Researchers developed and internally validated an interpretable machine learning model predicting incident cardiovascular disease among adult cancer survivors following systemic ...
Machine learning models utilizing routinely collected clinical biomarkers accurately predicted the short-term risk of impulsivity in hospitalized schizophrenia patients.
A machine learning study of 906 school children in Xi'an reveals that subjective street perceptions and strict environmental thresholds strongly ...
A comprehensive panel study across 65 developing economies reveals that renewable energy consumption significantly attenuates the emissions-increasing pressures of regional ...
An enhanced Random Forest model reveals that rising precipitation has overtaken solar radiation as the primary driver of grassland carbon ...
A multicenter retrospective study demonstrates that a longitudinal MRI habitat model reliably predicts pathological complete response to neoadjuvant chemoimmunotherapy in ...
Researchers have developed treatment-specific machine learning models using routine non-contrast CT scans to predict short-term stone-free outcomes after shock wave ...
In a retrospective study of 2,407 surgical cases from VitalDB, a multimodal data-fusion model improved five-minute-ahead depth-of-anesthesia prediction during the ...
Researchers designed an integrated machine learning framework combining recurrent neural networks, gradient boosting, and asynchronous optimization to predict multiday surface ...
An Extreme Gradient Boosting model using routinely collected bedside data accurately predicted delirium in non-mechanically ventilated sepsis patients during internal ...
Embark on a thrilling journey of discovery with Scienmag.com—your ultimate source for cutting-edge breakthroughs. Immerse yourself in a world where curiosity knows no limits and tomorrow’s possibilities become today’s reality!
