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 ...

A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results ...

feature CERN is nothing like today's agentic AI jockeys, who mostly rely on pre-set weights and generic TPUs and GPUs to ...

Long-term lithium therapy remains the most effective maintenance treatment for bipolar disorder, yet it poses a significant ...

Machine learning algorithms help computers analyse large datasets and make accurate predictions automatically.Classic models ...

A new study explores how artificial intelligence models can support clinical decision-making for sepsis management. Their research, titled “Responsible AI for Sepsis Prediction: Bridging the Gap ...

New framework combines Copilot, Claude, ChatGPT, Gemini, Perplexity, and multi-model LLMs to transform Power BI and ...

Abstract: Machine learning has been successfully applied to drug combination prediction in recent years. However, in some situations, the class imbalance problem still shows highly negative impacts on ...

A decision tree regression system incorporates a set of if-then rules to predict a single numeric value. Decision tree regression is rarely used by itself because it overfits the training data, and so ...

Kidney cancer is a highly heterogeneous oncologic disease with historically poor prognosis. Precise assessment of the risk of distal metastasis can facilitate risk stratification and improve prognosis ...

Acute Type A aortic dissection (ATAAD) is characterized by acute onset and rapid progression, with aortic rupture due to dissection extension being the primary lethal mechanism. Timely identification ...