Datasets:

swj0419
/

qa-pairs-mvd-10k_split2

license: mit
task_categories:
  - question-answering
  - multiple-choice
language:
  - en
tags:
  - medical
  - healthcare
  - ehr
  - diagnosis
  - medication
  - clinical
size_categories:
  - 10K<n<100K

Medical Question Answering Dataset (QA Pairs MVD 10K)

Dataset Description

This dataset contains medical question-answering tasks based on Electronic Health Record (EHR) data. The dataset focuses on three main prediction tasks in clinical settings:

Missing Medication MCQ: Predicting which medication should be added to a patient's current regimen
Next Diagnosis MCQ: Predicting the most likely future diagnosis for a patient
Next Measurement Value MCQ: Predicting future laboratory or vital sign values

Dataset Structure

Data Splits

Split	Examples
Train	17,158
Validation	3,754
Test	3,683
Total	24,595

Data Fields

prompt: The full question prompt including patient medical history
prompt_type: Type of question (missing_medication_mcq, next_diagnosis_mcq, next_measurement_value_mcq)
choices: List of multiple choice options (typically 5 options A-E)
answer_idx: Index of the correct answer (0-based)
completion: The correct answer choice letter (A, B, C, D, or E)
id: Unique identifier for each example

Example

{
  "prompt": "You are an assistant tasked with analyzing medical histories to determine which medication is missing from the patient's current regimen....",
  "prompt_type": "missing_medication_mcq",
  "choices": ["Cisplatin 50 MG Injection", "Tacrine 10 MG Oral Capsule", ...],
  "answer_idx": 4,
  "completion": "E",
  "id": "2543984390693637980missing_medication_mcq"
}

Task Types

1. Missing Medication MCQ

Analyzes a patient's medical history including demographics, visits, measurements, procedures, and current medications to predict which medication should be added to their regimen.

2. Next Diagnosis MCQ

Predicts the most likely future diagnosis based on a patient's medical trajectory and history.

3. Next Measurement Value MCQ

Predicts future laboratory values or vital signs based on historical measurement trends.

Patient Data Structure

Each prompt includes structured patient data with:

Demographics: Race, gender, year of birth
Visit History: Outpatient visits, ER visits with dates
Measurements: Height, weight, BMI, blood pressure, lab values with timestamps
Procedures: Medical procedures performed with dates
Medications: Current and past medications with start/end dates
Diagnoses: Prior medical conditions with dates

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("your_username/qa-pairs-mvd-10k")

# Access different splits
train_data = dataset['train']
val_data = dataset['validation'] 
test_data = dataset['test']

# Example usage
example = train_data[0]
print(f"Question type: {example['prompt_type']}")
print(f"Prompt: {example['prompt'][:200]}...")
print(f"Choices: {example['choices']}")
print(f"Correct answer: {example['completion']}")

Ethical Considerations

This dataset contains synthetic or anonymized medical data. Users should:

Ensure compliance with healthcare data regulations (HIPAA, etc.)
Use the dataset responsibly for research and educational purposes
Not use for actual medical diagnosis without proper validation
Consider potential biases in the synthetic data generation process

Citation

If you use this dataset in your research, please cite:

@dataset{qa_pairs_mvd_10k,
  title={Medical Question Answering Dataset (QA Pairs MVD 10K)},
  year={2024},
  url={https://huggingface.co/datasets/your_username/qa-pairs-mvd-10k}
}

License

This dataset is released under the MIT License.