Use the vitals package with ellmer to evaluate and compare the accuracy of LLMs, including writing evals to test local models.

BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, ...

Abstract: OBJECTIVE: To predict the risk of falls in older Chinese adults, and to develop a machine learning model for assessing fall risk based on longitudinal study data, which can help identify ...

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

The poster highlights an NSF award shared by Nelson and Casey Kennington, Boise State University Foundation Board Endowed Professor of Computer Science, and showcases their progress in expanding ...

Abstract: Data stream learning is an emerging machine learning paradigm designed for environments where data arrive continuously and must be processed in real time. Unlike traditional batch learning, ...

Early identification and prediction of persistent SA-AKI are crucial. Objective: The aim of this study was to develop and validate an interpretable machine learning (ML) model that predicts persistent ...

Background: This study seeks to develop and validate a machine learning (ML) model for predicting atrial fibrillation (AF) recurrence at 12 months following radiofrequency catheter ablation (RFCA).

Data Science Program, University of Delaware, Newark, Delaware 19716, United States Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States ...