BlueZeros/EHR-R1-1.7B

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:Aug 26, 2025License:cc-by-nc-4.0Architecture:Transformer0.0K Open Weights Warm

BlueZeros/EHR-R1-1.7B is a 1.7 billion parameter reasoning-enhanced foundational language model developed by BlueZeros, specifically designed for Electronic Health Record (EHR) analysis. Trained on the EHR-Ins instruction dataset and utilizing a multi-stage paradigm, it systematically acquires domain knowledge and diverse reasoning capabilities. This model excels at accurate and robust EHR analysis across 42 distinct tasks, as assessed by the EHR-Bench benchmark, and supports a 40960 token context length.

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EHR-R1-1.7B: Reasoning-Enhanced LLM for EHR Analysis

EHR-R1-1.7B is a 1.7 billion parameter model from the EHR-R1 series, specifically engineered for Electronic Health Record (EHR) analysis. Developed by BlueZeros, this model is detailed in the paper "EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis" [https://huggingface.co/papers/2510.25628].

Key Capabilities & Features

  • Domain-Specific Training: Trained on EHR-Ins, a large-scale, comprehensive EHR reasoning instruction dataset (3.5M non-reasoning, 300k reasoning data).
  • Multi-Stage Paradigm: Utilizes domain adaptation, reasoning enhancement, and reinforcement learning to acquire deep domain knowledge and diverse reasoning abilities.
  • EHR-Bench Benchmark: Assessed against EHR-Bench, a new benchmark curated from MIMIC-IV covering 42 distinct EHR analysis tasks.
  • Reasoning Enhancement: Designed to systematically acquire and apply reasoning capabilities for robust EHR analysis.
  • Flexible Input Format: Supports structured EHR input using a markdown-based format for both single and multiple record events.
  • Thinking-Graph Pipeline: The project also introduces a "thinking-graph" pipeline for synthesizing reasoning chains based on EHR entity relations.

Ideal Use Cases

  • Clinical Decision Support: Assisting healthcare professionals with insights derived from patient EHRs.
  • Medical Research: Analyzing large datasets of EHRs for patterns, predictions, and research hypotheses.
  • Healthcare Analytics: Performing complex analytical tasks on electronic health records.
  • EHR Data Interpretation: Extracting and interpreting critical information from structured and unstructured EHR data.