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Intelligent Document Processing with AWS AI/ML

You're reading from   Intelligent Document Processing with AWS AI/ML A comprehensive guide to building IDP pipelines with applications across industries

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781801810562
Length 246 pages
Edition 1st Edition
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Author (1):
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Sonali Sahu Sonali Sahu
Author Profile Icon Sonali Sahu
Sonali Sahu
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Table of Contents (16) Chapters Close

Preface 1. Part 1: Accurate Extraction of Documents and Categorization
2. Chapter 1: Intelligent Document Processing with AWS AI and ML FREE CHAPTER 3. Chapter 2: Document Capture and Categorization 4. Chapter 3: Accurate Document Extraction with Amazon Textract 5. Chapter 4: Accurate Extraction with Amazon Comprehend 6. Part 2: Enrichment of Data and Post-Processing of Data
7. Chapter 5: Document Enrichment in Intelligent Document Processing 8. Chapter 6: Review and Verification of Intelligent Document Processing 9. Chapter 7: Accurate Extraction, and Health Insights with Amazon HealthLake 10. Part 3: Intelligent Document Processing in Industry Use Cases
11. Chapter 8: IDP Healthcare Industry Use Cases 12. Chapter 9: Intelligent Document Processing – Insurance Industry 13. Chapter 10: Intelligent Document Processing – Mortgage Processing 14. Index 15. Other Books You May Enjoy

Handling documents with an FHIR data store

Now let’s see what happens when our input for HealthLake is not in FHIR format, but is a document instead. Amazon HealthLake currently only supports data in FHIR format. What if we need to process document-based health data along with FHIR data? Can we still create a centralized scalable FHIR data store for health data?

And the answer is yes. We can use Amazon Textract to get raw text from the document and convert it to an FHIR resource (a DocumentReference resource). This DocumentReference FHIR resource can then be input into Amazon HealthLake with additional FHIR resources.

This is a three-step process:

  1. Extracting data from the document with Amazon Textract
  2. Creating a DocumentReference FHIR resource from the extracted Textract response
  3. Ingesting the DocumentReference FHIR resource to Amazon HealthLake

You can see the architecture in the following figure:

Figure 7.15 – Document...

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