据。用户可以根据内置的`consensus_score`等质量指标对记录进行筛选，快速获取满足特定置信度阈值的高质量子集。数据以结构化的Parquet格式提供，可轻松转换为Pandas DataFrame或导出为CSV文件，无缝集成至现有的机器学习工作流中，为外科临床文本的分类、实体识别或问答等任务提供即用的训练与评估资源。

背景与挑战

背景概述

在医疗人工智能领域，高质量、合规的临床文本数据对于推动自然语言处理技术在手术记录分析、诊断辅助等任务中的应用至关重要。Surgical-1k数据集由WITNESS DATA FACTORY于2026年创建并发布，旨在提供企业级、符合HIPAA标准的合成手术医疗记录。该数据集的核心研究问题在于解决真实医疗数据因隐私保护法规（如HIPAA）而难以获取和使用的困境，通过采用三重大型语言模型集成方法生成零受保护健康信息的合成数据，为临床AI模型的训练与评估提供了安全、可扩展的资源基础，显著促进了医疗NLP技术在手术领域的合规化发展与应用探索。

当前挑战

该数据集致力于应对手术医疗文本分析中的核心挑战，即如何在严格遵守隐私法规的前提下，获取足够规模且高质量的标注数据，以支持文本分类、实体识别和问答等NLP任务。构建过程中的主要挑战包括：确保合成数据的临床真实性与多样性，避免单一模型生成导致的幻觉偏差；设计并实施严格的质量评估体系（如Trinity共识评分），以维持数据的高置信度与一致性；以及构建可扩展的合成管道，实现从千条到百万条记录的无缝生成与交付，同时保持零受保护健康信息的合规标准。

常用场景

经典使用场景

在临床人工智能与自然语言处理领域，高质量标注数据的稀缺性长期制约着模型性能的提升。Surgical-1k数据集以其合成的、结构化的手术记录，为研究者提供了一个经典的使用场景：作为基准数据集，用于开发和评估医疗文本分类、实体识别及问答系统的算法。其零PHI特性确保了数据使用的合规性与安全性，使得研究人员能够专注于模型架构的创新与优化，而无需担忧患者隐私泄露的风险。

实际应用

在实际应用层面，Surgical-1k数据集能够直接服务于医疗科技产品的开发与迭代。例如，其可用于训练智能电子健康记录系统，实现手术报告的自动编码、关键信息的结构化提取以及术后并发症的风险预警。这些由合成数据驱动的模型，在经过充分验证后，可部署于医院信息系统，辅助临床医生提升文档工作效率与诊疗决策的精准度，同时完全规避了处理真实患者数据所带来的合规性负担。

衍生相关工作

围绕此类高质量合成医疗数据，已衍生出一系列经典的研究工作与工程实践。一方面，在方法论上，催生了专注于利用合成数据提升模型泛化能力、进行对抗性测试以及研究领域自适应迁移的新兴研究方向。另一方面，在应用层面，它直接支撑了多家医疗AI初创公司构建其核心产品原型，例如手术结果预测模型和自动化临床编码工具，证明了合成数据在推动医疗AI从实验室走向商业化落地过程中的关键桥梁作用。