A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.
Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
Access to credit is identified as a major impediment to private sector investment in African agriculture. "The biggest risk is not taking any risk ... the only strategy that is guaranteed to fail is ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
Explore how proteomics in biomarker discovery accelerates diagnostic assay development and improves clinical validation.
Background Remission and low-disease activity are recommended targets in systemic lupus erythematosus (SLE), yet many ...
Objectives To evaluate whether type 2 diabetes mellitus (T2DM) presence and severity are associated with differences in global and domain-specific cognitive function among US adults, using ...
Machine learning can predict many things, but can it predict who will develop schizophrenia years before the average diagnosis time?
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