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 ...

Background Anti-C1q autoantibodies can disrupt normal complement function, contributing to the formation of pathogenic immune ...

Announcing a new article publication for Zoonoses journal. In northeastern China, tick-borne diseases pose a major public health challenge, which is exacerbated by environmental and anthropogenic ...

Background Stroke after transcatheter aortic valve implantation (TAVI) is an infrequent but serious complication with ...

Add Zee News as a Preferred Source Having overlaid random forests and gradient boosting on a logistic regression meta-model, the group of Ravi could achieve the maximum accuracy rate and obtained 93% ...

A machine learning model using routine lab data at 3 months postdiagnosis accurately predicted mortality or liver transplant risk in autoimmune hepatitis.

VENTURE Study Exploratory Analysis Shows VK2735 Improved Cardiometabolic Parameters After 13 Weeks; Reducing Prediabetes and Metabolic Syndrome SAN DIEGO, Nov. 6, 2025 /PRNewswire/ -- Viking ...

Machine learning models using initial neuropsychological and neuropsychiatric clinical data accurately distinguished AD from bvFTD.