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Thursday, September 17, 2020
AI and ML WeekExtract insights from unstructured medical data
Gustav Hoyer
Sr. Solutions Architect Manager
Worldwide Public Sector, AWS
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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
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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
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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
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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
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”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
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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.
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Demo
https://github.com/aws-samples/medical-transcription-analysis
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Customer examples
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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
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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
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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.
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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.
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• 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
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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/
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Thank You!