Emergency Department Case Notes Dataset

Dataset Summary

This dataset contains 43 emergency department medical case notes, sourced from MTSamples, a widely recognized repository of medical transcription samples. Each entry includes a case title, category, and source link to a PDF document with detailed notes.

This dataset is highly valuable for medical research, categorization, and analysis. The structured format allows for efficient information retrieval and classification, making it a well-maintained reference for academic and clinical research. A rigorous validation process ensures credibility, making this dataset reliable for further study and application.

Dataset Structure

The dataset consists of the following key columns: • Title: The medical case or note title. • Category: The classification under which the case falls (Emergency Department Notes). • Source Link: Direct links to case notes in PDF format from MTSamples.

Dataset Statistics • Total Entries: 43 • Categories: Emergency Department Notes • Source: MTSamples (PDF-based medical transcription samples) • No Missing Data: All fields are complete

Common Medical Cases

The dataset captures real-world emergency department cases, focusing on: • ER Visits: Cases related to dizziness, falls, and drug ingestion. • Respiratory Issues: Difficulty breathing and related conditions. • Pain Management: Conditions such as dental pain. • Substance-Related Cases: Drug ingestion, including ecstasy consumption. • General Emergency Cases: Conditions requiring urgent medical intervention.

Data Collection & Validation • Source: Extracted from MTSamples, ensuring high-quality transcription data. • Categorization: All cases fall under Emergency Department Notes for consistency. • Validation: Checked for completeness, metadata accuracy, and accessibility of links.

Usage

This dataset is ideal for: • Medical Research: Studying common emergency cases. • Healthcare Training: Enhancing clinical decision-making. • Machine Learning Applications: Categorizing medical case notes for NLP models. • Academic Research: Supporting studies in emergency medicine.

License & Citation

Ensure proper attribution to MTSamples when using this dataset.