Tag: Amazon Sagemaker

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.

Cybersecurity analytics is a systematic methodology designed to collect, ingest, process, aggregate, and analyze security events. This methodology empowers organizations to proactively perform security investigations, powered by advanced analytics and machine learning (ML), which help mitigate cyber issues more effectively and efficiently at scale. Learn about the core components of a cybersecurity analytics framework and how organizations can use AWS to design a cybersecurity analytics platform with analytics and ML services.

Nara Space Technology is a South Korea-based startup that builds nano satellite constellations and provides satellite data services to let customers quickly identify and address issues like changing climate conditions and disaster recovery to improve life on Earth. Nara Space provides solutions for nano satellite and small spacecraft system design, integration, development, and testing; enables satellite data analytics based on deep learning; and improves the visual quality of standard satellite imagery with its Super Resolution core technology. To do this, Nara Space uses AWS for secure, flexible, scalable, and cost-efficient cloud solutions.

The browser-based computational notebook tool, Jupyter, provides students and educators with an interactive learning environment to accelerate programming learning. But setting up collaborative Jupyter notebooks at the classroom and institutional level can be time-consuming and costly. Amazon SageMaker Studio Lab is a no-cost service built on Jupyter notebooks that takes care of the configuration and security of setting up multi-user Jupyter notebook environments – so educators can focus on teaching and learners can accelerate their journey in ML.

Artificial intelligence (AI) and machine learning (ML) are critical tools being used in healthcare research, autonomous applications, predictive maintenance, and also a key tool used to advance sustainability solutions. However, to use AI and ML to solve sustainability problems, innovators need specific datasets that are prepared for analysis and training of the models. To help create and accelerate sustainability solutions, the Amazon Sustainability Data Initiative (ASDI) today announced easier identification of sustainability datasets with integration in AWS Data Exchange and the launch of a sustainability hackathon.

To mitigate synthetic fraud, government agencies should consider complementing their rules-based improper payment detection systems with machine learning (ML) techniques. By using ML on a large number of disparate but related data sources, including social media, agencies can formulate a more comprehensive risk score for each individual or transaction to help investigators identify improper payments efficiently. In this blog post, we provide a foundational reference architecture for an ML-powered improper payment detection solution using AWS ML services.

Nursing shortages are not a new phenomenon, but the pandemic has exacerbated the problem. The situation has forced hospitals to think creatively about their staffing models and has also brought clinical teams, operators, and IT departments together in an effort to seek new ways to use technology. One healthcare system is using robotics powered by cloud technology, including artificial intelligence (AI) and machine learning (ML), to help nurses spend more time with patients.

A research team led by Dr. Beatriz Martinez Lopez at UC Davis supports pig farmers with a data analytics platform that aggregates and analyzes animal health data to diagnose animal viruses and diseases. But this platform was primarily designed for analysts and data scientists. To truly transform animal disease management, Martinez-Lopez wants to put this data analytics tool into the hands of farmers around the world. So the research team is using the scalable, cost-effective tools of the AWS Cloud, along with a research grant letter of support from AWS, to make this optimized platform a reality.