Sentence examples for medical analytics from inspiring English sources

Benson S. Hsu, MD, is Chief Medical Analytics Officer, Enterprise Data and Analytics, at Sanford Health.

They also built up a medical analytics software company, which tracks procedures and patient results for payers, enabling them to manage outcomes instead of just costs.

Over the previous five years as founder and CEO of D2Hawkeye, a Waltham, Massachusetts-based healthcare analytics company, Kryder had grown the firm from a six-person start-up into a leading developer and provider of medical analytics in the U.S.

Thus, the medical analytics demand to develop automatic decision systems by utilising computational intelligence for fast, accurate and efficient diagnosis, prognosis, and treatment processes.

On top of that, the company recently hired a new CEO, Mike Coyne, who joined the company from Verisk Health in August, a medical analytics company that he help build from a few million to over $200 million in annual revenue.

The goal of this position paper is to discuss and explore innovative and large-scale data science techniques in medical image analytics, which will benefit clinical decision-making and facilitate efficient medical data management.

IBM's Watson is one of the world's leading platforms for the application of artificial intelligence to knowledge organization and retrieval and the IBM Research Almaden group which is participating in the Center has dedicated expertise in nanomedicine and medical image analytics.

The goal of medical image analytics is to improve the interpretability of depicted contents [ 8].

He is the principal figure responsible for the emergence of Morphometrics over the last quarter-century as an interdisciplinary method combining medical imaging, analytic geometry, and multivariate statistics in novel tools for the analysis of biological form and its variation.

It uses productive loops of experiments, medical, data, data analytics, and simulation on all levels that will eventually bridge the scales.

Each data set is relevant for one or more set of stakeholders, from general managers to coaches to medical personnel to analytics staffs.