GENIE Zh 7b

🚀 Model Card for GENIE

GENIE (Generative Note Information Extraction) is an end - to - end model tailored for structuring free text from electronic health records (EHRs). It simplifies the traditional NLP workflow and reduces runtime and operational costs.

🚀 Quick Start

✨ Features

• Single - pass processing: GENIE processes EHRs in one go, extracting various biomedical information and outputting it in a structured JSON format.
• No prompt engineering needed: Unlike general - purpose LLMs, it doesn't require prompt engineering or few - shot examples.
• Cost - effective: It significantly cuts down both runtime and operational costs by generating all relevant attributes in a single pass.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from vllm import LLM, SamplingParams

PROMPT_TEMPLATE = "Human:\n{query}\n\n Assistant:\n"
sampling_params = SamplingParams(temperature=temperature, max_tokens=max_new_token)
EHR = ['xxxxx1','xxxxx2']
texts = [PROMPT_TEMPLATE.format(query=k) for k in EHR]
output = model.generate(texts, sampling_params)

Advanced Usage

Here is an example of input and output:

Input:

EHR = ['慢性乙型肝炎病史10余年，曾有肝功能异常，中医治疗后好转；1年余前查HBsAg转阴，但肝脏病理提示病毒性肝炎伴肝纤维化（G1S3-4）']

Output:

res = [
 { "术语": "慢性乙型肝炎",
 "语义类型": "疾病、综合征、病理功能",
 "叙述状态": "存在",
 "身体部位": "无",
 "数值": "NA",
 "单位": "NA",
 "修饰词": "无" },
 { "术语": "肝功能异常",
 "语义类型": "症状、体征、临床所见",
 "叙述状态": "存在",
 "身体部位": "无",
 "数值": "NA",
 "单位": "NA",
 "修饰词": "无" },
 { "术语": "HBsAg",
 "语义类型": "化学物质、药物",
 "叙述状态": "不存在",
 "身体部位": "NA",
 "数值": "无",
 "单位": "NA",
 "修饰词": "NA" },
{ "术语": "肝脏病理",
 "语义类型": "诊断操作",
 "叙述状态": "存在",
 "身体部位": "无",
 "数值": "无",
 "单位": "NA",
 "修饰词": "NA" },
 { "术语": "病毒性肝炎",
 "语义类型": "疾病、综合征、病理功能",
 "叙述状态": "存在",
 "身体部位": "无",
 "数值": "NA",
 "单位": "NA",
 "修饰词": "无" },
 { "术语": "肝纤维化",
 "语义类型": "疾病、综合征、病理功能",
 "叙述状态": "存在",
 "身体部位": "无",
 "数值": "NA",
 "单位": "NA",
 "修饰词": "无" },
]

📚 Documentation

Model Details

Model Description

GENIE (Generative Note Information Extraction, Chinese name: 病历精灵) is an end - to - end model designed to structure free text from electronic health records (EHRs). It processes EHRs in a single pass, extracting biomedical named entities along with their assertion statuses, body locations, modifiers, values, units, and intended purposes, outputting this information in a structured JSON format. This streamlined approach simplifies traditional natural language processing workflows by replacing all the analysis components with a single model, making the system easier to maintain while leveraging the advanced analytical capabilities of large language models (LLMs). Comparing with general - purpose LLMs, GENIE does not require prompt engineering or few - shot examples. Additionally, it generates all relevant attributes in one pass, significantly reducing both runtime and operational costs. GENIE is co - developed by the groups of Sheng Yu (https://www.stat.tsinghua.edu.cn/teachers/shengyu/), Tianxi Cai (https://dbmi.hms.harvard.edu/people/tianxi - cai), and Isaac Kohane (https://dbmi.hms.harvard.edu/people/isaac - kohane).

📄 License

The model is licensed under the Apache - 2.0 license.

📚 Citation

If you find our paper or models helpful, please consider citing:

BibTeX:

@misc{ying2025geniegenerativenoteinformation,
      title={GENIE: Generative Note Information Extraction model for structuring EHR data}, 
      author={Huaiyuan Ying and Hongyi Yuan and Jinsen Lu and Zitian Qu and Yang Zhao and Zhengyun Zhao and Isaac Kohane and Tianxi Cai and Sheng Yu},
      year={2025},
      eprint={2501.18435},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.18435}, 
}