Certificate Course: Applied NLP In Healthcare

A program designed to introduce the applications of NLP & ML in Healthcare Predictive Analytics.

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Key Features

Live Instructor-Led Training

Certification on Completion

Use-Case Driven

In Class Hands-on

Live Q&A

First of its kind

Small batch size

Feedback driven Teaching

Intuitive Conceptual Understanding

Our Trainers

Vidya Kurada

Senior Data Scientist & Lead Instructor

Darshil Shah

Data Scientist & Instructor

Objectives

1.
Understanding the importance of NLP in Healthcare
2.
Program is designed to make students comfortable in both concepts and coding with equal emphasis
3.
Combining NLP with ML to make Text Predictive Analytics a reality
4.
Emphasis on Use-Case based approach to help learners take off with their own projects
5.
Focussed on making the program approachable by taking interactive sessions
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Outcomes

Students will learn:

  • All the elements of text pre processing (parsing to vectorization)
  • How to prepare discharge summaries for predictive analytics
  • How to perform information extraction using clinical NLP pre-trained models
  • How can students start their journey into their personal projects
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    Audience

  • Data Science and ML enthusiasts
  • Healthcare & IT Professionals
  • Analytics Professionals
  • Students of Pharmacy & Medicine

    • Pre-Requisites

    It is preferable that you know the basics of:
  • Python Programming basics
  • Packages like Pandas & Sci-Kit Learn
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    Previous
    Next

    Course Curriculum​

    • Strings
    • Conditionals
    • Loops
    • Data Structures
    • Functions
    • Data Frames
    • Group Functions
    • Data Manipulations
    • Pandas & Numpy
    • Introduction to NLP
    • Applications of NLP in Medical Research
    • Regular Expressions
    • Tokenization
    • Stemming
    • Lemmatization
    • N-Grams
    • TF-IDF Vectorization
    • Count-Vectorization
    • Hands-on Session
    • Mini Use-Case on Text Mining (100% Hands-On)
    • Brief demo on Medacy and Sci Spacy
    • Parsing Techniques
      • XML Parsing
      • PDF Parsing
      • Text Parsing 
    • Part 1: Clinical Text Mining
    • Part 2: Clinical Information Extraction using Medacy Pre-Trained Models
    • Part 3: Detection of Patient Habits using Text Classification

    Duration

    0
    Hours
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