Khyatimirani/egg-freezing-clinical-question-answers

Dataset Card for Egg Freezing Patient Query Dataset

Dataset Details

Dataset Description

The Egg Freezing Patient Query Dataset is a small curated dataset of question–answer pairs representing common questions asked by patients considering or undergoing oocyte cryopreservation (egg freezing).

The dataset is designed to simulate real patient queries that occur during fertility consultations and fertility treatment journeys. Questions cover topics such as eligibility, procedure steps, medication side effects, recovery, timelines, costs, risks, and success rates.

The primary goal of the dataset is to support the development and evaluation of conversational AI assistants that help answer routine patient questions in fertility care while providing educational guidance.

The dataset contains structured patient questions paired with medically grounded, educational responses written in simple language suitable for patient communication.

• —Curated by: Khyati Mirani
• —Funded by: Self-funded research project
• —Shared by: Khyati Mirani
• —Language(s) (NLP): English
• —License: Apache 2.0

Dataset Sources

• —Repository: Hugging Face Dataset Repository
• —Paper: Egg freezing guideline by WHO
• —Demo: N/A

Uses

Direct Use

This dataset is intended for:

• —Training or fine-tuning conversational AI assistants for fertility education
• —Evaluating question-answering systems in reproductive health domains
• —Building patient support chatbots for fertility clinics
• —Studying common patient concerns during egg freezing journeys
• —Prototyping healthcare conversational UX

The dataset focuses specifically on informational support, not clinical decision-making.

Out-of-Scope Use

This dataset should not be used for:

• —Automated medical diagnosis
• —Clinical treatment recommendations
• —Emergency medical advice
• —Replacing licensed healthcare professionals
• —Building systems that provide personalized medical prescriptions

The responses are educational and generalized and should not be interpreted as medical advice.

Dataset Structure

The dataset consists of structured question–answer pairs.

Each example contains:

• —question: A natural language query representing a patient concern about egg freezing.
• —answer: A medically informed, educational response written in simple and supportive language.

Example structure:

Dataset Structure

{ "question": "At what age should I consider freezing my eggs?", "answer": "Egg freezing is generally more effective when done before the mid-30s because egg quality declines with age. Many doctors recommend considering egg freezing between ages 28 and 35 if you wish to delay pregnancy. A fertility specialist can evaluate your ovarian reserve and help determine whether it may be a good option for you." }

[More Information Needed]

The dataset currently contains fewer than 1,000 examples.

Dataset Creation

Curation Rationale

Patients considering egg freezing often have many repetitive questions during consultations and follow-ups. Fertility clinics and doctors frequently receive similar queries about procedures, medications, safety, and outcomes.

This dataset was created to model those recurring questions and provide structured educational answers that could support conversational AI tools designed to assist patients between doctor visits.

The goal is to reduce information gaps while still keeping healthcare professionals in the loop for medical decision-making.

Source Data

The dataset was created using a combination of:

• —Publicly available fertility education resources
• —Patient education materials from fertility clinics
• —WHO reproductive health guidance
• —General fertility knowledge commonly discussed during consultations

The questions are synthetic but realistic, designed to represent common patient concerns.

Data Collection and Processing

Questions were generated to reflect different stages of the egg freezing journey, including:

• —Considering egg freezing
• —Pre-treatment evaluation
• —Hormonal stimulation phase
• —Egg retrieval procedure
• —Post-procedure recovery
• —Storage and future use

Responses were written in simplified language suitable for patient communication. Technical medical terminology was minimized or explained where possible.

All data points were manually reviewed to ensure clarity and factual consistency.

Who are the source data producers?

The questions were generated and curated by the dataset author based on common fertility education topics and patient concerns documented in publicly available fertility resources.

No real patient conversations or private medical records were used in this dataset.

Annotations

Annotation process

The dataset uses a simple question–answer annotation format.

Each example was manually reviewed to ensure:

• —clarity of the question
• —medically reasonable educational response
• —non-diagnostic tone
• —patient-friendly language

Who are the annotators?

The dataset was created and reviewed by the dataset curator with guidance from publicly available reproductive health materials.

Future versions may include validation by fertility clinicians.

Personal and Sensitive Information

The dataset does not contain any personally identifiable information (PII).

All questions are synthetic and do not correspond to real individuals. The dataset contains reproductive health topics but no patient records, names, locations, or identifiable details.

Bias, Risks, and Limitations

This dataset has several limitations:

• —It represents general fertility education rather than personalized medical care.
• —The dataset may reflect fertility guidance common in certain healthcare systems and may not fully represent global practices.
• —Medical knowledge evolves, and recommendations may change over time.
• —The dataset size is small (<1K), which limits coverage of all possible patient questions.

AI systems trained on this dataset should not be used as a substitute for professional medical advice.

Recommendations

Users of this dataset should:

• —Treat the responses as educational guidance only
• —Ensure AI systems include clear disclaimers encouraging consultation with healthcare professionals
• —Avoid deploying models trained on this dataset for clinical decision-making without expert oversight

Citation

BibTeX:

[More Information Needed]

APA:

Mirani, K. (2026). Egg Freezing Patient Query Dataset. Hugging Face.

Glossary

Egg Freezing (Oocyte Cryopreservation): A fertility preservation technique where eggs are retrieved from the ovaries, frozen, and stored for future use.

Ovarian Stimulation: The use of hormonal medications to stimulate the ovaries to produce multiple eggs during a treatment cycle.

Egg Retrieval: A minor medical procedure in which mature eggs are collected from the ovaries.

Dataset Card Authors

Khyati Mirani

Dataset Card Contact

For questions or collaboration inquiries, please contact the dataset author via Hugging Face profile.