AWS Machine Learning Blog

Category: Healthcare

Whitepaper: Machine Learning Best Practices in Healthcare and Life Sciences

For customers looking to implement a GxP-compliant environment on AWS for artificial intelligence (AI) and machine learning (ML) systems, we have released a new whitepaper: Machine Learning Best Practices in Healthcare and Life Sciences. This whitepaper provides an overview of security and good ML compliance practices and guidance on building GxP-regulated AI/ML systems using AWS […]

Read More
SageMaker Data Wrangler Risk Modeling

Build a mental health machine learning risk model using Amazon SageMaker Data Wrangler

This post is co-written by Shibangi Saha, Data Scientist, and Graciela Kravtzov, Co-Founder and CTO, of Equilibrium Point. Many individuals are experiencing new symptoms of mental illness, such as stress, anxiety, depression, substance use, and post-traumatic stress disorder (PTSD). According to Kaiser Family Foundation, about half of adults (47%) nationwide have reported negative mental health […]

Read More

How Cortica used Amazon HealthLake to get deeper insights to improve patient care

This is a guest post by Ernesto DiMarino, who is Head of Enterprise Applications and Data at Cortica. Cortica is on a mission to revolutionize healthcare for children with autism and other neurodevelopmental differences. Cortica was founded to fix the fragmented journey families typically navigate while seeking diagnoses and therapies for their children. To bring […]

Read More

Build patient outcome prediction applications using Amazon HealthLake and Amazon SageMaker

Healthcare data can be challenging to work with and AWS customers have been looking for solutions to solve certain business challenges with the help of data and machine learning (ML) techniques. Some of the data is structured, such as birthday, gender, and marital status, but most of the data is unstructured, such as diagnosis codes […]

Read More

Annotate DICOM images and build an ML model using the MONAI framework on Amazon SageMaker

DICOM (Digital Imaging and Communications in Medicine) is an image format that contains visualizations of X-Rays and MRIs as well as any associated metadata. DICOM is the standard for medical professionals and healthcare researchers for visualizing and interpreting X-Rays and MRIs. The purpose of this post is to solve two problems: Visualize and label DICOM […]

Read More
The following diagram illustrates the architecture for our experiments.

Building predictive disease models using Amazon SageMaker with Amazon HealthLake normalized data

In this post, we walk you through the steps to build machine learning (ML) models in Amazon SageMaker with data stored in Amazon HealthLake using two example predictive disease models we trained on sample data using the MIMIC-III dataset. This dataset was developed by the MIT lab for Computational Physiology and consists of de-identified healthcare […]

Read More

Population health applications with Amazon HealthLake – Part 1: Analytics and monitoring using Amazon QuickSight

Healthcare has recently been transformed by two remarkable innovations: Medical Interoperability and machine learning (ML). Medical Interoperability refers to the ability to share healthcare information across multiple systems. To take advantage of these transformations, we launched a new HIPAA-eligible healthcare service, Amazon HealthLake, now in preview at re:Invent 2020. In the re:Invent announcement, we talk […]

Read More

Making sense of your health data with Amazon HealthLake

We’re excited to announce Amazon HealthLake, a new HIPAA-eligible service for healthcare providers, health insurance companies, and pharmaceutical companies to securely store, transform, query, analyze, and share health data in the cloud, at petabyte scale. HealthLake uses machine learning (ML) models trained to automatically understand and extract meaningful medical data from raw, disparate data, such […]

Read More