A machine learning model may help predict mortality for hospitalized patients with cirrhosis at the time of admission, ...

For inpatients with cirrhosis, a machine learning (ML) model using random forest (RF) analysis is superior for prediction of ...

The disease is known for its strong association with climatic variables, especially excessive rainfall, high humidity, and ...

Sai Krishna's work in text mining has earned him well-deserved recognition within his organization. His development of an RShiny-based machine learning workbench was a game-changer, leading to ...

In today's AI-driven world, AI tools for data analysis have supercharged the ability to extract meaningful insights from vast ...

Fake news articles contained significantly higher levels of negative sentiment, including emotions such as fear, anger, sadness, and disgust. In contrast, real news articles were more likely to elicit ...

Artificial intelligence models used to detect depression on social media are often biased and methodologically flawed, according to a study led by Northeastern University computer science graduates.

Predicting who might die while in the hospital is one of the hardest challenges for doctors caring for people with serious liver disease.

Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

Student retention and success remain persistent global challenges in higher education, with direct implications for institutional quality, resource ...