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Foundations of mining non-structured medical data

Learn Big Data in healthcare: Explore challenges and opportunities in mining unstructured medical data for valuable insights.

Learn Big Data in healthcare: Explore challenges and opportunities in mining unstructured medical data for valuable insights.

This course provides a comprehensive introduction to mining non-structured medical data using Big Data techniques. Learn about the types of data generated in healthcare, challenges in processing unstructured text and images, and opportunities for extracting meaningful information. Explore Natural Language Processing in medical contexts, medical image analysis, and data mining techniques for structured information. Gain insights into the European healthcare context and the potential impact of data analytics in various health sectors.

3.8

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Foundations of mining non-structured medical data

This course includes

6 Hours

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

2,436

What you'll learn

  • Understand the importance of Big Data in the medical domain

  • Identify challenges in processing unstructured medical data (text and images)

  • Learn Natural Language Processing techniques for medical texts

  • Explore medical image analysis concepts and applications

  • Understand data mining techniques for structured medical information

  • Gain insights into the European healthcare context and data analytics opportunities

Skills you'll gain

Big Data
healthcare analytics
NLP
medical imaging
data mining

This course includes:

4 Hours PreRecorded video

5 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

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There are 5 modules in this course

This course offers a comprehensive introduction to mining non-structured medical data using Big Data techniques. It covers the importance of the medical domain in the European context, types of data generated in healthcare, and challenges in processing unstructured text and images. Students will learn about Natural Language Processing in medical contexts, including tools, frameworks, and vocabularies. The course also explores medical image analysis, introducing digital image concepts and their application to medical imaging. Additionally, it covers data mining techniques for structured information, including classification, clustering, and association rules. By the end of the course, participants will have a solid foundation in the opportunities and challenges of applying Big Data analytics to healthcare data.

Introduction

Module 1 · 1 Hours to complete

Challenges in unstructured data in health domain

Module 2 · 48 Minutes to complete

NLP in medical domain

Module 3 · 1 Hours to complete

Medical Image Analysis

Module 4 · 1 Hours to complete

Data Analysis of structured information

Module 5 · 1 Hours to complete

Fee Structure

Payment options

Financial Aid

Instructors

Alejandro Rodríguez González
Alejandro Rodríguez González

2,965 Students

1 Course

From Aspiring Master's Student to Passionate Professor in Computer Science

Alejandro began his career in 2008 when he moved from Oviedo, Asturias, to Madrid, intending to work at the university for just a year while pursuing his master's degree. However, he became captivated by research, making it his lifelong commitment and choosing to stay in Madrid. Despite concerns about the challenges of a career in science—such as low pay and instability—he has no regrets. Alejandro is now a professor in Computer Languages and Systems and Software Engineering at the Technical University of Madrid.

Consuelo Gonzalo-Martín
Consuelo Gonzalo-Martín

2,965 Students

1 Course

Expert in Image Processing and Neural Networks for Remote Sensing and Healthcare Applications.

Consuelo Gonzalo Martín earned her B.A. in Physics from Salamanca University in 1986 and her Ph.D. from the Complutense University of Madrid in 1989. Since 1993, she has served as an Assistant Professor in the Department of Architecture and Technology of Computers at the Universidad Politécnica de Madrid. Her research focuses on image processing using pixels and objects, as well as artificial neural networks, particularly in remote sensing, medical imaging, and facial recognition. She has developed numerous algorithms for optical image fusion and artificial neural networks such as ART and SOM. In 2012, she joined the MIDAS (Data Mining and Simulation) research group at UPM’s Center for Biomedical Technology, contributing to R&D projects in text and image mining within healthcare. Throughout her career, she has directed over 12 funded research projects, participated in around 15 others, co-authored 25 publications in high-impact international journals, and presented at 40 international conferences.

Foundations of mining non-structured medical data

This course includes

6 Hours

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

2,436

Testimonials

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3.8 course rating

24 ratings

Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.