Over the past decade, we’ve witnessed a digital transformation in health with organizations capturing huge volumes of health data every day. Much of this health data has been collected as unstructured data which makes it difficult for healthcare and life science organizations to use it to derive insights and deliver better care to patients.

In this hackathon, participants will use Amazon Comprehend Medical, to structure medical record notes, together with other purpose-built Health AI services and broader AWS cloud services, such as SageMaker, to build, train and deploy machine learning models to make sense of health data to address key challenges for health organizations like health equity, bias in healthcare, predicting healthcare outcomes in populations, and improving care quality. You are welcome to use your own datasets and we will also provide datasets for you to consider (see details below). If you bring your own datasets, ensure they don’t contain Protected Health Information (PHI) and are not restricted for use in a public setting (e.g. this hackathon.) Through this hackathon, we hope to stimulate new ways to use machine learning and natural language processing to unlock new insights from health data to improve care and health equity. 

During the course of the hackathon, participants will explore Amazon Comprehend Medical with other purpose-built Health AI services and broader AWS cloud services. Participants will brainstorm ideas, develop a prototype, and present their results to the judges at the end of the competition.

$48,000 in prizes

• Potential Impact
  Includes the potential social impact and the extent to which the solution can be widely useful to healthcare and life science organizations, easy to use, accessible, etc.
• Quality of the Idea
  Includes creativity, originality of the project, such as solving a challenge in a unique way.
• Implementation
  Includes how well the idea was prototyped by the developers using Amazon Comprehend Medical and other AWS services.