This study compared 6 algorithmic fairness–improving approaches for low-birth-weight predictive models and found that they improved accuracy but decreased sensitivity for Black populations. Objective: ...

Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies on multivariable models, which integrate ...

Business leaders today are navigating an era of complex uncertainty, where risk moves faster than traditional oversight can keep up. From global supply chain volatility to internal compliance ...

AI has become an increasingly hot technology for the healthcare sector, boosting venture capital investment and spurring interest in tools that could stretch the overburdened provider workforce. But ...

Modern industry is moving beyond simple monitoring. By integrating Predictive AI with a digital twin service, businesses are ...

Patients are less comfortable with predictive models used for health care administration compared with those used in clinical practice, signaling misalignment between patient comfort, policy, and ...

From Reaction to Anticipation: Predictive analytics empower security teams to transition from reactive responses to proactive strategies by leveraging data to anticipate risks before they escalate.

Waiting for alerts is obsolete — predictive engineering lets cloud systems see trouble coming and fix it before users ever ...

Andrew Ferguson, American University Washington College of Law and planning committee member, provided the opening presentation for a session focused on theoretical underpinnings, examples of use, and ...

What’s launching today is Pecan’s “predictive agent,” an autonomous system that can interpret a company’s unique data structure, or “fingerprint,” by breaking down the predictive workflow into ...