Book Image

Natural Language Processing with AWS AI Services

By : Mona M, Premkumar Rangarajan
Book Image

Natural Language Processing with AWS AI Services

By: Mona M, Premkumar Rangarajan

Overview of this book

Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production. To start with, you'll understand the importance of NLP in today’s business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you'll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You'll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you'll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you'll learn best practices for auto-scaling your NLP inference for enterprise traffic. Whether you're new to ML or an experienced practitioner, by the end of this NLP book, you'll have the confidence to use AWS AI services to build powerful NLP applications.
Table of Contents (23 chapters)
1
Section 1:Introduction to AWS AI NLP Services
5
Section 2: Using NLP to Accelerate Business Outcomes
15
Section 3: Improving NLP Models in Production

Understanding clinical data with Amazon Comprehend Medical

In this section, we will talk about how you can use Amazon Comprehend Medical to gain insights from a valid medical intake form. We covered Amazon Comprehend's features in Chapter 3, Introducing Amazon Comprehend. In this chapter, we will learn how to use the Amazon Comprehend Medical Entity API to extract entities such as patient diagnosis and PHI data types such as claim ID from the medical intake form. Let's get started:

  1. Go back to your notebook and run the following cell to use the Comprehend Medical boto3 API:
    comprehend = boto3.client(service_name='comprehendmedical')
  2. Now, we will use the comprehend.detect_entities_v2 API (https://docs.aws.amazon.com/comprehend/latest/dg/API_medical_DetectEntitiesV2.html) to analyze the clinical text data from medical intake forms and return entities specific to the medical text, such as type or diagnosis. Run the following cell to see what entities we...