The CT-based whole-lung radiomic nomogram accurately identifies AECOPD and offers a robust tool for clinical diagnosis and treatment planning.

A machine learning model using basic clinical data can predict PH risk, identifying key predictors like low hemoglobin and elevated NT-proBNP. Researchers have developed a machine learning model that ...

In an era where insurance fraud drains billions from the global economy annually, a groundbreaking study by researchers ...

A new clinical model accurately predicts acne recurrence, highlighting lifestyle and psychological risk factors that can ...

Recent research has shown that genetic risk factors in systemic lupus erythematosus (SLE) can influence the emergence of ...

Researchers identified that newly derived risk scores can safely predict the risk of myocardial infarction (MI) and major ...

Conclusions: There is a potential mismatch between what clinicians identify as important in determining palliative care need and final eligibility determinations. Patients with dementia were less ...

An analysis of 5 machine-learning algorithms identified predictors for moderate-to-severe cancer-related fatigue in patients with CRC undergoing chemotherapy.

Large-scale analyses connect genetic risk factors in lupus with ACR-82 clinical manifestations, suggesting genetic risk ...

The research aim is to develop an intelligent agent for cybersecurity systems capable of detecting abnormal user behavior ...