arpine-aikoda/amazon-textract-and-comprehend-medical-document-processing

AWS ML Healthcare Workshop

Introduction

Machine learning in healthcare

The advent of Machine learning is undoubtfully speeding up the medical development, such as new drug discovery and manufacturing, automating the diagnosis through computer vision, personalized treatments and improvements to organization of patients’ health records.

The availability of large amount of clinical data is opening up a lot new opportunities for digital transformation in the healthcare industry. Such as patient management optimization, AI predictive diagnosis and forecasting healthcare trajectories. However, patients’ health records have long been captured and kept as hard copies. In fact, nearly 20% of the doctors still prefer to use paper medical records. Such a phenomenon have greatly affected the pace of digitalization and knowledge sharing, thus limiting the ability to realize the potential from the wealth of medical data. When medical records are digitized, doctors’ notes are often captured in free text and is usually difficult to comprehend and extract meaningful data for analysis. To address the challenges of digitalization of medical records, we have developed a workshop that will guide you to:

1. Build an medical document processing pipeline with Amazon Textract and Comprehend Medical
2. Build, train and deploy a classification machine learning model with medical data extracted from the medical document processing pipeline

Objectives:

By the end of this workshop, you would have learnt how to:

1. Build, train and deploy a classification model with medical notes using Amazon SageMaker notebook
2. Extract textual data from PDF reports using Amazon Textract
3. Extract medical data from textual doctor’s notes using Amazon Sagemaker Comprehend
4. Build a pipeline of automatic processing of medical in pdf format and predicting further treatment with Amazon Lambda, Amazon Textract, Amazon Comprehend Medical and SageMaker Endpoint

The template to be deployed contains all of the codes and data you need to finish the workshop. To understand AWS ML services better, you are highly encouraged to write some of the code yourselves. The notebooks have some cells where the you are asked to complete a challenge.

Lab Instruction

[Workshop Link Placeholder]

Deploy your Working Environment

The first step is to deploy a Cloudformation template that will perform most of the initial setup for you.

1. Download the cloudformation template

Go to the following URL https://raw.githubusercontent.com/dalacan/aws-ml-healthcare-workshop/master/aws-ml-healthcare-worshop.yaml, right click 'Save As' and download the cloudformation template.

Note Make sure you save the file as a .yaml file.

2. Create a new cloud formationstack

In another browser window or tab, login to your AWS account. Once you have done that, open the link below in a new tab to start the process of deploying the items you need via CloudFormation.

3. Upload the cloud formation template

Select Upload a template, click Choose file and select the cloudformation template file you've just downloaded and then click Next.

4. Specify stack details

In this section, optionally specify the following options:

    1. Stack Name - Change the stack name to something more relevant if required.
    2. Notebook Name - Change the name of your SageMaker notebook which you will be using if required.
    3. Volumen Size - Set the size of SageMaker EBS volume (default is 10GB). If you expect to load a larger dataset (i.e. if you want to reuse this lab to experiment with larger dataset), increase this accordingly.

When you're done, click the Next button at the bottom of the page

5. Configure stack options

All of the defaults in this section will be sufficient to complete the lab. If you have any custom requirements, please alter as required. Once you're done, click the Next button to continue.

Finally, in the next section, scroll to the bottom of the page and check the checkbox to enable the template to create IAM resources and click the Create stack button.

It will take a few minutes to provision the resources required for the lab. Once it is completed, navigate to the SageMaker service by clicking Services in tht top of the console and then search for SageMaker and click on the service.

6. Launch the SageMaker notebook

Click on Notebook instance and open the aws-ml-healthcare-workshop notebook (or the name of the notebook you provided in Cloudformation) by clicking Open JupyterLab

Cleanup

Once you're done with the lab, please make sure you follow the instructions at the end of the notebook to delete all the resources you created in your AWS account. Once you have done that, go to the CloudFormation service in the AWS console and delete the HealthcareWorkshop stack.

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References