Many scientific problems entail labeling data items with one of a given, finite set of classes based on features of the data items. For example, oncologists classify tumors as different known cancer ...
The vehicles on American roads have grown larger — and they are killing thousands more pedestrians, a Times investigation ...
Objective Unlike several other fields of healthcare, little is known about the size of ‘therapist effects’ on patient ...
Objectives To quantify out-of-pocket (OOP) costs and catastrophic health expenditure (CHE) among patients hospitalised with ...
Objectives To assess the outcomes of patients undergoing open abdominal surgery at a National Referral Hospital in Tanzania. Design A prospective, observational, single-arm cohort study. Setting Dar ...
Background Understanding the ‘real-world’ challenges of delivery of pre-exposure prophylaxis (PrEP) among men who have sex ...
Missing a night of sleep leaves a specific chemical signature in saliva that can be reliably detected with a high degree of ...
Abstract: Class imbalance is a persistent challenge in machine learning, particularly in high-stakes applications such as medical diagnostics, bioinformatics, and fraud detection, where the minority ...
Abstract: Medical datasets are usually imbalanced, where negative cases severely outnumber positive cases. Therefore, it is essential to deal with this data skew problem when training machine learning ...
Through trend analyses, this surveillance highlighted both the emergence and decline of AMR across diverse bacterial pathogens, helping inform which antibiotics may remain appropriate as first-line ...
Retailers are implementing AI through software companies like Happy Returns and Narvar to detect return fraud and issue ...
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