EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic
  Health Record Analysis
Electronic Health Records (EHRs) contain rich yet complex information, and
their automated analysis is critical for clinical decision-making. Despite
recent advances of large language models (LLMs) in clinical workflows, their
ability to analyze EHRs remains limited due to narrow task coverage and lack of
EHR-oriented reasoning capabilities. This paper aims to bridge the gap,
specifically, we present EHR-Ins, a large-scale, comprehensive EHR reasoning
instruction dataset, comprising 300k high-quality reasoning cases and 4M
non-reasoning cases across 42 distinct EHR tasks. Its core innovation is a
thinking-graph-driven framework that enables to generate high-quality reasoning
data at scale. Based on it, we develop EHR-R1, a series of reasoning-enhanced
LLMs with up to 72B parameters tailored for EHR analysis. Through a multi-stage
training paradigm, including domain adaptation, reasoning enhancement, and
reinforcement learning, EHR-R1 systematically acquires domain knowledge and
diverse reasoning capabilities, enabling accurate and robust EHR analysis.
Lastly, we introduce EHR-Bench, a new benchmark curated from MIMIC-IV, spanning
42 tasks, to comprehensively assess reasoning and prediction across EHR
scenarios. In experiments, we show that the resulting EHR-R1 consistently
outperforms state-of-the-art commercial and open-source LLMs (including
DeepSeek-V3 and GPT-4o), surpassing GPT-4o by over 30 points on MIMIC-Bench and
achieving a 10\% higher zero-shot AUROC on EHRSHOT. Collectively, EHR-Ins,
EHR-R1, and EHR-Bench have significantly advanced the development for more
reliable and clinically relevant EHR analysis.
69042c562c556835fa67f0c8
Yusheng Liao
claimed_verified
2025-11-03T20:54:50.198Z
64bf44c0a0e5471066875053
/avatars/b165c0b0632e849248362b7ed65314a9.svg
Yusheng Liao
BlueZeros
user
BlueZeros
69042c562c556835fa67f0c9
Chaoyi Wu
69042c562c556835fa67f0ca
Junwei Liu
69042c562c556835fa67f0cb
Shuyang Jiang
69042c562c556835fa67f0cc
Pengcheng Qiu
claimed_verified
2025-10-31T14:26:53.758Z
63f5b314fcf95ecac2b51968
https://cdn-avatars.huggingface.co/v1/production/uploads/63f5b314fcf95ecac2b51968/XwuSCoUBMZzVVIWE2IZ34.png
pengchengqiu
Henrychur
user
Henrychur
69042c562c556835fa67f0cd
Haowen Wang
69042c562c556835fa67f0ce
Yun Yue
69042c562c556835fa67f0cf
Shuai Zhen
69042c562c556835fa67f0d0
Jian Wang
69042c562c556835fa67f0d1
Qianrui Fan
69042c562c556835fa67f0d2
Jinjie Gu
69042c562c556835fa67f0d3
Ya Zhang
69042c562c556835fa67f0d4
Yanfeng Wang
69042c562c556835fa67f0d5
Yu Wang
69042c562c556835fa67f0d6
Weidi Xie
EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic
  Health Record Analysis
Electronic Health Records (EHRs) contain rich yet complex information, and
their automated analysis is critical for clinical decision-making. Despite
recent advances of large language models (LLMs) in clinical workflows, their
ability to analyze EHRs remains limited due to narrow task coverage and lack of
EHR-oriented reasoning capabilities. This paper aims to bridge the gap,
specifically, we present EHR-Ins, a large-scale, comprehensive EHR reasoning
instruction dataset, comprising 300k high-quality reasoning cases and 4M
non-reasoning cases across 42 distinct EHR tasks. Its core innovation is a
thinking-graph-driven framework that enables to generate high-quality reasoning
data at scale. Based on it, we develop EHR-R1, a series of reasoning-enhanced
LLMs with up to 72B parameters tailored for EHR analysis. Through a multi-stage
training paradigm, including domain adaptation, reasoning enhancement, and
reinforcement learning, EHR-R1 systematically acquires domain knowledge and
diverse reasoning capabilities, enabling accurate and robust EHR analysis.
Lastly, we introduce EHR-Bench, a new benchmark curated from MIMIC-IV, spanning
42 tasks, to comprehensively assess reasoning and prediction across EHR
scenarios. In experiments, we show that the resulting EHR-R1 consistently
outperforms state-of-the-art commercial and open-source LLMs (including
DeepSeek-V3 and GPT-4o), surpassing GPT-4o by over 30 points on MIMIC-Bench and
achieving a 10\% higher zero-shot AUROC on EHRSHOT. Collectively, EHR-Ins,
EHR-R1, and EHR-Bench have significantly advanced the development for more
reliable and clinically relevant EHR analysis.
EHR-R1, a series of reasoning-enhanced LLMs, outperforms existing models in EHR analysis through a comprehensive instruction dataset and multi-stage training paradigm.
large language models EHR reasoning instruction dataset thinking-graph-driven framework domain adaptation reasoning enhancement reinforcement learning EHR-Bench MIMIC-IV zero-shot AUROC EHRSHOT