Researchers developed and validated a machine-learning algorithm for predicting nutritional risk in patients with nasopharyngeal carcinoma.
Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational ...
A scoping review shows machine learning models may help predict response to biologic and targeted synthetic DMARDs in ...
Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...
Researchers at Thomas Jefferson University have developed an automated machine learning (AutoML) model that can accurately differentiate between two common types of brain tumors using preoperative MRI ...
Software engineers are increasingly seeking structured pathways to transition into machine learning roles as companies expand ...
A hybrid AI–human scoring system delivers expert-level accuracy in ulcerative colitis endoscopic assessment while reducing human review by 81 percent.
Machine learning models delivered the strongest performance across nearly all evaluation metrics. CHAID and CART provided the highest and most stable sensitivity, accuracy and discriminatory power, ...
Overview: Machine learning failures usually start before modeling, with poor data understanding and preparation.Clean data, ...
According to a new study, machine learning can reliably identify patients at high risk of early dysphagia following acute ...
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