© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Thursday, September 17, 2020

AI and ML WeekExtract insights from unstructured medical data

Gustav Hoyer

Sr. Solutions Architect Manager

Worldwide Public Sector, AWS

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Healthcare considerations

• Centrality patient – clinician

• Health information privacy

• Technology-driven burnout

• Increasing patient consumer expectations

• Proliferation of new data-heavy clinical approaches

• Wet-lab research is increasingly augmented by computational power

© 2020, Amazon Web Services, Inc. or its Affiliates.

Use cases for machine learning in healthcare

Care quality improvement

Operational efficiency

Revenue cycle management

Protected Health Information (PHI) compliance

Increase customer engagement

Patient and

population health

analytics

Fraud

detection

Document / content

automation

© 2020, Amazon Web Services, Inc. or its Affiliates.

The AWS Machine Learning stack

Broadest and most complete set of machine learning (ML) capabilities

VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS

Ground

Truth

AWS

Marketplace

for ML

Neo Augmented

AIBuilt-in

algorithmsNotebooks Experiments Processing

Model

training &

tuning

Debugger AutopilotModel

hostingModel Monitor

Deep Learning

AMIs & Containers

GPUs &

CPUs

Elastic

InferenceInferentia FPGA

AmazonRekognition

AmazonPolly

AmazonTranscribe

+Medical

AmazonComprehend

+Medical

AmazonTranslate

AmazonLex

AmazonPersonalize

AmazonForecast

AmazonFraud Detector

AmazonCodeGuru

AI SERVICES

ML SERVICES

ML FRAMEWORKS & INFRASTRUCTURE

AmazonTextract

AmazonKendra

Contact Lens

For Amazon Connect

SageMaker Studio IDE

Amazon SageMaker

DeepGraphLibrary

© 2020, Amazon Web Services, Inc. or its Affiliates.

Amazon Transcribe Medical

Transcribe and analyze

contact center calls and

help lines at payer and

provider locations

Provider and payer contact centers

Transcribe drug-safety

center calls reporting

medicines and

adverse effects

Drug safety (Pharmacovigilance)

Generate subtitles

for telemedicine

conversations between

clinician and patient

Telemedicine

Enhance clinician

efficiency for medical

note taking and

data entry

Clinical documentation

5

© 2020, Amazon Web Services, Inc. or its Affiliates.

”Patient is 37 year old female

with…”

3) Get a stream of text

2) Pass an audio stream

Microphone-enabled

clientAmazon Transcribe Medical

1) Call the API

Clinical workflow using Amazon Transcribe Medical

© 2020, Amazon Web Services, Inc. or its Affiliates.

Understand clinical data with Amazon Comprehend Medical

Entities

• Medication

• Medical condition

• Test, treatments, and procedures

• Anatomy

• PHI

Relationship extraction

• Medication and dosage

• Test and result

• Many more

Entity traits

• Negation

• Temporality

• Diagnosis, sign, or

symptom

Protected Health Information Identification

(PHId API)*

Distill a complex process into a simple API call

Medical Named Entity and

Relationship Extraction (NERe API)

Service is HIPAA Eligible and “stateless,” no customer data stored

*This API extracts PHI only at lower cost.

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Demo

https://github.com/aws-samples/medical-transcription-analysis

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Customer examples

© 2020, Amazon Web Services, Inc. or its Affiliates.

Identifying critical medical forms

Beth Israel Deaconess Medical Center uses Amazon SageMaker to identify completed patient consent forms before a surgery, lowering the number of delayed or cancelled procedures.

Use case: Driving operational efficiency

© 2020, Amazon Web Services, Inc. or its Affiliates.

Processing healthcare claimsfaster and more accurately

Change Healthcare often receives claim forms that have unreadable labels. Fixing those forms manually adds time and cost. Change Healthcare now uses Amazon SageMaker Debugger to superimpose labels from readable forms, improving the accuracy of the model and enabling more resilient model training in real-time.

Use case: Faster claims processing

© 2020, Amazon Web Services, Inc. or its Affiliates.

Medically accurate text transcripts from calls

Amgen is looking to Amazon Transcribe Medical to accurately review recorded calls from patients or healthcare providers in order to identify any reported potential side effects associated with medicinal products to quickly detect, collect, assess, report, and monitor adverse effects to the benefit of patients globally.

© 2020, Amazon Web Services, Inc. or its Affiliates.

Use case: Medical Imaging

Machine learning can assist radiologists by

making them more efficient

Image Classification: Classify images into

categories. Example: Malignant or benign,

fracture or no fracture.

Image Segmentation: Highlight an area of

interest in an image. Example: Where is the

tumor?

The gating factor for such models: Availability

of high-quality labeled datasets.

© 2020, Amazon Web Services, Inc. or its Affiliates.

• Deep learning algorithms assess the

malignancy risk of pulmonary

nodules based on factors such as

nodule size, shape, density, volume,

as well as patient demographics.

• Use AWS Deep Learning AMI and the

TensorFlow machine learning

framework to train computer vision

algorithms for CT scans.

Deep learning for pulmonary nodules in lung cancer

© 2020, Amazon Web Services, Inc. or its Affiliates.

How to get started?

• AWS for healthcare providers - https://aws.amazon.com/health/providers-and-insurers/

• Amazon Comprehend Medical - https://aws.amazon.com/comprehend/medical/

• Amazon Transcribe Medical - https://aws.amazon.com/transcribe/medical/

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Thank You!