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 ...
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 ...
Modern industry is moving beyond simple monitoring. By integrating Predictive AI with a digital twin service, businesses are ...
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 ...
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.
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 ...
Can anyone remember their life before artificial intelligence (AI)? Many struggle with that, but what I do remember is how things worked in the business sector, especially in education.
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 ...
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