DATEXIS/DeepICD-R1-zero-32B

TEXT GENERATIONConcurrency Cost:2Model Size:32.8BQuant:FP8Ctx Length:32kPublished:Mar 15, 2026License:otherArchitecture:Transformer Cold

DATEXIS/DeepICD-R1-zero-32B is a 32.8 billion parameter clinical reasoning model from DATEXIS, designed for ICD-10-CM diagnosis outcome prediction from admission notes. It utilizes the DeepICD-R1 framework, treating diagnosis prediction as a reinforcement learning task optimized with structured reward signals. This "R1-Zero" style model was trained primarily through reinforcement learning without supervised fine-tuning, allowing reasoning behaviors to emerge autonomously. It excels at generating reasoning traces and predicting single ICD-10-CM diagnoses from clinical text for research purposes.

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Overview

DATEXIS/DeepICD-R1-zero-32B is a 32.8 billion parameter clinical reasoning model developed by DATEXIS. It is specifically designed for ICD-10-CM diagnosis outcome prediction from clinical admission notes, operating within the DeepICD-R1 framework. This model is notable for its "R1-Zero" training paradigm, where it was trained primarily through reinforcement learning (GRPO-style) without initial supervised fine-tuning. This approach encourages the model to autonomously discover and develop reasoning strategies, such as chain-of-thought and self-verification, directly from reward signals.

Key Capabilities

  • ICD-10-CM Diagnosis Prediction: Predicts single ICD-10-CM codes from clinical text.
  • Reasoning Trace Generation: Produces structured outputs that include a reasoning trace (<think> tags) explaining how the diagnosis was derived, followed by the predicted ICD-10-CM code (<diagnosis> tags).
  • Reinforcement Learning for Reasoning: Demonstrates how RL alone can induce structured reasoning behaviors in large language models.
  • Clinical NLP Research: Intended for research in clinical reasoning experiments and structured prediction from clinical notes.

Intended Use Cases

This model is strictly for research purposes, including:

  • Clinical reasoning experiments.
  • ICD-10-CM code prediction research.
  • Reinforcement learning for language models.
  • Reasoning trace generation and structured prediction from clinical notes.

Limitations and Ethical Considerations

  • Trained primarily on English clinical notes from hospital-specific populations, leading to potential dataset biases and limitations with rare diagnoses.
  • Reasoning traces, while convincing, may be incorrect, and predictions can fail for rare or long-tail diagnoses.
  • Must not be used for medical diagnosis, clinical decision-making, or automated medical coding without expert supervision.
  • Potential risks include propagation of dataset biases and overconfidence in generated reasoning. Expert oversight and fairness audits are crucial for responsible use.

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