Tag: #healthcare

Technology is changing how we practice medicine. Sensors and wearables are getting smaller and cheaper, and algorithms are becoming powerful enough to predict medical outcomes. Yet despite rapid advances, healthcare lags behind other industries in truly putting these technologies to use. A major barrier to entry is the cross-disciplinary approach required to create such tools, […]

Build a machine learning model to predict the likelihood of stroke in a patient with newly diagnosed atrial fibrillation In healthcare, patient outcome prediction is a critical step in improving the effectiveness of care delivery while reducing its overall cost.  Being able to accurately forecast what will happen next to patients, at scale, is key […]

We have an exciting re:Invent planned for the healthcare and life science industries this year. This year’s conference will be our biggest yet, with 60,000+ attendees, more than 2,000 technical sessions and dedicated tracks for both healthcare and life sciences. Below is some key information to help industry attendees make the most of re:Invent 2019. […]

WIRED just published a story about how leading healthcare organizations like Cerner, Aidoc, and Arterys are using AWS machine learning to provide faster, more accurate diagnosis, improve clinical workflows, and streamline patient care.  With Amazon SageMaker at the core, these organizations are leveraging a wide variety of AWS Cloud services, which allows them to focus […]

Healthcare interoperability is a major initiative across all stakeholders of the healthcare ecosystem, and the Fast Healthcare Interoperability Resources (FHIR) standard has opened the doors for a modern approach to sharing such information. I have frequently heard from healthcare customers that they want to make a difference in patients’ lives by sharing the most relevant […]

In today’s digitally transformed world, it’s more important than ever to have a deeper understanding of your patient’s attitude and behavior, and to engage them with personalized content through the channels that they prefer in order to provide efficient and personalized patient care. In addition, with today’s value-based healthcare, the patient is the center of […]

Biomedical researchers require access to accurate, detailed data. The MIT Laboratory of Computational Physiology (LCP) MIMIC-III dataset is a popular resource that captures a variety of measures longitudinally over time, across many patients, and can drive analytics and machine learning toward research discovery and improved clinical decision-making. Recently, the MIT Laboratory of Computational Physiology (LCP) […]

Life science industries are transitioning from wet lab environments to digital labs. Digital labs decrease the “time to science” and de-risk R&D portfolios. Customers see computational methods as a way to increase the performance, throughput, and effectiveness of laboratory operations. This presents opportunities around long-standing challenges with experiment reproducibility and the ability to address lab […]