Tag: HAQM Sagemaker

Healthcare organizations across the nation are working to address clinician burnout and are more constrained than ever. That’s why Netsmart, an industry leader in electronic health records for human services and post-acute care, and HAQM Web Services joined forces to advance artificial intelligence for community-based care providers, through the development of an AI Data Lab. By combining the scale and power of AWS with the Netsmart CareFabric® platform, Netsmart is driving intelligent automation, generative AI, predictive analytics, natural language processing and risk models to improve care delivery and outcomes for community-based care providers.

The education sector has gone through a transformative technological change in the last few years. First, the pandemic created a rise in e-learning solutions, as teachers and students adopted digital platforms for communicating, teaching and learning, and managing academic information. These solutions show that students all over the world can get quality education over the […]

This walkthrough explores how to leverage HAQM Forecast to derive valuable business insights in satellite communications use-cases. Operations teams can quickly see accurate satellite capacity forecasts on a per beam basis. The benefits include lower cost via provisioning just the right amount of bandwidth, and a more streamlined customer experience since users will be less impacted by weather or surge events.

In this post, learn use cases for digital twins, plus how to create an open-source digital twin sample front-end application built with AWS Amplify, HAQM Cognito, and AWS IoT Core that you can use as a starting point for building efficient, scalable, and secure digital twin solutions.

Artificial intelligence (AI) and machine learning (ML) technologies are transforming many industries. But although public sector organizations are realizing the benefits of these technologies, there are many remaining challenges, including biases and a lack of transparency, that limit the wider adoption to unlock the full potential of AI and ML. In this post, learn a high-level framework for how AWS can help you address these challenges and provide better outcomes for constituents.

Geospatial data, including many climate and weather datasets, are often released by government and nonprofit organizations in compressed file formats such as the Network Common Data Form (NetCDF) or GRIdded Binary (GRIB). As the complexity and size of geospatial datasets continue to grow, it is more time- and cost-efficient to leave the files in one place, virtually query the data, and download only the subset that is needed locally. Unlike legacy file formats, the cloud-native Zarr format is designed for virtual and efficient access to compressed chunks of data saved in a central location such as HAQM S3. In this walkthrough, learn how to convert NetCDF datasets to Zarr using an HAQM SageMaker notebook and an AWS Fargate cluster and query the resulting Zarr store, reducing the time required for time series queries from minutes to seconds.

In this guest post, Ajay K. Gupta, co-founder and chief executive officer (CEO) of HSR.health, explains how healthcare technology (HealthTech) nonprofit HSR.health uses geospatial artificial intelligence and AWS to develop solutions that support improvements in healthcare and health equity around the world.

Pathology is currently undergoing a transformation. While microscopes still dominate many workflows, digital pathology combined with artificial intelligence (AI) is disrupting the space. AI tools can complement expert assessment with quantitative measurements to enable data-driven medicine. Ultivue is a healthcare technology (HealthTech) company that provides high-quality multiplex immunofluorescence assays and large-scale, AI-based computational pathology—built on AWS.

Sudden cardiac arrest (SCA) is the number one cause of death for student athletes and the leading cause of death on school campuses. The nonprofit Who We Play For (WWPF) advocates for SCA prevention through advocacy, automated external defibrillator (AED) placement, cardiopulmonary resuscitation (CPR) training, and heart screenings, which include low-cost electrocardiogram (ECG) screenings from physicians that are experts in pediatric ECG interpretation. To scale their efforts, WWPF collaborated with AWS to build a ML solution to help extend the chance to get screened for SCA to every young person, potentially saving many lives each year.

Diabetes is a major chronic disease that often results in hospital readmissions due to multiple factors. An estimated $25 billion is spent on preventable hospital readmissions that result from medical errors and complications, poor discharge procedures, and lack of integrated follow-up care. If hospitals can predict diabetic patient readmission, medical practitioners can provide additional and personalized care to their patients to pre-empt this possible readmission, thus possibly saving cost, time, and human life. In this blog post, learn how to use machine learning (ML) from AWS to create a solution that can predict hospital readmission – in this case, of diabetic patients – based on multiple data inputs.