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Model: DATEXIS/DeepICD-R1-7B Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: other
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- clinical-nlp
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- medical-coding
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- icd10
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- icd-10-cm
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- reasoning
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- reinforcement-learning
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- grpo
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- healthcare
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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---
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# DeepICD-R1-7B
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## Model Summary
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**DeepICD-R1-7B** is a clinical reasoning language model for **ICD-10-CM diagnosis outcome prediction from admission notes**.
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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.
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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.
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The training methodology follows the approach described in the paper:
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**DeepICD-R1: Medical Reasoning through Hierarchical Rewards and Unsupervised Distillation**
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This work frames clinical diagnosis prediction as a **reasoning task optimized through reinforcement learning**.
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---
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# Model Details
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- **Model name:** DeepICD-R1-7B
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- **Organization:** DATEXIS
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- **Base model:** Qwen2.5-7B-Instruct
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- **Parameters:** ~7B
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- **Task:** Single ICD-10-CM diagnosis prediction from admission notes
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- **Training paradigm:** Supervised reasoning + reinforcement learning
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- **Framework:** VERL RL trainer
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- **Domain:** Clinical NLP / healthcare reasoning
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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}
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---
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# Intended Use
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This model is intended for **research purposes**, including:
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- clinical reasoning research
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- ICD-10-CM coding prediction
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- reinforcement learning for language models
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- reasoning trace generation
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- structured prediction from clinical text
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### Out-of-Scope Use
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This model **must not be used for**:
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- medical diagnosis
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- clinical decision support
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- patient triage
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- automated medical coding without expert supervision
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- billing or compliance workflows
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|
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---
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# Training Methodology
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The **DeepICD-R1 framework** treats diagnosis prediction as a reasoning problem.
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Training combines:
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### 1. Supervised reasoning traces
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A dataset of reasoning chains explaining diagnosis predictions.
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### 2. Reinforcement learning optimization
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Training uses **Group Relative Policy Optimization (GRPO)** to improve reasoning and prediction accuracy.
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### 3. Hierarchical reward signals
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Rewards are aligned with the hierarchical structure of ICD codes.
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The reward function combines:
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- **format reward** — correct reasoning + diagnosis structure
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- **outcome reward** — correct diagnosis prediction
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- **hierarchical reward** — partial credit for correct ICD prefixes
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This design encourages models to produce both **accurate diagnoses and structured reasoning**.
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---
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# Training Data
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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**.
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Task formulation:
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**Input**
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Clinical admission note describing patient presentation.
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**Output**
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Structured reasoning trace and predicted ICD-10-CM code.
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---
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# Output Format
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The model is trained to produce structured outputs separating reasoning from the final diagnosis.
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### Example
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```text
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<think>
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The patient presents with ...
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Symptoms and clinical history suggest ...
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...
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</think>
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<diagnosis>
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M5116
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</diagnosis>
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```
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## Training Configuration
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||||
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||||
The model was trained using the **VERL reinforcement learning trainer** with **Group Relative Policy Optimization (GRPO)**, following the DeepICD-R1 training framework.
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### Core Training Parameters
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||||
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||||
| Parameter | Value |
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||||
|-----------|------|
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||||
| Algorithm | GRPO |
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| Training framework | VERL (`verl.trainer.main_ppo`) |
|
||||
| Base model | Qwen2.5-7B-Instruct |
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||||
| Training batch size | 64 |
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| PPO mini batch size | 64 |
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| PPO micro batch size per GPU | 16 |
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| Learning rate | 1e-6 |
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| LR warmup steps | 80 |
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| Total epochs | 1 |
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| Max prompt length | 2048 tokens |
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| Max response length | 1024 tokens |
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||||
|
||||
### Rollout / Generation Settings
|
||||
|
||||
| Parameter | Value |
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||||
|-----------|------|
|
||||
| Rollout engine | vLLM |
|
||||
| Samples per prompt (`n`) | 8 |
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| Temperature | 0.9 |
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||||
| Top-k | disabled |
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||||
| dtype | bfloat16 |
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| Tensor parallel size | 1 |
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| GPU memory utilization | 0.4 |
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### Optimization Details
|
||||
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| Parameter | Value |
|
||||
|-----------|------|
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||||
| Entropy coefficient | 0.001 |
|
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| KL controller coefficient | 0.001 |
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| KL loss | disabled |
|
||||
| Gradient checkpointing | enabled |
|
||||
| Torch compile | enabled |
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| FSDP param offload | disabled |
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| FSDP optimizer offload | disabled |
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||||
|
||||
### Hardware
|
||||
|
||||
| Component | Value |
|
||||
|-----------|------|
|
||||
| GPUs | 4 |
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| Nodes | 1 |
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||||
| Precision | bfloat16 |
|
||||
|
||||
### Reward Function
|
||||
|
||||
Training uses a **custom batched reward function** combining several reward signals:
|
||||
|
||||
- **Outcome reward** — correct ICD-10 prediction
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||||
- **Format reward** — correct `<think>` and `<diagnosis>` structure
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||||
- **Hierarchical reward** — partial credit for ICD prefix matches
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||||
- **Reasoning reward** — encourages meaningful reasoning traces
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- **LLM-based reward** — optional external judge scoring
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These rewards align the model toward producing **both accurate diagnoses and structured reasoning traces**.
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The reasoning trace provides transparency into how the diagnosis was derived from the clinical note.
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|
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---
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## Evaluation
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Evaluation follows the methodology described in the **DeepICD-R1 paper**.
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Performance is measured using **macro-averaged F1 scores** at multiple levels of the ICD hierarchy.
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||||
| Level | Description |
|
||||
|------|-------------|
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||||
| Chapter | Broad ICD category |
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| Category | First three digits |
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| Full code | Complete ICD-10 code |
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||||
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||||
Hierarchical evaluation allows partial credit when the model predicts the correct high-level diagnostic category even if the full code is incorrect.
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||||
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||||
---
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||||
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||||
## Limitations
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||||
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||||
Models following the **DeepICD-R1 framework** share several limitations.
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||||
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||||
### Dataset limitations
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||||
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- Training data consists primarily of **English clinical notes**
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- Distribution reflects **hospital-specific patient populations**
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||||
- ICD labels are **highly imbalanced**, affecting rare diagnoses
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||||
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||||
### Model limitations
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||||
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||||
- Reasoning traces may appear convincing while being incorrect
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||||
- Predictions may fail for rare or long-tail diagnoses
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||||
- Models may demonstrate **premature diagnostic closure**
|
||||
- Reinforcement learning rewards are only proxies for expert feedback
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||||
|
||||
---
|
||||
|
||||
## Ethical Considerations
|
||||
|
||||
This model is trained on **de-identified clinical data** and intended strictly for research.
|
||||
|
||||
### Potential risks
|
||||
|
||||
- propagation of dataset biases
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||||
- overconfidence in generated reasoning
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||||
- 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}
|
||||
}
|
||||
|
||||
24
added_tokens.json
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added_tokens.json
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{
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"</tool_call>": 151658,
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|
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|
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|
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54
chat_template.jinja
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chat_template.jinja
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||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
58
config.json
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config.json
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|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3584,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 18944,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
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||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user