数据集概览
| 数据集名称 | 临床医学文本 NLP 标注数据集 |
| 数据总量 | 328,000 条(电子病历 + 诊断报告 + 医嘱 + 医学文献摘要) |
| 文本类型 | 入院记录 85K / 病程记录 72K / 出院小结 45K / 检查报告 63K / 医嘱 38K / 文献摘要 25K |
| NER 实体类别 | 疾病、症状、药品、检查、手术、解剖部位、实验室指标 7大类 46子类 |
| 关系标注 | 疾病-症状、药品-疾病、检查-发现、手术-解剖 4种关系类型 |
| 术语标准化 | ICD-10-CM / SNOMED CT / ATC / LOINC 映射 |
| 标注方式 | 临床医学专业人员标注 + NLP预标注辅助 + 术语专家审核 |
| 格式输出 | BIO/BIOS 序列标注 / JSON / Brat Standoff / CoNLL |
数据维度详细说明
| 字段名称 | 数据类型 | 取值范围/说明 |
|---|---|---|
| doc_id | String(32) | 脱敏文档ID |
| doc_type | Enum | ADMISSION | PROGRESS | DISCHARGE | REPORT | ORDER | LITERATURE |
| department | Enum | INTERNAL | SURGERY | OBGYN | PEDIATRICS | ONCOLOGY | CARDIOLOGY 等 |
| text | Text | 脱敏后的临床文本(患者姓名→[NAME],日期→[DATE],机构→[ORG]) |
| entities | Array | [{start, end, type, subtype, text, icd10_code, snomed_code}] |
| relations | Array | [{head_entity_idx, tail_entity_idx, relation_type}] |
| bio_tags | Array | BIO/BIOS 序列标注标签列表 |
| text_length | Integer | 字符数(含标点) |
| entity_count | Integer | 实体数量 |
| annotator_id | String(8) | 脱敏标注者ID |
| qc_score | Float | 质检评分 0.0 - 1.0 |
脱敏 JSON 数据样例
[
{
"doc_id": "CLT-A0000821",
"doc_type": "ADMISSION",
"department": "CARDIOLOGY",
"text": "患者[NAME],[AGE]岁[GENDER],因"反复胸闷、心悸[DURATION]"入院。既往有[DISEASE]病史[DRUG]治疗中。入院查体:[VALUE]次/分,血压[VALUE]/[VALUE]mmHg。",
"entities": [
{"start": 62, "end": 70, "type": "SYMPTOM", "subtype": "胸闷", "text": "反复胸闷、心悸"},
{"start": 82, "end": 90, "type": "DISEASE", "subtype": "心血管疾病", "text": "[DISEASE]", "icd10_code": "I25.1"},
{"start": 92, "end": 98, "type": "DRUG", "subtype": "心血管药物", "text": "[DRUG]", "atc_code": "C07AB"}
],
"relations": [
{"head_entity_idx": 0, "tail_entity_idx": 1, "relation_type": "SYMPTOM_OF"}
],
"text_length": 156,
"entity_count": 3,
"annotator_id": "ANN-0421",
"qc_score": 0.96
},
{
"doc_id": "CLT-R0012345",
"doc_type": "REPORT",
"department": "RADIOLOGY",
"text": "检查部位:[BODY_PART]。影像所见:[BODY_PART]见[SIZE]cm[DESCRIPTOR][FINDING],边界[DEGREE],周边[DESCRIPTOR]。诊断意见:[DIAGNOSIS]。",
"entities": [
{"start": 4, "end": 16, "type": "ANATOMY", "subtype": "检查部位", "text": "[BODY_PART]", "snomed_code": "123037004"},
{"start": 20, "end": 32, "type": "ANATOMY", "subtype": "检查部位", "text": "[BODY_PART]"},
{"start": 33, "end": 42, "type": "LAB", "subtype": "尺寸测量", "text": "[SIZE]cm"},
{"start": 43, "end": 55, "type": "FINDING", "subtype": "影像发现", "text": "[DESCRIPTOR][FINDING]"}
],
"relations": [
{"head_entity_idx": 1, "tail_entity_idx": 3, "relation_type": "HAS_FINDING"}
],
"text_length": 89,
"entity_count": 4,
"annotator_id": "ANN-0715",
"qc_score": 0.94
},
{
"doc_id": "CLT-D0003672",
"doc_type": "DISCHARGE",
"department": "ONCOLOGY",
"text": "出院诊断:[DISEASE]([STAGE])。住院期间行[PROCEDURE]治疗,过程顺利。出院医嘱:1.[DRUG] [DOSAGE];2.定期复查[LAB];3.不适随诊。",
"entities": [
{"start": 5, "end": 15, "type": "DISEASE", "subtype": "肿瘤", "text": "[DISEASE]", "icd10_code": "C34.9"},
