| --- |
| license: apache-2.0 |
| language: |
| - zh |
| base_model: |
| - Qwen/Qwen2.5-7B-Instruct |
| pipeline_tag: feature-extraction |
| tags: |
| - structuring |
| - EHR |
| - medical |
| - IE |
| --- |
| # Model Card for GENIE |
|
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|
|
| ## Model Details |
|
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| Model Size: 7B |
|
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| Max Tokens: 8192 |
|
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| Base model: Qwen 2.5 7B |
|
|
| ### Model Description |
|
|
| GENIE (Generative Note Information Extraction, 中文名：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). |
|
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|
|
| ## Usage |
|
|
| ```python |
| 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) |
| ``` |
|
|
| # An example |
|
|
| Input: |
| ```python |
| 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）'] |
| ``` |
|
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| Output: |
| ```python |
| 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" }, |
| ] |
| ``` |
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|
|
| ## Citation |
|
|
| If you find our paper or models helpful, please consider cite: |
|
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| **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}, |
| } |
| ``` |
