Distilbart Med Summary

🚀 Medical Summary Generation with BART

This project utilizes a DistilBART model to generate concise and informative medical summaries from input text.

🚀 Quick Start

The DistilBART-Med-Summary Generator, built with the Hugging Face Deep Learning Container, is designed to generate medical summaries from input text. This README offers guidance on using the model, details about its architecture, and information on where to find downloads.

✨ Features

• The DistilBART model is trained to understand medical data and generate accurate summaries.
• It is built using the Hugging Face Deep Learning Container, ensuring easy deployment and use.

📦 Installation

To use the model for medical summary generation, you need to install the required dependencies:

• pip install transformers
• pip install torch
• pip install datasets

💻 Usage Examples

Basic Usage

from transformers import pipeline
summarizer = pipeline("summarization", model="Mahalingam/DistilBart-Med-Summary")

conversation = '''write the below JSON into normal text
{
  "Sex": "M",
  "ID": 585248,
  "DateOfBirth": "08/10/1995",
  "Age": "28 years",
  "VisitDate": "09/25/2023",
  "LogNumber": 6418481,
  "Historian": "Self",
  "TriageNotes": ["fever"],
  "HistoryOfPresentIllness": {
    "Complaint": [
      "The patient presents with a chief complaint of chills.",
      "The problem is made better by exercise and rest.",
      "The patient also reports change in appetite and chest pain/pressure as abnormal symptoms related to the complaint."
    ]
  }
}                                           
'''
nlp(conversation)

📚 Documentation

Model Details

📄 Contact

For any inquiries or support related to this model, feel free to contact:

• Name: Mahalingam Balasubramanian
• Email: mahalingamb.1978@gmail.com