The development of next-generation metallic materials is entering a transformative era driven by data-driven methodologies. Traditional trial-and-error ...
Machine learning predicts who will decline faster in Alzheimer’s disease using routine clinic data
Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...
This important study describes long-range serial dependence of performance on a visual texture discrimination training task that manipulated conditions to induce differing degrees of location transfer ...
Sleep is one of medicine's underused data streams. Clinically, disturbed sleep has often been treated as a symptom of a disorder, but sleep is also a physiological state in which brain, cardiac, ...
Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...
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 ...
Abstract: The conventional direct position determination (DPD) algorithms impose significant communication overhead since the raw signals are required to be transmitted from all sensor stations to a ...
ML is poised to become faster and more accessible by 2026. Simply having the support of GenAI already gives it an advantage over other AI-based solutions.
Saria is director of the Johns Hopkins Machine Learning and Healthcare Lab. Bayesian's technology, which received an FDA ...
Abstract: This paper introduces a novel two-stage active learning (AL) pipeline for automatic speech recognition (ASR), combining unsupervised and supervised AL methods. The first stage utilizes ...
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