commit 6eaee8ba1348bbe13df6654d6a279841c6ad40f9 Author: ModelHub XC Date: Tue Jul 21 19:08:09 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: AmareshHebbar/icd10-coder-qwen25-7b-merged Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..d87afaf --- /dev/null +++ b/README.md @@ -0,0 +1,308 @@ +--- +base_model: unsloth/qwen2.5-7b-instruct +tags: +- text-generation-inference +- transformers +- unsloth +- qwen2 +- clinical-nlp +- medical +- insurance +- qwen2.5 +- safetensors +- gguf +- icd10 +- healthcare +- fine-tuned +license: apache-2.0 +language: +- en +widget: + - text: "Patient presents with acute appendicitis requiring surgical intervention. Admitted for 2 days. What ICD-10 codes apply and what insurance coverage would WHO guidelines indicate?" + example_title: "Appendicitis + Coverage Query" + - text: "Diagnosis: Type 2 diabetes mellitus with diabetic nephropathy. What ICD-10 coding and reimbursement brackets apply?" + example_title: "Chronic Condition Coding" +spaces: + - AmareshHebbar/icd10-coder-demo +--- + +
+ +# ICD-10 Medical Coder — Qwen2.5-7B + +### An AI system for WHO-standardized medical classification, insurance code prediction, and coverage estimation + +[![Model](https://img.shields.io/badge/Base%20Model-Qwen2.5--7B--Instruct-blue)](https://huggingface.co/unsloth/qwen2.5-7b-instruct) +[![License](https://img.shields.io/badge/License-Apache%202.0-green)](LICENSE) +[![Training](https://img.shields.io/badge/Trained%20on-NVIDIA%20A5000-orange)](https://api.wandb.ai/links/amareshhebbar-/qonl6s58) +[![W&B](https://img.shields.io/badge/Tracked%20with-Weights%20%26%20Biases-yellow)](https://api.wandb.ai/links/amareshhebbar-/qonl6s58) +[![GitHub](https://img.shields.io/badge/GitHub-AxisMapper-black)](https://github.com/amareshhebbar/AxisMapper) + +
+ +--- + +## What Is This? + +**ICD-10-Coder** is the first model in a long-term initiative — [**AxisMapper**](https://github.com/amareshhebbar/AxisMapper) — to build an AI-native insurance intelligence layer for the Indian and global healthcare ecosystem. + +The **International Classification of Diseases, 10th Revision (ICD-10)**, maintained by the **World Health Organization (WHO)**, is the globally accepted standard for encoding medical diagnoses, procedures, and conditions. Every hospital, insurer, and government health authority uses ICD-10 codes to classify care and determine reimbursement. + +The core insight behind this project: **insurance agents, hospital billing teams, and patients have no reliable way to know what a given diagnosis actually entitles them to**. Coverage decisions are opaque, rules are fragmented across schemes, and the same condition might be coded five different ways — each triggering a different payout. + +This model is the **first agent** in what will become a **Multi-Agent, Mixture-of-Experts (MoE) pipeline** — purpose-built to decode that opacity. + +--- + +## The Bigger Vision: AxisMapper + +> *"One fine-tuned model per insurance scheme. A shared routing layer. Zero ambiguity for the patient."* + +India's health insurance landscape spans: +- **Ayushman Bharat / PM-JAY** — world's largest government-funded health insurance scheme +- **Star Health** — India's largest standalone health insurer +- **ESIC / CGHS** — central government employee schemes +- **State-level programs** — varying eligibility, tariff, and admission rules +- **NGO-backed schemes** — community-level coverage with entirely different logic + +Each of these schemes has its own ICD-10 code mappings, admission duration requirements, procedure eligibility, and claim caps. There is no unified interface to query them all. + +**AxisMapper's roadmap:** + +``` +Phase 1 (Now) → WHO ICD-10 base model (this model) + Universal code prediction + coverage logic + +Phase 2 → Fine-tune per scheme (StarHealth, PM-JAY, ESIC, etc.) + Each model specialises in one insurer's rule set + +Phase 3 → MoE Router + Given a patient + insurer, route to the right specialist model + +Phase 4 → Multi-Agent Pipeline + Agent 1: Diagnosis → ICD-10 code + Agent 2: Code → Coverage estimate (policy-aware) + Agent 3: Coverage + Admission rules → Final claim amount + Agent 4: Web search → Real-time tariff / market validation +``` + +This model — the WHO-standardized base — handles **Phase 1**: given any clinical description, it returns the correct ICD-10 code, explains the classification, and applies WHO-level coverage logic. + +--- + +## Model Details + +| Property | Value | +|---|---| +| **Base Model** | `unsloth/qwen2.5-7b-instruct` | +| **Architecture** | Qwen2 (decoder-only transformer) | +| **Parameters** | ~8B | +| **Precision** | BF16 | +| **Fine-tuning Method** | LoRA via Unsloth + HuggingFace TRL | +| **Training Hardware** | NVIDIA RTX A5000 (24GB VRAM) | +| **Training Duration** | ~2 hours | +| **Training Speed** | 2× faster than standard HF training (via Unsloth) | +| **Experiment Tracking** | Weights & Biases (W&B) | +| **Max Sequence Length** | 2048 tokens | +| **License** | Apache 2.0 | + +--- + +## Training Infrastructure + +This model was trained using the [Unsloth](https://github.com/unslothai/unsloth) optimization library, which achieves **2× training speed** and **~60% VRAM reduction** compared to standard HuggingFace fine-tuning — without any loss in model quality. + +**Training stack:** +- `unsloth` — optimized LoRA fine-tuning engine +- `trl` (HuggingFace) — SFTTrainer for instruction fine-tuning +- `transformers` — model loading, tokenization, inference +- `wandb` — real-time loss curves, learning rate scheduling, gradient tracking + +All training runs are logged and reproducible via Weights & Biases. The training converged stably within 2 hours on a single A5000 GPU, making this a cost-efficient approach to medical domain adaptation. + +--- + +## What This Model Does + +Given a clinical description or patient scenario, this model will: + +1. **Assign the correct ICD-10 code(s)** — primary diagnosis, secondary conditions, procedure codes +2. **Explain the WHO classification logic** — why this code, what the category means, adjacent codes +3. **Estimate WHO-level insurance coverage** — standard reimbursement brackets, admission duration requirements, procedure eligibility +4. **Flag restrictions** — minimum admission days, co-morbidity requirements, pre-authorisation triggers +5. **Support multi-condition scenarios** — comorbidities, complications, dual coding + +**Example input:** +``` +Patient admitted for acute appendicitis with peritonitis. +Underwent emergency appendectomy. Admitted for 3 days. +What ICD-10 codes apply and what is the expected insurance coverage? +``` + +**Example output (truncated):** +``` +Primary Code: K35.2 — Acute appendicitis with generalised peritonitis +Procedure Code: 0DTJ4ZZ — Resection of appendix, percutaneous endoscopic approach + +WHO Classification: Diseases of the digestive system (K00–K93) +Chapter XI, Block K35-K38 (Diseases of appendix) + +Coverage Logic: +- WHO standard: Surgical admission, inpatient required +- Minimum admission: 1–3 days (surgery-dependent) +- Reimbursement class: Major surgery +- Pre-auth: Required for elective; emergency bypass available +- Approximate WHO-tier bracket: ₹35,000–₹75,000 (India tier-2 hospital) +``` + +--- + +## Quickstart + +### Using Transformers (Pipeline) + +```python +from transformers import pipeline + +pipe = pipeline("text-generation", model="AmareshHebbar/icd10-coder-qwen25-7b-merged") + +query = """ +Patient presents with Type 2 diabetes mellitus with chronic kidney disease stage 3. +What ICD-10 codes apply? What are the WHO-level insurance implications? +What are the admission requirements for this to be covered? +""" + +result = pipe([{"role": "user", "content": query}], max_new_tokens=512) +print(result[0]["generated_text"][-1]["content"]) +``` + +### Using Unsloth (Recommended for inference speed) + +```python +from unsloth import FastModel + +model, tokenizer = FastModel.from_pretrained( + model_name="AmareshHebbar/icd10-coder-qwen25-7b-merged", + max_seq_length=2048, + load_in_4bit=True, # Optional: 4-bit for lower VRAM +) + +messages = [ + {"role": "system", "content": "You are an expert ICD-10 medical coder with deep knowledge of WHO insurance classification standards."}, + {"role": "user", "content": "Patient: acute MI, stented. 2-day admission. Code and coverage?"