Researchers developed an interpretable machine learning model using ICU data that achieved good discrimination in estimating methicillin resistance among patients ...
A study evaluating XAI on live honeypot telemetry found that tree-based models provide more faithful explanations for intrusion detection than ...
A 1-kilometer machine learning reconstruction reveals that Northern Hemisphere permafrost active layer thickness increased by 9.68 centimeters between 2000 and ...
A comparative modeling study of critically ill patients with acute myocardial infarction demonstrated that early six-hour landmark mortality prediction is ...
Supervised machine learning algorithms successfully predicted synergistic combinations between antimicrobial peptides and conventional antimicrobial agents using fractional inhibitory concentration data.
A time-split validation revealed that a MALDI-TOF machine-learning classifier for carbapenem-resistant Klebsiella pneumoniae had limited temporal sensitivity and lost discriminative ...
Researchers developed an explainable machine learning framework using routine clinical and biochemical data to accurately classify hepatocellular carcinoma in Vietnamese ...
A new machine learning framework using reduced-channel EEG and variational mode decomposition achieves 92.16% accuracy in classifying nine types of ...
A comparative study demonstrates that optimized gradient boosting machine learning models reliably predict customer churn on digital health platforms by ...
A nationwide evaluation in Bangladesh found that bottom-up machine learning models achieved the lowest observed point error for one-week-ahead dengue ...
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