In response to the COVID-19 crisis, Artificial Intelligence (AI) has been applied to a range of applications in healthcare and public health such as case identification or monitoring of the population. The urgency of the situation should not be to th...
Colon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms ...
Since various dance teaching systems have attracted much attention with the development of Artificial Intelligence (AI) technology, this paper improves the recognition performance of Latin dance teaching systems by optimizing the action recognition m...
BACKGROUND: Artificial intelligence (AI) has the potential to enhance diagnostic accuracy and improve treatment outcomes. However, AI integration into clinical workflows and patient perspectives remain unclear.
BACKGROUND: Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur.
Ischemic stroke (IS) is a complex neurological disease that can lead to severe disability or even death. Understanding the molecular mechanisms involved in the occurrence and progression of IS is of great significance for developing effective treatme...
Critical reviews in clinical laboratory sciences
Nov 1, 2023
Serum protein electrophoresis (SPEP) is a valuable laboratory test that separates proteins from the blood based on their electrical charge and size. The test can detect and analyze various protein abnormalities, and the interpretation of graphic SPEP...
OBJECTIVE: Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic rev...
Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify impacts associated with chemical emissions and chemicals in products. However, the systematic application of ML-based approaches to fill chemical data gaps is ...
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