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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Published on: September 20, 2018

Medical Information Extraction Model for User-generated Content.

Fahad Kamal Alsheref¹

¹Information System Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.

Acta Informatica Medica : AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku Bih

|November 26, 2019

Summary

This summary is machine-generated.

This study introduces a model to detect medical information in user-generated content (UGC) from social media. The model, utilizing the Unified Medical Language System (UMLS), achieved high accuracy in identifying clinical data.

Keywords:

Electronic health record Facebook Social network Text similarity

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Area of Science:

Natural Language Processing
Medical Informatics
Social Media Analysis

Background:

Increasing volume of user-generated content (UGC) on social media platforms.
Potential for extracting valuable information, including user sentiments and personal life events.
Need for methods to identify specific types of information within this data.

Purpose of the Study:

To describe a model for detecting medical information within user-generated content.
To explore the application of this model in analyzing social media posts and comments.
To highlight the potential benefits of extracting clinical data.

Main Methods:

Development of a model based on the Unified Medical Language System (UMLS).
Testing the model on a dataset collected from social media platforms like Twitter and Facebook.
Utilizing Natural Language Processing techniques for information extraction.

Main Results:

The proposed model demonstrated high performance in detecting medical information.
Achieved an accuracy of 94.6% and a precision of 87%.
Successfully extracted clinical information from social media data.

Conclusions:

The study successfully extracted clinical information from user-generated content (UGC).
The Unified Medical Language System (UMLS) proved to be a suitable resource for the model.
The model shows potential for early disease detection and commercial applications in the medical field.