AWS Machine Learning Blog
Category: Amazon HealthLake
Build a cognitive search and a health knowledge graph using AWS AI services
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 […]
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 […]
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 […]
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 […]