GENIE En 8b

🚀 GENIE Model Card

GENIE (Generative Note Information Extraction) is an end - to - end model that simplifies the structuring of free text from electronic health records (EHRs). It efficiently extracts key biomedical information and outputs it in a structured JSON format, reducing costs and streamlining workflows.

🚀 Quick Start

Prerequisites

Make sure you have the vllm library installed.

Code Example

from vllm import LLM, SamplingParams

model = LLM(model='THUMedInfo/GENIE_en_8b', tensor_parallel_size=1)
#model = LLM(model=path/to/your/local/model, tensor_parallel_size=1)

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)
res = json.loads(output[0].outputs[0].text)

✨ Features

• End - to - End Structuring: Processes EHRs in a single pass, outputting structured JSON data.
• Reduced Complexity: Replaces multiple analysis components with a single model, making the system easier to maintain.
• Cost - Effective: Compares favorably with general - purpose LLMs, eliminating the need for prompt engineering and few - shot examples, and reducing runtime and operational costs.

📚 Documentation

Model Details

Model Description

GENIE (Generative Note Information Extraction) 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 Examples

Basic Usage

from vllm import LLM, SamplingParams

model = LLM(model='THUMedInfo/GENIE_en_8b', tensor_parallel_size=1)
#model = LLM(model=path/to/your/local/model, tensor_parallel_size=1)

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)
res = json.loads(output[0].outputs[0].text)

Example Input and Output

Input

EHR = ["""Unit No:___

Admission Date:___

Discharge Date:___

Date of Birth:___

Sex:   F

Service: MEDICINE

Allergies:
Sulfur / Norvasc

Attending:___
Addendum:
See below

Chief Complaint:
abdominal pain

Major Surgical or Invasive Procedure:
none

History of Present Illness:
84 F with PMHx of Renovascular HTN c/b NSTEMI now s/p renal
stents, Gout and h/o Crohn's disease who presented to the ED on
___with RLQ pain for approx 2 days.  She denies any
nausea/vomiting/diarrhea or constipation but has not been taking

po well and felt dehydrated."""]

Output

res = [{'phrase': 'allergies',
  'semantic_type': 'Disease, Syndrome or Pathologic Function',
  'assertion_status': 'title',
  'body_location': 'null',
  'modifier': 'null',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'not applicable'},
 {'phrase': 'sulfur',
  'semantic_type': 'Chemical or Drug',
  'assertion_status': 'conditional',
  'body_location': 'not applicable',
  'modifier': 'not applicable',
  'value': 'null',
  'unit': 'units: null',
  'purpose': 'null'},
 {'phrase': 'norvasc',
  'semantic_type': 'Chemical or Drug',
  'assertion_status': 'conditional',
  'body_location': 'not applicable',
  'modifier': 'not applicable',
  'value': 'null',
  'unit': 'units: null',
  'purpose': 'null'},
 {'phrase': 'abdominal pain',
  'semantic_type': 'Sign, Symptom, or Finding',
  'assertion_status': 'present',
  'body_location': 'Abdominal',
  'modifier': 'null',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'not applicable'},
 {'phrase': 'surgical or invasive procedure',
  'semantic_type': 'Therapeutic or Preventive Procedure',
  'assertion_status': 'title',
  'body_location': 'null',
  'modifier': 'not applicable',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'null'},
 {'phrase': 'renovascular hypertension',
  'semantic_type': 'Disease, Syndrome or Pathologic Function',
  'assertion_status': 'present',
  'body_location': 'renal',
  'modifier': 'null',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'not applicable'},
 {'phrase': 'non - st elevation myocardial infarction',
  'semantic_type': 'Disease, Syndrome or Pathologic Function',
  'assertion_status': 'present',
  'body_location': 'null',
  'modifier': 'null',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'not applicable'},
 {'phrase': 'gout',
  'semantic_type': 'Disease, Syndrome or Pathologic Function',
  'assertion_status': 'present',
  'body_location': 'null',
  'modifier': 'null',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'not applicable'},
 {'phrase': "crohn ' s disease",
  'semantic_type': 'Disease, Syndrome or Pathologic Function',
  'assertion_status': 'present',
  'body_location': 'not applicable',
  'modifier': 'not applicable',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'not applicable'},
 {'phrase': 'emergency department',
  'semantic_type': 'Therapeutic or Preventive Procedure',
  'assertion_status': 'present',
  'body_location': 'null',
  'modifier': 'not applicable',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'null'},
 {'phrase': 'pain',
  'semantic_type': 'Sign, Symptom, or Finding',
  'assertion_status': 'present',
  'body_location': 'right lower quadrant',
  'modifier': 'null',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'not applicable'},
 {'phrase': 'nausea',
  'semantic_type': 'Sign, Symptom, or Finding',
  'assertion_status': 'absent',
  'body_location': 'null',
  'modifier': 'null',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'not applicable'},
 {'phrase': 'vomiting',
  'semantic_type': 'Sign, Symptom, or Finding',
  'assertion_status': 'absent',
  'body_location': 'null',
  'modifier': 'null',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'not applicable'},
 {'phrase': 'diarrhea',
  'semantic_type': 'Sign, Symptom, or Finding',
  'assertion_status': 'absent',
  'body_location': 'null',
  'modifier': 'null',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'not applicable'},
 {'phrase': 'constipation',
  'semantic_type': 'Sign, Symptom, or Finding',
  'assertion_status': 'absent',
  'body_location': 'null',
  'modifier': 'null',
  'value': 'not applicable',
  'unit': 'not applicable',
  'purpose': 'not applicable'}]

📄 License

This project is licensed under the Apache - 2.0 license.

📚 Citation

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

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}, 
}