Datasets:

AmareshHebbar
/

icd10-coder-sft

license: apache-2.0
language:
  - en
tags:
  - medical
  - icd-10
  - clinical-nlp
  - coding
  - cms
task_categories:
  - text-generation
pretty_name: ICD-10-CM Medical Coder
size_categories:
  - 10K<n<100K

ICD-10-CM Medical Coder

Part of the AxisMapper Medical AI Suite — 16 domain-specific SFT datasets for fine-tuning medical LLMs.

Built by AmareshHebbar | Studio Ilios / Humanova Minds

What this dataset does

Maps clinical descriptions to ICD-10-CM codes

Why download this

Fine-tune LLMs to automatically assign ICD-10-CM codes from clinical text. Useful for EHR automation, medical coding assistants, and clinical NLP pipelines.

Dataset stats

Split	Rows
Train	59,775
Validation	7,472
Test	7,472
Total	74,719

Data format

Every row is a messages list in chat format — compatible with Unsloth, TRL SFTTrainer, LLaMA-Factory, and any OpenAI-style fine-tuning pipeline:

{
  "messages": [
    {"role": "system",    "content": "You are a ..."},
    {"role": "user",      "content": "Patient presents with uncontrolled type 2 diabetes with diabetic nephropathy."},
    {"role": "assistant", "content": "E11.65 — Type 2 diabetes mellitus with hyperglycemia
Secondary: N08 — Glomerular disorders in diseases classified elsewhere"}
  ]
}

Data source

CMS FY2026 ICD-10-CM Tabular Order (97k billable codes) → https://www.cms.gov/medicare/coding-billing/icd-10-codes

All data is extracted from authoritative public sources. No LLM-generated or synthetic content.

Who should use this

Medical coders, clinical NLP engineers, health informatics researchers, EHR vendors.

Quick start

from datasets import load_dataset

ds = load_dataset("AmareshHebbar/icd10-coder-sft")
print(ds["train"][0])

Fine-tuning example (Unsloth)

from unsloth import FastLanguageModel
from trl import SFTTrainer
from datasets import load_dataset

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Qwen2.5-3B-Instruct",
    max_seq_length=2048,
    load_in_4bit=True,
)

dataset = load_dataset("AmareshHebbar/icd10-coder-sft", split="train")

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="messages",
    max_seq_length=2048,
)
trainer.train()

Related datasets in this collection

Dataset	Task	Rows
icd10-coder-sft	ICD-10-CM coding	74.7k
symptom-diagnoser-sft	Symptom → diagnosis	119k
clinical-summarizer-sft	SOAP summarization	30k
discharge-qa-sft	Discharge summary QA	30k
pmjay-classifier-sft	PM-JAY packages	11.1k
radiology-coder-sft	Radiology coding	25k
medical-ner-sft	Clinical NER	16.7k
hindi-medical-sft	Hindi medical QA	19.7k

Citation

@misc{axiomapper2026,
  author    = {Hebbar, Amaresh},
  title     = {AxisMapper: Medical AI Fine-tuning Dataset Suite},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/collections/AmareshHebbar/axiomapper-medical-ai-suite}
}

AxisMapper is an open-source project. Star the repo, open issues, and contribute at GitHub.