Multicenter critical care research often relies on sharing sensitive patient data across sites, requiring complex data use agreements (DUAs) and yielding redundant work to account for diverse data ...

Overview Curated list highlights seven impactful books covering fundamentals, tools, machine learning, visualization, and industry.Guides beginners and professi ...

The Department for Work and Pensions (DWP) has published a “data strategy” document that sets out what it believes it will take to become an organisation transformed by data usage by 2030. This ...

We created a hybrid rules–based and natural language processing (NLP)–based pipeline that automatically screens patients using structured and unstructured electronic health record data standardized to ...

The Common Data Set (CDS) initiative is a collaborative effort among data providers in the higher education community and publishers as represented by the College Board, Peterson’s, and U.S. News & ...

Dynamic predictive modeling using electronic health record data has gained significant attention in recent years. The reliability and trustworthiness of such models depend heavily on the quality of ...

Chances are, you’ve seen clicks to your website from organic search results decline since about May 2024—when AI Overviews launched. Large language model optimization (LLMO), a set of tactics for ...

The Common Data Set can help prospective students know how much aid they could get to pay for college. Why don’t all schools provide it? By Ron Lieber A similar version of this column was published ...

Abstract: Data-driven Quality of Experience (QoE) modeling using Machine Learning (ML) is a key enabler for future communication networks as it allows accelerated and unbiased QoE modeling while ...

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...