WitnessDataFactory/cardiology-1k

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

# Cardiology 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 cardiology 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/cardiology-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("cardiology_sample.csv", index=False)
```

---

## Dataset Schema

```json
{
"record_id": "uuid-v4",
"domain": "cardiology",
"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/cardiology-10k) |
| **Production** | 50,000 | **$7,999** | $0.16 | Model training, Series C+ | [Buy Now](https://witness-data-factory.onrender.com/pay/cardiology-50k) |
| **Enterprise** | 250,000 | **$29,999** | $0.12 | FDA-track, clinical AI | [Buy Now](https://witness-data-factory.onrender.com/pay/cardiology-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_cardiology_2026,
title = {Cardiology Synthetic Medical Dataset},
author = {WITNESS DATA FACTORY},
year = {2026},
publisher = {HuggingFace},
url = {https://huggingface.co/datasets/WitnessDataFactory/cardiology-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

在医学人工智能领域，高质量标注数据的匮乏长期制约着临床自然语言处理模型的发展。Cardiology-1k数据集采用创新的Trinity集成方法构建，通过融合三个大型语言模型的生成能力，协同产生结构化的合成心脏病学临床记录。该流程不仅确保了数据的临床合理性与多样性，还通过严格的共识评分机制对每条记录进行质量验证，最终生成完全不含受保护健康信息的合成数据集，规避了传统真实病历数据面临的隐私与合规风险。

特点

该数据集的核心特征体现在其卓越的质量保证体系与合规性设计。每一条记录均附有详尽的元数据，包括由Trinity集成模型计算得出的共识分数、模型间标注者一致性以及宏观F1分数，为研究者提供了透明的质量过滤依据。数据集完全由合成数据构成，实现了受保护健康信息的零存在，符合HIPAA架构要求，可直接用于研究与商业用途，无需伦理审查。其数据模式涵盖了患者年龄、性别、临床子类别、笔记类型及多层级标签体系，为心脏病学领域的多任务模型训练提供了丰富而规范的输入。

使用方法

为便利研究社区快速开展实验，数据集以Parquet格式发布，可通过Hugging Face的`datasets`库单行代码加载。用户可依据`consensus_score`等字段对数据进行筛选，例如设定阈值以获取高质量子集，并轻松转换为Pandas DataFrame进行后续分析。该免费样本旨在作为概念验证，而更大规模的生产级数据集（从一万至一百万条记录）可供升级，以满足模型训练、试点部署乃至临床人工智能产品开发等不同阶段的需求。

背景与挑战

背景概述

在临床人工智能与自然语言处理领域，高质量、可扩展且符合隐私规范的医学数据集是推动诊断辅助系统与电子健康记录分析技术发展的关键基石。Cardiology-1k数据集由WITNESS DATA FACTORY于2026年创建并发布，其核心研究目标在于提供一种零真实患者健康信息、符合HIPAA架构的合成心脏病学临床记录，以支持文本分类、实体识别及问答等多种医疗NLP任务的模型训练与验证。该数据集通过创新的三大型语言模型集成生成与质量评估流程，旨在缓解医学数据因隐私法规严格而导致的获取困难与标注成本高昂的普遍困境，为心血管疾病领域的临床人工智能研究提供了安全、可靠且标准化的数据资源。

当前挑战

该数据集致力于应对医疗自然语言处理中因数据稀缺与隐私约束所带来的核心挑战，即如何在严格遵守HIPAA等法规、确保零真实患者健康信息暴露的前提下，生成大规模、高质量且临床可信的标注数据，以训练鲁棒的疾病诊断与信息抽取模型。在构建过程中，主要挑战体现在生成技术的复杂性上，包括如何通过多模型集成方法有效规避单一模型可能产生的幻觉与偏见，以及如何设计并实施如Trinity共识评分等严格的质量保障体系，以确保每一条合成记录在医学准确性与逻辑一致性上达到接近真实临床文本的标准，从而支撑起严肃的科研与商业应用。

常用场景

经典使用场景

在心血管医学自然语言处理领域，高质量标注数据的稀缺长期制约着临床人工智能模型的研发进程。Cardiology-1k数据集通过提供千条结构化心脏病学记录，为研究者构建文本分类、实体识别和问答系统等任务奠定了数据基础。其经典应用场景集中于训练和评估临床文档自动化处理模型，例如从电子健康记录中自动提取诊断信息、识别关键医学实体，以及构建智能临床决策支持系统的原型验证。

实际应用

在实际医疗场景中，基于此类数据训练的模型可部署于医院信息系统，实现心血管疾病临床笔记的自动化编码与摘要生成。例如，系统能够实时分析入院记录，自动标注心肌梗死、心力衰竭等关键诊断，辅助医生快速完成病历书写与质量控制。此外，这些模型还能集成到临床研究平台，高效筛选符合特定条件的患者队列，为回顾性研究和临床试验招募提供技术支持，从而提升医疗机构的运营效率与研究能力。

衍生相关工作

该数据集的发布催生了一系列围绕合成医学数据质量评估与应用的创新研究。学者们以此为基础，探索了多模型集成生成技术在保持临床语义真实性方面的优势，并发展了新的合成数据质量量化指标。相关工作进一步拓展至跨专科医学语言模型的预训练，以及合成数据在罕见病诊断模型开发中的迁移学习策略。这些研究不仅验证了合成数据在复杂医学领域的可行性，也为构建更大规模、多模态的医疗人工智能基础设施提供了方法论参考。