---
language: en
license: mit
pretty_name: Oncology AMIE Dataset
size_categories:
- n<1K
task_categories:
- text-classification
- other
task_ids:
- text-classification
- medical-diagnosis
---

# Oncology AMIE Dataset

## Dataset Description

This dataset contains detailed clinical cases of breast cancer patients, including their age, molecular phenotype (ER/PR/HER2 status), treatment status, special considerations, and ground truth treatment recommendations. The cases are divided into treatment-naive and treatment-refractory categories.

### Dataset Summary

A clinical dataset containing breast cancer cases with their descriptions, molecular phenotypes, and treatment recommendations. The dataset includes both treatment-naive and treatment-refractory cases, along with structured questions and system prompts for AI assistance.

### Features

The dataset includes the following columns:
- id: Unique identifier for each case
- type: Whether the case is treatment-naive or treatment-refractory
- age: Patient age
- molecular_phenotype: ER/PR/HER2 status
- treatment_status: Current treatment status
- special_considerations: Additional clinical considerations
- description: Detailed case description
- ground_truth: Expert-provided treatment recommendation
- system_prompt: General instruction for AI assistance
- question_all: Complete case evaluation question with structured format
- question_neoadjuvant: Specific question about neoadjuvant therapy
- question_surgery: Specific question about surgical approach
- question_adjuvant: Specific question about adjuvant therapy
- question_genetic: Specific question about genetic testing
- question_support: Specific question about psychosocial support

### Usage

This dataset can be used for:
- Training medical AI systems
- Clinical decision support development
- Medical education and training
- Research in oncology treatment protocols

### Citation

If you use this dataset, please cite:
```
@misc{onc_amie_dataset,
  title={Oncology AMIE Dataset},
  author={Gallifant, Jack},
  year={2024},
  publisher={Hugging Face}
}
```