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Information Extraction from Free Text Data in Health
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Information Extraction from Free Text Data in Health

Learn advanced NLP techniques to extract and analyze health information from unstructured clinical text data.

Course Cost

₹ 2,699

Intermediate

Skill Level

22 Hours

Self-paced lessons

This course introduces advanced machine learning and natural language processing techniques for parsing and extracting information from unstructured text documents in healthcare. Designed for aspiring data scientists and early to mid-career professionals in data science or IT in healthcare, the course covers critical skills in information extraction and analysis. Students will learn to identify and extract different types of information from health-related text data, create end-to-end NLP pipelines for extracting medical concepts from clinical free text, and configure deep neural network models for specific healthcare applications. The course emphasizes practical skills and hands-on experience, preparing participants to tackle real-world challenges in healthcare data analysis.

V. G. Vinod Vydiswaran

V. G. Vinod Vydiswaran

English

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English

V. G. Vinod Vydiswaran

V. G. Vinod Vydiswaran

What you'll learn

  • Identify and apply text mining approaches for health-related text data

  • Create an end-to-end NLP pipeline for medical concept extraction

  • Develop machine learning models for sequential classification tasks

  • Configure deep neural networks for healthcare applications

  • Evaluate information extraction techniques using various metrics

Skills you'll gain

information extraction
NLP
healthcare data
machine learning
deep learning

This course includes:

4 Hours PreRecorded video

5 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

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There are 4 modules in this course

This course on Information Extraction from Free Text Data in Health covers four key modules: Introduction to Information Extraction, Named Entity Recognition (NER), Sequential Classification, and Advanced Approaches to NER in Health. Students will learn to apply text mining techniques to health-related documents, use terminology resources for medical concept extraction, develop machine learning models for sequential classification tasks, and implement deep learning approaches for advanced information extraction. The course emphasizes practical skills through hands-on exercises and programming assignments, preparing participants to tackle real-world challenges in healthcare data analysis using state-of-the-art NLP and machine learning techniques.

What is Information Extraction?

Module 1 · 6 Hours to complete

Named Entity Recognition (NER)

Module 2 · 5 Hours to complete

Sequential Classification

Module 3 · 6 Hours to complete

Introduction to Advanced Approaches to NER in Health

Module 4 · 5 Hours to complete

Fee Structure

Payment options

Financial Aid

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

V. G. Vinod Vydiswaran
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4.2

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3,809

Reviews

1,51,881

Students

2

Courses

Dr. V. G. Vinod Vydiswaran is an Assistant Professor at the University of Michigan, holding positions in both the Medical School's Department of Learning Health Sciences and the School of Information. His research focuses on critical areas such as information trustworthiness, large-scale text mining, natural language processing (NLP), and machine learning. Dr. Vydiswaran's current work involves mining and analyzing health information from diverse sources, including scientific literature, community health forums, and social networks, with a particular emphasis on assessing the credibility of online medical information and its implications for healthcare.In addition to his research endeavors, Dr. Vydiswaran teaches courses on applied text mining and information extraction in health contexts, equipping students with essential skills to navigate and analyze health data effectively. His innovative applications of algorithmic models aim to tackle real-world challenges in healthcare, making significant contributions to the field of health informatics and enhancing the understanding of how information impacts patient care and health outcomes
Information Extraction from Free Text Data in Health

Intermediate

Skill Level

22 Hours

Self-paced lessons

Course Cost

₹ 2,699

Completion

CERTIFICATE

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.