The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
AHA/ASA Journals

Criteria to Assess the Predictive and Clinical Utility of Novel Models, Biomarkers, and Tools for Risk of Cardiovascular Disease: A Scientific Statement From the American Heart ...

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 ...
CIO

Predicting the future is easy — deciding what to do is the hard part

AI agents help businesses stop guessing — linking predictions to actions so teams can move from “what might happen” to ...
IEEE

Nonlinear Optimal Midcourse Guidance Using Sequential Pseudospectral Model Predictive Control

Abstract: A sequential pseudospectral model predictive control (SPMPC) method is proposed for nonlinear optimal midcourse guidance with a general performance index. First, the optimal midcourse ...
IEEE

Federated Discrete Prompt Tuning for Language Models Using Synthetic Examples

Abstract: Effective prompt tuning is critical for using generative AI models, such as large language models (LLMs) and small language models (SLMs), for domain-specific tasks. However, optimizing ...