Machine learning-based ICU mortality prediction across hematologic malignancy subtypes: A comparative analysis using MIMIC-IV. Model performance and top predictive features.

A major clinical trial involving 50 hospital intensive care units (ICUs) throughout New Zealand and Australia will test if artificial intelligence (AI) can help doctors save more patients’ lives who ...

Brain cancer is one of the deadliest diseases — and early detection is crucial for better outcomes. There are obvious symptoms like sudden, severe headaches and dizziness, while subtle signs such as ...

Machine learning often feels difficult at the beginning, especially when everything stays theoretical. That changes once you start working on real projects and see how models are actually used. The ...

A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously ...

Gastric cancer remains a major global health burden, and its pronounced molecular heterogeneity hampers progress in precise subtyping and targeted therapy. Invasion-related programs are considered ...

Abstract: This article examines recent studies on the use of deep learning (DL) and machine learning (ML) in the diagnosis of brain tumors. It demonstrates how these technologies transform detection ...

A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results predicted a patient's risk of hepatocellular carcinoma (HCC), the most common ...

The field of neuroimaging has undergone profound transformation in recent years, driven primarily by rapid advances in machine learning (ML), and especially deep learning (DL), techniques. These ...

Most ML projects fail to reach production. Five recurring pitfalls drive failures in ML projects: choosing the wrong problem, data quality/labeling issues, the model-to-product gap, offline-online ...