Book Image

Intelligent Document Processing with AWS AI/ML

By : Sonali Sahu
Book Image

Intelligent Document Processing with AWS AI/ML

By: Sonali Sahu

Overview of this book

With the volume of data growing exponentially in this digital era, it has become paramount for professionals to process this data in an accelerated and cost-effective manner to get value out of it. Data that organizations receive is usually in raw document format, and being able to process these documents is critical to meeting growing business needs. This book is a comprehensive guide to helping you get to grips with AI/ML fundamentals and their application in document processing use cases. You’ll begin by understanding the challenges faced in legacy document processing and discover how you can build end-to-end document processing pipelines with AWS AI services. As you advance, you'll get hands-on experience with popular Python libraries to process and extract insights from documents. This book starts with the basics, taking you through real industry use cases for document processing to deliver value-based care in the healthcare industry and accelerate loan application processing in the financial industry. Throughout the chapters, you'll find out how to apply your skillset to solve practical problems. By the end of this AWS book, you’ll have mastered the fundamentals of document processing with machine learning through practical implementation.
Table of Contents (16 chapters)
Part 1: Accurate Extraction of Documents and Categorization
Part 2: Enrichment of Data and Post-Processing of Data
Part 3: Intelligent Document Processing in Industry Use Cases

Learning to use Amazon Comprehend Medical for medical ontology

What is medical ontology linking? It is the method of identifying medical information and mapping it to standard medical codes and concepts. For example, medical conditions are linked to ICD-10-CM codes. Moreover, medications are mapped to RxNorm codes. Also, Amazon Comprehend Medical results infer SNOMED CT codes to provide medical insights, conditions, affected anatomy, test treatments, and procedures. Amazon Comprehend Medical also supports entity traits. For example, “Patient refused to take medication” has a negation entity trait.

Now let’s see an example with a sample prescription document:

  1. Use the following code to display the sample prescription document:
    documentName = "prescription.png"

You can see the document in the following figure:

Figure 5.19 – Sample medical prescription

  1. Get raw text...