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Information Extraction from Free Text Data in Health
  • Level Professional
  • المدة 24 ساعات hours
  • الطبع بواسطة University of Michigan
  • Offered by

عن

In this MOOC, you will be introduced to advanced machine learning and natural language processing techniques to parse and extract information from unstructured text documents in healthcare, such as clinical notes, radiology reports, and discharge summaries. Whether you are an aspiring data scientist or an early or mid-career professional in data science or information technology in healthcare, it is critical that you keep up-to-date your skills in information extraction and analysis. To be successful in this course, you should build on the concepts learned through other intermediate-level MOOC courses and specializations in Data Science offered by the University of Michigan, so you will be able to delve deeper into challenges in recognizing medical entities in health-related documents, extracting clinical information, addressing ambiguity and polysemy to tag them with correct concept types, and develop tools and techniques to analyze new genres of health information. By the end of this course, you will be able to: Identify text mining approaches needed to identify and extract different kinds of information from health-related text data Create an end-to-end NLP pipeline to extract medical concepts from clinical free text using one terminology resource Differentiate how training deep learning models differ from training traditional machine learning models Configure a deep neural network model to detect adverse events from drug reviews List the pros and cons of Deep Learning approaches."

الوحدات

Course Introduction

    • 1 Videos
    • 3 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Meet your Classmates

1 Videos

  • Welcome to Information Extraction from Free Text Data in Health

3 Readings

  • Syllabus
  • Community Engagement Rules
  • Help Us Learn More About You

Information Extraction: Getting Started

    • 3 Videos
Show info about module content

3 Videos

  • What is Information Extraction? | Part 1
  • What is Information Extraction? | Part 2
  • Information Extraction on Formatted Text

Information Extraction: Dates and Lists

    • 3 Videos
    • 2 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Week 1 | What is Information Extraction Quiz

2 Discussions

  • Exercise 1: Variety of Date Formats
  • Exercise 2: Power of Lists

3 Videos

  • Identifying Dates
  • Using Curated Lists for Information Extraction
  • Evaluation Metrics

Information Extraction: Hands-On Application

    • 1 Videos
    • 1 Programming
Show info about module content

1 Programming

  • Week 1 Hands-On Exercise

1 Videos

  • Hands-On Exercise Demo

Medical Natural Language Processing

    • 2 Videos
    • 1 Discussion
Show info about module content

1 Discussions

  • Applications of Language Processing Steps in Medicine

2 Videos

  • Medical Natural Language Processing | Part 1
  • Medical Natural Language Processing | Part 2

Health Ontology Resources

    • 3 Videos
    • 1 Discussion
Show info about module content

1 Discussions

  • Health Ontology Resources: Building a Concept Extraction Pipeline

3 Videos

  • Health Ontology Resources | Part 1
  • Health Ontology Resources | Part 2
  • Health Ontology Resources | Part 3

Named Entity Recognition (NER): Hands-On Application

    • 1 Videos
    • 1 Programming
Show info about module content

1 Programming

  • Week 2 Hands-On Exercise

1 Videos

  • Hands-On Exercice Demo

Medical Named Entity Extraction

    • 2 Videos
    • 1 Discussion
Show info about module content

1 Discussions

  • Building De-Identification Toolkit

2 Videos

  • Introduction to Medical Named Entity Extraction
  • Medical Named Entity Extraction

Sequence Labeling and Hidden Markov Models

    • 2 Videos
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Hidden Markov Models: Knowledge Check

1 Discussions

  • Hidden Markov Models and Selected Applications in Speech Recognition

2 Videos

  • Sequence Labeling
  • Hidden Markov Models

Conditional Random Fields & NER Features

    • 2 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Designing Features for a Conditional Random Fields Model: Knowledge Check

2 Videos

  • Conditional Random Fields
  • NER Features

Sequential Classification: Hands-On Application

    • 1 Videos
    • 1 Programming
Show info about module content

1 Programming

  • Week 3 Hands-On Exercise

1 Videos

  • Hands-On Exercice Demo

Deep Learning & Perceptron : Simplest Neural Network

    • 2 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Perceptron: Knowledge Check

2 Videos

  • What is Deep Learning?
  • Perceptron: Simplest Neural Network

Deep Neural Networks

    • 2 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Deep Neural Network Models: Knowledge Check

2 Videos

  • Deep Neural Networks
  • Deep Learning: Applications

Deep Learning: Hands-On Application

    • 1 Videos
    • 1 Readings
    • 1 Programming
Show info about module content

1 Programming

  • Week 4 Hands-On Exercise

1 Videos

  • Hands-On Exercice Demo

1 Readings

  • Post-Course Survey

Auto Summary

Discover "Information Extraction from Free Text Data in Health," a professional-level course by Coursera, led by the University of Michigan. This course delves into advanced machine learning and NLP techniques to extract valuable insights from unstructured healthcare texts like clinical notes and radiology reports. Ideal for data scientists and IT professionals in healthcare, it builds on intermediate data science concepts. Over 1440 minutes, you'll learn to create NLP pipelines, train deep learning models, and analyze health information. Available via Starter, Professional, and Paid subscriptions, this course will elevate your expertise in health data analysis.

V. G. Vinod Vydiswaran
Instructor

V. G. Vinod Vydiswaran