{"start": 24, "end": 32, "type": "PROCEDURE", "subtype": "治疗操作", "text": "[PROCEDURE]"},
{"start": 47, "end": 53, "type": "DRUG", "subtype": "抗肿瘤药", "text": "[DRUG]", "atc_code": "L01XE"},
{"start": 64, "end": 70, "type": "LAB", "subtype": "实验室检查", "text": "[LAB]", "loinc_code": "24331-1"}
],
"relations": [
{"head_entity_idx": 0, "tail_entity_idx": 1, "relation_type": "TREATED_BY"},
{"head_entity_idx": 0, "tail_entity_idx": 3, "relation_type": "MONITORED_BY"}
],
"text_length": 112,
"entity_count": 4,
"annotator_id": "ANN-0388",
"qc_score": 0.97
},
{
"doc_id": "CLT-O0009105",
"doc_type": "ORDER",
"department": "INTERNAL",
"text": "[DRUG] [DOSAGE] [ROUTE] [FREQUENCY] [DURATION]天",
"entities": [
{"start": 0, "end": 6, "type": "DRUG", "subtype": "抗生素", "text": "[DRUG]", "atc_code": "J01MA"},
{"start": 7, "end": 15, "type": "DOSAGE", "subtype": "剂量", "text": "[DOSAGE]"},
{"start": 16, "end": 22, "type": "ROUTE", "subtype": "给药途径", "text": "[ROUTE]"}
],
"relations": [],
"text_length": 42,
"entity_count": 3,
"annotator_id": "ANN-0912",
"qc_score": 0.99
},
{
"doc_id": "CLT-L0001543",
"doc_type": "LITERATURE",
"department": "ONCOLOGY",
"text": "本研究旨在评估[DISEASE]患者中[DRUG]联合[DRUG]方案的有效性和安全性。共纳入[NUMBER]例患者,ORR为[VALUE]%,中位PFS为[VALUE]个月。",
"entities": [
{"start": 6, "end": 16, "type": "DISEASE", "subtype": "肿瘤", "text": "[DISEASE]"},
{"start": 18, "end": 24, "type": "DRUG", "subtype": "抗肿瘤药", "text": "[DRUG]"},
{"start": 26, "end": 32, "type": "DRUG", "subtype": "抗肿瘤药", "text": "[DRUG]"},
{"start": 40, "end": 48, "type": "LAB", "subtype": "临床指标", "text": "ORR"},
{"start": 55, "end": 63, "type": "LAB", "subtype": "临床指标", "text": "中位PFS"}
],
"relations": [
{"head_entity_idx": 0, "tail_entity_idx": 1, "relation_type": "TREATED_BY"},
{"head_entity_idx": 0, "tail_entity_idx": 3, "relation_type": "MEASURED_BY"}
],
"text_length": 97,
"entity_count": 5,
"annotator_id": "ANN-0523",
"qc_score": 0.95
}
]
标注规范与质量控制
标注标准
- NER 标注遵循 i2b2 2010/2012 临床命名实体识别标准扩展,新增 46 个细粒度子类别
- 关系抽取采用「头实体→关系→尾实体」三元组格式,关系类型覆盖诊断-发现、治疗-疾病等 4 大类
- 术语标准化双映射:ICD-10-CM 诊断编码 + SNOMED CT 临床术语,药品用 ATC 编码
- 文本脱敏:PHI(Protected Health Information)全部替换为类型占位符,零信息泄露
质量指标
96.3%
NER F1
91.7%
关系抽取F1
98.5%
术语映射准确率
0.89
标注一致率Kappa
AI 训练适用场景
临床命名实体识别(NER)
适用模型:BERT-BiLSTM-CRF / BioBERT / PubMedBERT / ClinicalBERT / GatorTron。训练面向中文临床文本的命名实体识别模型,F1 可达 90%+。
医学关系抽取
适用模型:BioRelEx / PURE / PL-Marker / REBEL。在实体识别基础上抽取疾病-症状、药品-疾病等关系,构建医学知识图谱。
临床术语标准化
适用模型:SapBERT / CODER / BioSyn。将口语化/非标准化临床术语映射到 ICD-10/SNOMED CT 标准术语体系。
电子病历结构化
适用模型:LLM + RAG(GPT-4o/Claude/Qwen2.5 + LangChain)。端到端将非结构化病历转为结构化 FHIR/CDA 格式。
认证专家网络
5000+
认证科研专家
覆盖数学、物理、化学、生物、地学、计算机六大学科
四重
质量审核体系
学术资质审核、专业能力评估、数据标注培训、质量持续监控
AI+
人机协同标注
AI预标注+专家复核,效率提升300%,准确率99.5%+
相关数据集
商业应用价值
智慧医院信息化
嵌入HIS/EMR系统中的智能病历质控、CDSS临床决策支持模块,提升病历规范率和诊疗质量。
医保智能审核
基于诊断-药品-检查关系抽取,训练医保智能审核模型,自动识别不合理用药和过度检查。
药企真实世界研究
从海量电子病历中结构化提取用药信息和临床结局,支撑药物上市后真实世界证据(RWE)研究。