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)
1
Part 1: Accurate Extraction of Documents and Categorization
6
Part 2: Enrichment of Data and Post-Processing of Data
10
Part 3: Intelligent Document Processing in Industry Use Cases

Summary

In this chapter, we discussed the fundamentals of FHIR and how to use it in the healthcare industry to solve challenges such as healthcare data interoperability. We also discussed Amazon HealthLake and its core features for storing, transforming, and analyzing health data. Amazon HealthLake’s NLP models interpret medical insights such as medical condition, medication, dosage, medical ontology linking, and more from health data, which can be further leveraged to create additional models with Amazon SageMaker or visualizations.

We then walked through the console and code to see how to create an Amazon HealthLake FHIR data store and how to input FHIR resources into our data store. We also discussed a sample architecture and implementation to ingest document-based health data into Amazon HealthLake to create a centralized, secure, scalable, HIPAA-eligible health data lake.

In the next chapter, we will extend the discussion to healthcare data interoperability. We will...