Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...
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 ...
AI agents help businesses stop guessing — linking predictions to actions so teams can move from “what might happen” to ...
Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...
Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...
Interpretability of Support Vector Machine (SVM) or Neural Networks (NN) models, examples of black-box models, is a field of study that has recently gained attention, especially for the significant ...
Abstract: This article proposes a deep Koopman-based identification method for nonlinear dynamical systems with modeling residuals learned recursively by incremental Gaussian process regression (IGPR) ...
Choosing the right curve fit model is essential for revealing key data features, such as rate of change, asymptotes, and EC 50 /IC 50 values. The best model is the one that most faithfully reflects ...
Artificial intelligence models can secretly transmit dangerous inclinations to one another like a contagion, a recent study found. Experiments showed that an AI model that’s training other models can ...
Singapore-based AI startup Sapient Intelligence has developed a new AI architecture that can match, and in some cases vastly outperform, large language models (LLMs) on complex reasoning tasks, all ...
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