Information Extraction from Free Text Data in Health
Learn advanced NLP techniques to extract and analyze health information from unstructured clinical text data.
Learn advanced NLP techniques to extract and analyze health information from unstructured clinical text data.
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.
Instructors:
English
21 languages available
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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
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
Instructor
Innovator in Health Informatics and Natural Language Processing
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
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