AmareshHebbar

icd10-coder-qwen25-7b-merged

README

License: apache-2.0

What Is This?

ICD-10-Coder is the first model in a long-term initiative — 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:

text

Phase 1 (Now)  → WHO ICD-10 base model (this model)                 Universal code prediction + coverage logicPhase 2        → Fine-tune per scheme (StarHealth, PM-JAY, ESIC, etc.)                 Each model specialises in one insurer's rule setPhase 3        → MoE Router                 Given a patient + insurer, route to the right specialist modelPhase 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

Table with columns: Property, Value
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 Infrastructure

This model was trained using the 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:

Assign the correct ICD-10 code(s) — primary diagnosis, secondary conditions, procedure codes
Explain the WHO classification logic — why this code, what the category means, adjacent codes
Estimate WHO-level insurance coverage — standard reimbursement brackets, admission duration requirements, procedure eligibility
Flag restrictions — minimum admission days, co-morbidity requirements, pre-authorisation triggers
Support multi-condition scenarios — comorbidities, complications, dual coding

Example input:

text

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):

text

Primary Code: K35.2 — Acute appendicitis with generalised peritonitisProcedure Code: 0DTJ4ZZ — Resection of appendix, percutaneous endoscopic approachWHO 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 vllmvllm 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 ./Modelfileollama run icd10-coder "Patient: appendicitis, emergency surgery. Code and coverage?"

🔌 Integrations Supported

Table with columns: Backend, Status, Use Case
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

ICD-10 Coverage

This model has been fine-tuned across all major ICD-10-CM chapters:

Table with columns: Chapter, Description
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)

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.

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}}

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Model Details

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README

License: apache-2.0

What Is This?

ICD-10-Coder is the first model in a long-term initiative — AxisMapper — to build an AI-native insurance intelligence layer for the Indian and global healthcare ecosystem.

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:

text

Phase 1 (Now)  → WHO ICD-10 base model (this model)                 Universal code prediction + coverage logicPhase 2        → Fine-tune per scheme (StarHealth, PM-JAY, ESIC, etc.)                 Each model specialises in one insurer's rule setPhase 3        → MoE Router                 Given a patient + insurer, route to the right specialist modelPhase 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

Model Details

Table with columns: Property, Value
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 Infrastructure

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

What This Model Does

Given a clinical description or patient scenario, this model will:

Assign the correct ICD-10 code(s) — primary diagnosis, secondary conditions, procedure codes
Explain the WHO classification logic — why this code, what the category means, adjacent codes
Estimate WHO-level insurance coverage — standard reimbursement brackets, admission duration requirements, procedure eligibility
Flag restrictions — minimum admission days, co-morbidity requirements, pre-authorisation triggers
Support multi-condition scenarios — comorbidities, complications, dual coding

Example input:

text

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):

text

Primary Code: K35.2 — Acute appendicitis with generalised peritonitisProcedure Code: 0DTJ4ZZ — Resection of appendix, percutaneous endoscopic approachWHO 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 vllmvllm serve "AmareshHebbar/icd10-coder-qwen25-7b-merged" --max-model-len 2048

pip install vllmvllm 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 ./Modelfileollama run icd10-coder "Patient: appendicitis, emergency surgery. Code and coverage?"

# Export to GGUF first (via llama.cpp or Unsloth export)ollama create icd10-coder -f ./Modelfileollama run icd10-coder "Patient: appendicitis, emergency surgery. Code and coverage?"

🔌 Integrations Supported

Table with columns: Backend, Status, Use Case
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

ICD-10 Coverage

This model has been fine-tuned across all major ICD-10-CM chapters:

Table with columns: Chapter, Description
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)

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.

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}}

icd10-coder-qwen25-7b-merged

README

What Is This?

The Bigger Vision: AxisMapper

Model Details

Training Infrastructure

What This Model Does

Quickstart

Using Transformers (Pipeline)

Using Unsloth (Recommended for inference speed)

Using vLLM (Production / High Throughput)

Using Ollama (Local / Offline)

🔌 Integrations Supported

ICD-10 Coverage

Limitations & Intended Use

Links

Citation

Explore FriendliAI today

README

What Is This?

The Bigger Vision: AxisMapper

Model Details

Training Infrastructure

What This Model Does

Quickstart

Using Transformers (Pipeline)

Using Unsloth (Recommended for inference speed)

Using vLLM (Production / High Throughput)

Using Ollama (Local / Offline)

🔌 Integrations Supported

ICD-10 Coverage

Limitations & Intended Use

Links

Citation