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---
language:
- en
license: other
pipeline_tag: text-generation
library_name: transformers
tags:
- clinical-nlp
- medical-coding
- icd10
- icd-10-cm
- reasoning
- reinforcement-learning
- grpo
- healthcare
base_model:
- Qwen/Qwen2.5-7B-Instruct
---
# DeepICD-R1-7B
## Model Summary
**DeepICD-R1-7B** is a clinical reasoning language model for **ICD-10-CM diagnosis outcome prediction from admission notes**.
It is derived from **Qwen2.5-7B-Instruct** and trained using the **DeepICD-R1 framework**, which combines structured reasoning traces with reinforcement learning and hierarchical reward signals.
The model is designed to predict a **single ICD-10-CM diagnosis code** from clinical text while producing an interpretable reasoning trace explaining the decision.
The training methodology follows the approach described in the paper:
**DeepICD-R1: Medical Reasoning through Hierarchical Rewards and Unsupervised Distillation**
This work frames clinical diagnosis prediction as a **reasoning task optimized through reinforcement learning**.
---
# Model Details
- **Model name:** DeepICD-R1-7B
- **Organization:** DATEXIS
- **Base model:** Qwen2.5-7B-Instruct
- **Parameters:** ~7B
- **Task:** Single ICD-10-CM diagnosis prediction from admission notes
- **Training paradigm:** Supervised reasoning + reinforcement learning
- **Framework:** VERL RL trainer
- **Domain:** Clinical NLP / healthcare reasoning
The Qwen2.5-7B-Instruct architecture is a **7-billion-parameter instruction-tuned language model designed for instruction following and long-form generation tasks**. :contentReference[oaicite:1]{index=1}
---
# Intended Use
This model is intended for **research purposes**, including:
- clinical reasoning research
- ICD-10-CM coding prediction
- reinforcement learning for language models
- reasoning trace generation
- structured prediction from clinical text
### Out-of-Scope Use
This model **must not be used for**:
- medical diagnosis
- clinical decision support
- patient triage
- automated medical coding without expert supervision
- billing or compliance workflows
---
# Training Methodology
The **DeepICD-R1 framework** treats diagnosis prediction as a reasoning problem.
Training combines:
### 1. Supervised reasoning traces
A dataset of reasoning chains explaining diagnosis predictions.
### 2. Reinforcement learning optimization
Training uses **Group Relative Policy Optimization (GRPO)** to improve reasoning and prediction accuracy.
### 3. Hierarchical reward signals
Rewards are aligned with the hierarchical structure of ICD codes.
The reward function combines:
- **format reward** — correct reasoning + diagnosis structure
- **outcome reward** — correct diagnosis prediction
- **hierarchical reward** — partial credit for correct ICD prefixes
This design encourages models to produce both **accurate diagnoses and structured reasoning**.
---
# Training Data
The training task uses **clinical admission notes paired with ICD-10-CM diagnosis codes**, derived from de-identified electronic health record datasets such as **MIMIC-IV**.
Task formulation:
**Input**
Clinical admission note describing patient presentation.
**Output**
Structured reasoning trace and predicted ICD-10-CM code.
---
# Output Format
The model is trained to produce structured outputs separating reasoning from the final diagnosis.
### Example
```text
<think>
The patient presents with ...
Symptoms and clinical history suggest ...
...
</think>
<diagnosis>
M5116
</diagnosis>
```
## Training Configuration
The model was trained using the **VERL reinforcement learning trainer** with **Group Relative Policy Optimization (GRPO)**, following the DeepICD-R1 training framework.
