Reports demonstrates that whole-brain Single Photon Emission Computed Tomography (SPECT) imaging, combined with advanced ...

Glucose-to-potassium ratio shows a J-shaped link with AKI after traumatic brain injury, with high levels predicting increased ...

An artificial intelligence (AI) machine-learning model has been developed that can predict the risk of early death in trauma ...

Analyzing thousands of proteins from a single drop of blood is no longer science fiction. High-throughput proteomics has transformed biomarker discovery by enabling simultaneous profiling of thousands ...

Objective SLE is a heterogeneous systemic autoimmune disease with diverse clinical manifestations. We aimed to identify ...

Abstract: With the explosive growth of data volume and computing capability, federated learning, which involves constructing global models over multiple data islands, has demonstrated its advantages ...

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 ...

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...

A UK Biobank study of nearly 390,000 people mapped 251 NMR-derived plasma metabolic traits against hundreds of health traits, ...

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 ...

Objectives To examine primary care contacts among individuals with eating disorders (EDs) and assess differences across ...

BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the ...