This document provides an overview of Amazon Comprehend Medical and Amazon SageMaker and how they can be used together for mortality risk prediction using clinical notes. It describes Amazon Comprehend Medical's capabilities for medical entity and relationship extraction. It then outlines how SageMaker can be used to build and deploy a deep learning model using both structured data and clinical note embeddings generated by Comprehend Medical to predict patient mortality risk. The workflow involves text processing of notes, model training, and offline evaluation.

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Ujjwal Ratan
Principal AI/ML SA, GLS
AWS
L F S 3 0 3
Mengqi Jin
Machine Learning Scientist
AWS
Bryan Coon
Sr. Manager, Solutions
Architecture, GLS
AWS
Natural Language Processing and Mortality Risk Prediction Using
Amazon Comprehend Medical & Amazon SageMaker

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Agenda
What is Amazon Comprehend Medical?
What is Amazon SageMaker?
Workshop use case
Workshop architecture
Hands on

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Amazon Comprehend Medical
A new HIPAA-eligible service that uses machine learning to extract medical information with high
accuracy, reducing the cost, time and effort of processing large amounts of unstructured medical text

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Amazon Comprehend Medical
Entities
• Medication
• Medical condition
• Test, Treatments and Procedures
• Anatomy
• Protected Health Information (PHI)
Relationship Extraction
• Medication and dosage
• Test and result
• Many more
Entity Traits
• Negation
• 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)

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Protected Health Information (PHI)
Mr. Doe : Name
63 : Age
Comprehend Medical - NERe API
Mr. Doe is a 63-year-old gentleman with
coronary artery disease and hypertension.
CURRENT MEDICATIONS: taking a dose of
LIPITOR 20 mg once daily.
aws comprehend-medical detect-entities --region
us-east-1 --text “<Insert Text Here>”
Named Entities

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Comprehend Medical – NERe API
CURRENT MEDICATIONS: taking a dose of
LIPITOR 20 mg once daily
aws comprehend-medical detect-entities --region
us-east-1 --text “<Insert Text Here>”
Relationship Extraction

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Amazon Comprehend – NERe API
What are Entity Traits?
(1) Negation
(2) Diagnosis, sign or symptom
Rash
Discontinue Abraxane, patient denies
taking Tylenol 325 mg and is not taking
calcium carbonate, patient also stopped
taking cholecalciferol 1,000 units PO
Entity Traits

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Amazon Comprehend – PHId API
Mr. Doe is a 63-year-old gentleman with
coronary artery disease and hypertension. He
currently lives in Anytown and works as a
teacher. He currently lives in Anytown and
works as a teacher. His PCP, Dr. Stiles, works at
the University of Anystate.
aws comprehend-medical detect-phi --region us-
east-1 --text “<Insert Text Here>”
In addition to extracting PHI, the
PHId API identifies relevant patient
identifiers described in HIPAA Safe
Harbor method of de-identification
Personal Health Information

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12. “For cancer patients and the researchers dedicated to curing them,
time is the limiting resource. The process of developing clinical
trials and connecting them with the right patients requires
research teams to sift through and label mountains of clinical
record data. The Amazon Comprehend Medical service reduces
this time burden from hours to seconds. This is a vital step toward
getting researchers rapid access to the information they need
when they need it so they can advance lifesaving therapies for
patients.”
Matthew Trunnell,
Chief Information Officer,
Fred Hutchinson Cancer Research Center

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A fully managed service that enables data scientists and developers to quickly and easily
build machine-learning based models into production smart applications.
Amazon SageMaker

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End-to-End
Machine Learning
Platform
Zero setup Flexible Model
Training
Pay by the second
Amazon SageMaker
Build, train, and deploy machine learning models at scale

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One-click training
for ML, DL, and
custom algorithms
Easier training with
hyperparameter
optimization
Highly optimized
machine learning
algorithms
Deployment
without
engineering effort
Fully managed
hosting at scale
BuildPre-built
notebook
instances
Deploy
Train
Amazon SageMaker

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Business problem
Predict an ICU patient’s risk of mortality
Why is this important?

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This workshop has four sections

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Go to the following link to begin setup
https://bit.ly/2KcWj6R

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Architecture diagram

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Mortality risk prediction
Amazon
ECR
Amazon
SageMaker
Amazon
S3
Data
Algorithm
Training Inference
Model Artifacts Prediction Outputs
Amazon
S3
Doc2Vec
Note Embeddings
1 32
Notebooks

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Mortality risk prediction
0 hr 12 hr 24 hr 36 hr 48 hr … … … … Discharge
Observation
Window
Prediction
Window
Death? DischargeICU Admission Prediction
• Goal: Early detection of at-risk
patients in ICU
• Using first 48 hrs data of
admission to predict in-hospital
mortality
• Numerical Variables:
• Vital Signs
• Labs
• Bedside Measurements
• Unstructured Notes:
• Nursing Notes
• Physician Notes
• ECG Notes
• Other 12 types of notes
Mortality is one of the metrics
used by CMS to evaluate quality
of care and hospital ranking

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Risk modeling with text information
Multimodal
Embedding
Raw Notes
Model
Artifacts
Doc2VecC
Medication
Diagnosis
Treatment/
Test/
Procedure
Recurrent
Neural Network
Amazon
Comprehend
Medical
Extracted Medical Entities
Concatenated
entities
Step 1: Text Processing
Step 2: Training Step 3: Offline Testing
Evaluation
Metrics

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Risk modeling with text information
Step 1: Generate Document Embeddings
• Use Doc2VecC to generate embeddings for 4000 medical notes
• Save trained embedding dictionary on Amazon Simple Storage Service (Amazon S3)
Input
Raw notes saved in csv files
Output
NumPy dictionary of document embeddings keyed by Note ID

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Risk modeling with text information
Step 2: Amazon SageMaker Training Job
• Run the algorithm over the training data
• Save the output model and select the best one to save it on Amazon S3
Input
Time series data
Pre-trained document embeddings
Output
Model artifacts saved in model.tar.gz file

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Risk modeling with text information
Step 3: Amazon SageMaker BatchTransform Job
• Making inference on test data set
• Evaluate model performance
Input
Selected best model saved in model.tar.gz file
Time series data
Pre-trained document embeddings
Output
Prediction for each time series record
Evaluation metrics

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Related breakouts
Thursday November 29th
Introducing Amazon Comprehend Medical
12:15 – 1:15 PM | Aria West Level 3 Juniper 4

34. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Ujjwal Ratan
ujjwalr@amazon.com
Bryan Coon
bccn@amazon.com
Mengqi Jin
maggjin@amazon.com

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