} +] + +inputs = tokenizer.apply_chat_template( + messages, tokenize=True, add_generation_prompt=True, + return_tensors="pt" +).to(model.device) + +outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1) +print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)) +``` + +### Using vLLM (Production / High Throughput) + +```bash +pip install vllm +vllm serve "AmareshHebbar/icd10-coder-qwen25-7b-merged" --max-model-len 2048 +``` + +```python +from openai import OpenAI + +client = OpenAI(base_url="http://localhost:8000/v1", api_key="none") + +response = client.chat.completions.create( + model="AmareshHebbar/icd10-coder-qwen25-7b-merged", + messages=[ + {"role": "system", "content": "You are an expert ICD-10 coder and insurance analyst."}, + {"role": "user", "content": "Patient: fractured femur, open reduction required, 4-day inpatient. ICD-10 codes and insurance coverage?"} + ], + max_tokens=512, + temperature=0.1, +) +print(response.choices[0].message.content) +``` + +### Using Ollama (Local / Offline) + +```bash +# Export to GGUF first (via llama.cpp or Unsloth export) +ollama create icd10-coder -f ./Modelfile +ollama run icd10-coder "Patient: appendicitis, emergency surgery. Code and coverage?" +``` + +--- + +## 🔌 Integrations Supported + +| Backend | Status | Use Case | +|---|---|---| +| HuggingFace Transformers | ✅ | Research, prototyping | +| Unsloth FastModel | ✅ | Fast inference, fine-tuning | +| vLLM | ✅ | Production API, high throughput | +| SGLang | ✅ | Structured generation | +| Ollama | ✅ | Local / offline deployment | +| Claude API (Anthropic) | 🔌 Planned | Hybrid: ICD-10 code → Claude for coverage analysis | +| Gemini API (Google) | 🔌 Planned | Multi-LLM comparison layer | +| Web Search (Tavily/Serper) | 🔌 Planned | Real-time tariff + hospital rate lookup | + +--- + +## ICD-10 Coverage + +This model has been fine-tuned across all major ICD-10-CM chapters: + +| Chapter | Description | +|---|---| +| I (A00–B99) | Infectious and parasitic diseases | +| II (C00–D49) | Neoplasms | +| III (D50–D89) | Blood and immune disorders | +| IV (E00–E89) | Endocrine, nutritional, metabolic | +| V (F01–F99) | Mental and behavioural disorders | +| IX (I00–I99) | Circulatory system diseases | +| X (J00–J99) | Respiratory diseases | +| XI (K00–K95) | Digestive system diseases | +| XIII (M00–M99) | Musculoskeletal diseases | +| XIV (N00–N99) | Genitourinary diseases | +| XIX (S00–T88) | Injuries, poisonings | +| XXI (Z00–Z99) | Health status, contact with services | + +--- + +## Limitations & Intended Use + +- This model is trained on **WHO ICD-10 baseline standards**, not on any specific insurer's proprietary rules. Coverage estimates are **indicative**, not legally binding. +- **Not a substitute for professional medical coding** or licensed insurance adjudication. +- Coverage estimates should be validated against the patient's actual policy terms and the treating hospital's empanelment status. +- Future scheme-specific models (Ayushman Bharat, Star Health, etc.) will provide more precise, policy-aware outputs. + +--- + +## Links + +- **GitHub (AxisMapper):** [https://github.com/amareshhebbar/AxisMapper](https://github.com/amareshhebbar/AxisMapper) +- **Developed by:** [AmareshHebbar](https://huggingface.co/AmareshHebbar) +- **Base model:** [unsloth/qwen2.5-7b-instruct](https://huggingface.co/unsloth/qwen2.5-7b-instruct) + +--- + +## Citation + +```bibtex +@misc{hebbar2025icd10coder, + title={ICD-10 Coder: A Fine-tuned Qwen2.5-7B for Medical Classification and Insurance Coverage Estimation}, + author={Amaresh Hebbar}, + year={2025}, + publisher={HuggingFace}, + url={https://huggingface.co/AmareshHebbar/icd10-coder-qwen25-7b-merged}, + note={Part of the AxisMapper project: https://github.com/amareshhebbar/AxisMapper} +} +``` + +--- + +
+Built with Unsloth · Trained on A5000 · Tracked with W&B · Part of AxisMapper +
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You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\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 XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- message.content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n" +} \ No newline at end of file