MOTIVATION The performance of classifiers is often assessed using Receiver Operating Characteristic ROC [or (AC) accumulation curve or enrichment curve] curves and the corresponding areas under the curves (AUCs). However, in many fundamental problems ranging from information retrieval to drug discovery, only the very top of the ranked list of predictions is of any interest and ROCs and AUCs are...

To date, no studies have attempted to utilize data from a combination of vital signs, heart rate variability and complexity (HRV, HRC), as well as machine learning (ML), for identifying the need for lifesaving interventions (LSIs) in trauma patients. The objectives of this study were to examine the utility of the above for identifying LSI needs and compare different LSI-associated models, with ...

Receiver operating characteristic (ROC) curves are useful statistical tools for medical diagnostic testing. It has been proved its capability to assess diagnostic marker’s ability to distinguish between healthy and diseased subjects and to compare different diagnostic markers. In this paper we introduce non parametric ROC summary statistics to assess a ROC curve across the entire range of FPFs ...