EHR-R1 is a family of reasoning-enhanced Large Language Models (LLMs) specifically tailored for Electronic Health Record (EHR) analysis. It is developed based on EHR-Ins, a large-scale, comprehensive EHR reasoning instruction dataset, and is trained through a multi-stage paradigm including domain adaptation, reasoning enhancement, and reinforcement learning. This approach systematically acquires domain knowledge and diverse reasoning capabilities, enabling accurate and robust EHR analysis. The project also introduces EHR-Bench, a new benchmark curated from MIMIC-IV for comprehensive assessment across 42 distinct EHR tasks.

Paper: https://huggingface.co/papers/2510.25628
GitHub Repository: https://github.com/MAGIC-AI4Med/EHR-R1

💡 Key Highlights

We open-source a large-scale instruction dataset EHR-Ins, including 3.5M non-reasoning data and 300k reasoning data.
We open-source a comprehensive benchmark EHR-Bench, which covers 42 distinct EHR analysis tasks.
We open-source EHR reasoning-enhanced LLMs EHR-R1, including EHR-R1-1.7B, EHR-R1-8B, and EHR-R1-72B.
We open-source the "thinking-graph" pipeline, which can synthesize reasoning chains for EHR analysis tasks according to the relation of EHR entities.

⚡ Directly Use

EHR Input Format

For any EHR data, keep the EHR input with markdown format as below:

For the event with single record:

## Evant Name [Event Time (YYYY-MM-DD HH:MM:SS)]
- ItemKey_1: ItemValue_1
- ItemKey_2: ItemValue_2
- ItemKey_3: ItemValue_3

For the event with multiple records (like labevents):

## Evant Name [Event Time (YYYY-MM-DD HH:MM:SS)]
 |  ItemKey_1  |  ItemKey_2  |  ItemKey_3  |
 |  ---------  |  ---------  |  ---------  |
 | ItemValue_1 | ItemValue_2 | ItemValue_3 |
 | ItemValue_1 | ItemValue_2 | ItemValue_3 |
 | ItemValue_1 | ItemValue_2 | ItemValue_3 |

Models Inference with Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "BlueZeros/EHR-R1-8B" # This specific EHR-R1-8B model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

ehr_input = "{YOUR FOMATTED EHR INPUT}"
instruction = "{YOUR TASK INSTRUCTION}"
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": ehr_input + "\n" + instruction}
]

# For EHR-R1-1.7B & EHR-R1-8B, control the reasoning mode by setting enable_thinking
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
).to(model.device)
# For EHR-R1-72B, you can manually add `<think>\n\n</think>` at the end of the model_inputs to close the reasoning modes.
text += "<think>\n\n</think>"

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=2048,
    temperature=0.0
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

📖 Citation

If you find our work helpful or inspiring, please feel free to cite it:

bib

@article{liao2025ehrr1,
  title={{EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis}},
  author={Liao, Yusheng and Wu, Chaoyi and Liu, Junwei and Jiang, Shuyang and Qiu, Pengcheng and Wang, Haowen and Yue, Yun and Zhen, Shuai and Wang, Jian and Fan, Qianrui and Gu, Jinjie and Zhang, Ya and Wang, Yanfeng and Wang, Yu and Xie, Weidi},
  journal={arXiv preprint arXiv:2510.25628},
  year={2025}
}