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Circulation. Cardiovascular quality and outcomes
Research Hotspot & Journal Scope - Learning


Circulation. Cardiovascular quality and outcomes - DOI: 10.1161/CIRCOUTCOMES.119.005794
Simulation-Based Mastery Learning Improves Patient and Caregiver Ventricular Assist Device Self-Care Skills: A Randomized Pilot Trial.

Jeffrey H Barsuk · Jane E Wilcox · Elaine R Cohen · Rebecca S Harap · Kerry B Shanklin · Kathleen L Grady · Jane S Kim · Gretchen P Nonog · Lauren E Schulze · Alison M Jirak · Diane B Wayne · Kenzie A Cameron ·

Medicine
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Circulation. Cardiovascular quality and outcomes - DOI: 10.1161/CIRCOUTCOMES.118.005595
Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

Lichy Han · Mariam Askari · Russ B Altman · Susan K Schmitt · Jun Fan · Jason P Bentley · Sanjiv M Narayan · Mintu P Turakhia ·

Medicine
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The use of the structural expectation-maximization algorithm is the most common approach toward learning Bayesian networks from incomplete datasets.

Tractable learning of Bayesian networks from partially observed data [10.1016/J.PATCOG.2019.02.025]


A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1–45 Hz) and TBR could classify participants into ADHD, first‐degree relatives and/or control groups.

EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD [10.1111/ejn.14645]


We investigated activity logs of a novel web coding game to uncover learning trajectories and what people struggle with when learning flexible box (flexbox), a collection of new CSS layout features.

Understanding Learning Curves and Trajectories in CSS Layout [10.1145/3287324.3287387]


Furthermore, a simplified spike-timing-dependent plasticity learning rule is implemented by adjusting the widths of memory pulses.

Transposable 3T-SRAM Synaptic Array Using Independent Double-Gate Feedback Field-Effect Transistors [10.1109/TED.2019.2939393]


Here, we investigate oculomotor learning in healthy participants in response to a sinusoidally evolving error.

A generative learning model for saccade adaptation [10.1101/492512]


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