### Core Training Parameters
| Parameter | Value |
|-----------|------|
| Algorithm | GRPO |
| Training framework | VERL (`verl.trainer.main_ppo`) |
| Base model | Qwen2.5-7B-Instruct |
| Training batch size | 64 |
| PPO mini batch size | 64 |
| PPO micro batch size per GPU | 16 |
| Learning rate | 1e-6 |
| LR warmup steps | 80 |
| Total epochs | 1 |
| Max prompt length | 2048 tokens |
| Max response length | 1024 tokens |
### Rollout / Generation Settings
| Parameter | Value |
|-----------|------|
| Rollout engine | vLLM |
| Samples per prompt (`n`) | 8 |
| Temperature | 0.9 |
| Top-k | disabled |
| dtype | bfloat16 |
| Tensor parallel size | 1 |
| GPU memory utilization | 0.4 |
### Optimization Details
| Parameter | Value |
|-----------|------|
| Entropy coefficient | 0.001 |
| KL controller coefficient | 0.001 |
| KL loss | disabled |
| Gradient checkpointing | enabled |
| Torch compile | enabled |
| FSDP param offload | disabled |
| FSDP optimizer offload | disabled |
### Hardware
| Component | Value |
|-----------|------|
| GPUs | 4 |
| Nodes | 1 |
| Precision | bfloat16 |
### Reward Function
Training uses a **custom batched reward function** combining several reward signals:
- **Outcome reward** — correct ICD-10 prediction
- **Format reward** — correct `<think>` and `<diagnosis>` structure
- **Hierarchical reward** — partial credit for ICD prefix matches
- **Reasoning reward** — encourages meaningful reasoning traces
- **LLM-based reward** — optional external judge scoring
These rewards align the model toward producing **both accurate diagnoses and structured reasoning traces**.
The reasoning trace provides transparency into how the diagnosis was derived from the clinical note.
---
## Evaluation
Evaluation follows the methodology described in the **DeepICD-R1 paper**.
Performance is measured using **macro-averaged F1 scores** at multiple levels of the ICD hierarchy.
| Level | Description |
|------|-------------|
| Chapter | Broad ICD category |
| Category | First three digits |
| Full code | Complete ICD-10 code |
Hierarchical evaluation allows partial credit when the model predicts the correct high-level diagnostic category even if the full code is incorrect.
---
## Limitations
Models following the **DeepICD-R1 framework** share several limitations.
### Dataset limitations
- Training data consists primarily of **English clinical notes**
- Distribution reflects **hospital-specific patient populations**
- ICD labels are **highly imbalanced**, affecting rare diagnoses
### Model limitations
- Reasoning traces may appear convincing while being incorrect
- Predictions may fail for rare or long-tail diagnoses
- Models may demonstrate **premature diagnostic closure**
- Reinforcement learning rewards are only proxies for expert feedback
---
## Ethical Considerations
This model is trained on **de-identified clinical data** and intended strictly for research.
### Potential risks
- propagation of dataset biases
- overconfidence in generated reasoning
- misuse in clinical decision making
### Appropriate safeguards
- expert oversight
- dataset bias evaluation
- fairness audits
- controlled deployment environments
---
## Hardware and Training Setup
Typical training configuration for models in this family includes:
- **GPUs:** multi-GPU training (4–8 GPUs)
- **Precision:** bfloat16
- **Rollout engine:** vLLM
- **Training framework:** VERL PPO / GRPO trainer
- **Sampling:** multiple rollouts per prompt
---
## Usage
### Transformers Example
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "DATEXIS/DeepICD-R1-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype="auto"
)
prompt = """
You are a clinical reasoning model.
Given the following admission note,
produce reasoning in <think> tags
and a final ICD-10 diagnosis in <diagnosis> tags.
[ADMISSION NOTE]
"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=512
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Recommended Inference Practices
- Use prompts consistent with the training format.
- Validate predicted ICD-10 codes against official code formats.
- Always review predictions with medical experts.
- Avoid exposing reasoning traces in safety-critical settings without verification.
---
## Citation
If you use this model, please cite:
```bibtex
@inproceedings{roehr2026deepicdr1,
title={DeepICD-R1: Medical Reasoning through Hierarchical Rewards and Unsupervised Distillation},
author={R{\"o}hr, Tom and Steffek, Thomas and Teucher, Roman and Bressem, Keno and others},
booktitle={Proceedings of LREC-COLING},
year={2026}
}

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