At AWS re:Invent 2020, we previewed Amazon HealthLake, a fully managed, HIPAA-eligible service that allows healthcare and life sciences customers to aggregate their health information from different silos and formats into a structured, centralized AWS data lake, and extract insights from that data with analytics and machine learning (ML). Today, I'm very happy to announce that Amazon HealthLake is generally available to all AWS customers. The ability to store, transform, and analyze health data quickly and at any scale is critical in driving high-quality health decisions. In their daily practice, doctors need a complete chronological view of patient history to identify the best course of action. During an emergency, giving medical teams the right information at the right time can dramatically improve patient outcomes.

Medical data is highly contextual and heavily multi-modal, in which each data silo is treated separately. To bridge different data, a knowledge graph-based approach integrates data across domains and helps represent the complex representation of scientific knowledge more naturally. For example, three components of major electronic health records (EHR) are diagnosis codes, primary notes, and specific medications. Because these are represented in different data silos, secondary use of these documents for accurately identifying patients with a specific observable trait is a crucial challenge. By connecting those different sources, subject matter experts have a richer pool of data to understand how different concepts such as diseases and symptoms interact with one another and help conduct their research.

Calling machine learning "one of the most disruptive technologies we will ever encounter in our generation," Amazon Machine Learning Vice President Swami Sivasubramanian introduced a bevy of new tools Tuesday at AWS re:Invent. There will be nine new Amazon SageMaker capabilities, a HIPAA-eligible service for healthcare and life science organizations called Amazon HealthLake, and general availability for Amazon Neptune ML, he said. Sivasubramanian also announced previews for Amazon Redshift ML and Amazon Lookout for Metrics. "More than 100,000 customers use AWS for machine learning today. These tools are no longer a niche investment. Our customers are applying machine learning to the core of their business. Our customers are innovating in every industry," Sivasubramanian said.

Amazon's vice president of machine learning, Swami Sivasubramanian, Tuesday offered a keynote on machine learning for week two of Amazon's re:Invent conference for Amazon Web Services. A few brilliant strokes of ingenuity, combined with a large dose of capitalism, made the e-retailer into the world's cloud services leader. During the keynote, Sivasubramanian announced the company's middleware platform for machine learning, SageMaker, will be able going forward to automatically break up the parts of a large neural net and distribute those parts across multiple computers. This form of parallel computing, known as model parallelism, is usually something that takes substantial effort. The new capability, he said, was part of a theme of bringing machine learning, even large deep learning forms, to more individuals than the small group of scientists with the skills for developing it.

During its re:Invent 2020 virtual keynote today, Amazon launched Amazon HealthLake, a service that enables health care organizations to store, transform, and analyze up to petabytes of life science data in Amazon Web Services. Amazon says that the HIPAA-eligible HealthLake, which is available in preview starting today, can automatically understand and extract medical information including rules, procedures, and diagnoses in real time. Health care data is often spread across various systems such as electronic medical lab systems, and it's challenging to organize because it's often unstructured. Data in medical records like clinical notes, reports and forms like insurance claims, and image scans needs to be prepped and normalized before analyses can begin. HealthLake aims to address this challenge by enabling customers to apply intelligence to hundreds of thousands of data points across different siloes in dozens of formats.