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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- meta-llama/Llama-3.1-8B-Instruct |
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pipeline_tag: feature-extraction |
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tags: |
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- structurization |
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- EHR |
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- medical |
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- information extraction |
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--- |
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# Model Card for GENIE |
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## Model Details |
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Model Size: 8B (English) |
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Max Tokens: 8192 |
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Base model: Llama 3.1 8B (English) |
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### Model Description |
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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. |
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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 |
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```python |
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from vllm import LLM, SamplingParams |
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model = LLM(model='THUMedInfo/GENIE_en_8b', tensor_parallel_size=1) |
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#model = LLM(model=path/to/your/local/model, tensor_parallel_size=1) |
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PROMPT_TEMPLATE = "Human:\n{query}\n\n Assistant:" |
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sampling_params = SamplingParams(temperature=temperature, max_tokens=max_new_token) |
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EHR = ['xxxxx1','xxxxx2'] |
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texts = [PROMPT_TEMPLATE.format(query=k) for k in EHR] |
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output = model.generate(texts, sampling_params) |
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res = json.loads(output[0].outputs[0].text) |
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``` |
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## An example: |
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Input: |
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```python |
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EHR = ["""Unit No:___ |
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Admission Date:___ |
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Discharge Date:___ |
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Date of Birth:___ |
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Sex: F |
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Service: MEDICINE |
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Allergies: |
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Sulfur / Norvasc |
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Attending:___ |
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Addendum: |
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See below |
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Chief Complaint: |
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abdominal pain |
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Major Surgical or Invasive Procedure: |
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none |
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History of Present Illness: |
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84 F with PMHx of Renovascular HTN c/b NSTEMI now s/p renal |
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stents, Gout and h/o Crohn's disease who presented to the ED on |
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___with RLQ pain for approx 2 days. She denies any |
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nausea/vomiting/diarrhea or constipation but has not been taking |
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po well and felt dehydrated."""] |
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``` |
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Output: |
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```python |
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res = [{'phrase': 'allergies', |
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'semantic_type': 'Disease, Syndrome or Pathologic Function', |
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'assertion_status': 'title', |
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'body_location': 'null', |
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'modifier': 'null', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'not applicable'}, |
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{'phrase': 'sulfur', |
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'semantic_type': 'Chemical or Drug', |
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'assertion_status': 'conditional', |
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'body_location': 'not applicable', |
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'modifier': 'not applicable', |
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'value': 'null', |
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'unit': 'units: null', |
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'purpose': 'null'}, |
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{'phrase': 'norvasc', |
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'semantic_type': 'Chemical or Drug', |
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'assertion_status': 'conditional', |
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'body_location': 'not applicable', |
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'modifier': 'not applicable', |
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'value': 'null', |
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'unit': 'units: null', |
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'purpose': 'null'}, |
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{'phrase': 'abdominal pain', |
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'semantic_type': 'Sign, Symptom, or Finding', |
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'assertion_status': 'present', |
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'body_location': 'Abdominal', |
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'modifier': 'null', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'not applicable'}, |
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{'phrase': 'surgical or invasive procedure', |
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'semantic_type': 'Therapeutic or Preventive Procedure', |
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'assertion_status': 'title', |
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'body_location': 'null', |
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'modifier': 'not applicable', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'null'}, |
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{'phrase': 'renovascular hypertension', |
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'semantic_type': 'Disease, Syndrome or Pathologic Function', |
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'assertion_status': 'present', |
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'body_location': 'renal', |
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'modifier': 'null', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'not applicable'}, |
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{'phrase': 'non-st elevation myocardial infarction', |
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'semantic_type': 'Disease, Syndrome or Pathologic Function', |
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'assertion_status': 'present', |
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'body_location': 'null', |
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'modifier': 'null', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'not applicable'}, |
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{'phrase': 'gout', |
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'semantic_type': 'Disease, Syndrome or Pathologic Function', |
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'assertion_status': 'present', |
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'body_location': 'null', |
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'modifier': 'null', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'not applicable'}, |
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{'phrase': "crohn ' s disease", |
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'semantic_type': 'Disease, Syndrome or Pathologic Function', |
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'assertion_status': 'present', |
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'body_location': 'not applicable', |
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'modifier': 'not applicable', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'not applicable'}, |
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{'phrase': 'emergency department', |
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'semantic_type': 'Therapeutic or Preventive Procedure', |
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'assertion_status': 'present', |
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'body_location': 'null', |
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'modifier': 'not applicable', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'null'}, |
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{'phrase': 'pain', |
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'semantic_type': 'Sign, Symptom, or Finding', |
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'assertion_status': 'present', |
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'body_location': 'right lower quadrant', |
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'modifier': 'null', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'not applicable'}, |
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{'phrase': 'nausea', |
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'semantic_type': 'Sign, Symptom, or Finding', |
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'assertion_status': 'absent', |
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'body_location': 'null', |
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'modifier': 'null', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'not applicable'}, |
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{'phrase': 'vomiting', |
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'semantic_type': 'Sign, Symptom, or Finding', |
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'assertion_status': 'absent', |
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'body_location': 'null', |
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'modifier': 'null', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'not applicable'}, |
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{'phrase': 'diarrhea', |
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'semantic_type': 'Sign, Symptom, or Finding', |
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'assertion_status': 'absent', |
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'body_location': 'null', |
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'modifier': 'null', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'not applicable'}, |
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{'phrase': 'constipation', |
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'semantic_type': 'Sign, Symptom, or Finding', |
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'assertion_status': 'absent', |
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'body_location': 'null', |
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'modifier': 'null', |
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'value': 'not applicable', |
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'unit': 'not applicable', |
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'purpose': 'not applicable'}] |
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``` |
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## Citation |
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If you find our paper or models helpful, please consider cite: |
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**BibTeX:** |
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``` |
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@misc{ying2025geniegenerativenoteinformation, |
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title={GENIE: Generative Note Information Extraction model for structuring EHR data}, |
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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}, |
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year={2025}, |
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eprint={2501.18435}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2501.18435}, |
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} |
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``` |
