A machine learning-driven framework accurately predicts MPA exposure and supports individualized dosing in childhood-onset LN.
Balancing nitrogen use is critical for maximizing crop yield while minimizing environmental and economic costs. A new approach integrates drone-based ...
Drug discovery is like molecular Tetris. Chemists snap atoms together, adjusting the pieces until everything fits and suddenly, a molecule makes a promising new medicine.
Read more about AI and machine learning drive digital transformation across global mining operations on Devdiscourse ...
A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a ...
Autonomy is no longer science fiction. Advanced flight control systems that augment pilot decisions with artificial ...
Carbon Robotics machines update global system each time they go to the field, boosting data in the machine-learning library ...
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 ...
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Monitoring and treating heart failure (HF) is a challenging condition at any age. Several models, such as Atrial fibrillation, Hemoglobin, Elderly, Abnormal renal parameters, Diabetes mellitus (AHEAD) ...
Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced ...
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