File size: 3,312 Bytes
f3d3fa0 1795aa2 f3d3fa0 2e5d994 f3d3fa0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
---
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
## Model Details
Model Size: 8B (English) / 7B (Chinese)
Max Tokens: 8192
Base model: Llama 3.1 8B (English) / Qwen 2.5 7B (Chinese)
### 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).
## Usage
```python
from vllm import LLM, SamplingParams
PROMPT_TEMPLATE = "Human:\n{query}\n\n Assistant:"
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)']
```
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" },
]
```
## Citation [optional]
If you find our paper or models helpful, please consider cite: (to be released)
**BibTeX:**
[More Information Needed] |