Khyatimirani/pcos-management-patient-qa-from-eshre-guideline

Dataset Card for pcos-management-patient-qa-from-eshre-guideline

Dataset Details

Dataset Description

pcos-management-patient-qa-from-eshre-guideline is a clinically grounded conversational dataset designed to support training and evaluation of chat-based AI models for patient education in Polycystic Ovary Syndrome (PCOS).

The dataset contains structured user–assistant conversations derived from evidence-based recommendations in the International Evidence-based Guideline for the Assessment and Management of PCOS developed by the European Society of Human Reproduction and Embryology (ESHRE) and partner organizations.

Responses are written in a patient-friendly, clinically responsible tone to help models generate safe, guideline-aligned answers while avoiding prescriptive medical decision-making.

• —Curated by: Khyati Mirani
• —Language(s): English
• —License: MIT

Dataset Sources

• —Primary Source: International Evidence-based Guideline for the Assessment and Management of Polycystic Ovary Syndrome (ESHRE).

• —Organization: European Society of Human Reproduction and Embryology (ESHRE) https://www.eshre.eu

Uses

Direct Use

This dataset is intended for:

• —Training conversational healthcare AI assistants
• —Fine-tuning LLMs for patient education
• —Retrieval-augmented generation (RAG) systems
• —Safety-aligned medical chatbots
• —Research in clinical NLP

It is particularly suitable for models that must balance clinical accuracy with non-diagnostic language.

Out-of-Scope Use

This dataset should NOT be used for:

• —Automated medical diagnosis
• —Treatment prescription
• —Emergency medical decision-making
• —Replacing licensed healthcare professionals

Models trained on this dataset must present information as educational guidance rather than medical advice.

Dataset Structure

Each entry follows a chat format:

{
  "messages": [
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."}
  ]
}


## Loading the Dataset

You can load the dataset using the Hugging Face `datasets` library.

### Install

pip install datasets

from datasets import load_dataset

dataset = load_dataset("khyatimirani/pcos-management-patient-qa-from-eshre-guideline")

print(dataset)

View a sample

print(dataset["train"][0])