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Decorrelative network architecture for robust electrocardiogram classification.
Patterns (N Y)
December 2024
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
To achieve adequate trust in patient-critical medical tasks, artificial intelligence must be able to recognize instances where they cannot operate confidently. Ensemble methods are deployed to estimate uncertainty, but models in an ensemble often share the same vulnerabilities to adversarial attacks. We propose an ensemble approach based on feature decorrelation and Fourier partitioning for teaching networks diverse features, reducing the chance of perturbation-based fooling.
View Article and Find Full Text PDFSurvival machine learning methods for mortality prediction after heart transplantation in the contemporary era.
PLoS One
January 2025
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms-Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr.
View Article and Find Full Text PDFThe impact of achievement goals on college students' English performance-A moderated mediation model.
PLoS One
January 2025
Faculty of Educational Studies, Universiti Putra Malaysia, Seri Kembangan, Selangor, Malaysia.
The study investigated the relationship between learning engagement and achievement goals, and English performance among college students. With the increasing popularity of online teaching methods, exploring how different teaching modes (online and classroom teaching) might influence students' learning outcomes is important. The researcher sought to understand how adopting different achievement goals such as mastery and performance-avoidance approaches could impact English performance and learning engagement.
View Article and Find Full Text PDF[Digitally based clinically oriented anatomy: the future of teaching].
Chirurgie (Heidelb)
January 2025
Abteilung für Anatomie, Fakultät VI Medizin und Gesundheitswissenschaften, Carl von Ossietzky Universität Oldenburg, Carl-von-Ossietzky-Str 9-11, 26129, Oldenburg, Deutschland.
University teaching is undergoing radical changes. Rising student numbers and the progressive digitalization of routine daily life are also leading to the testing of various new teaching and learning formats. This article provides an overview of the reasons for and approaches used to effectively and efficiently organize teaching of anatomy using digital learning methods and to fulfil the expectations of students.
View Article and Find Full Text PDFPurpose: Although there is a robust literature on the benefits and outcomes of active learning in medical education, little is known about the faculty experience of transitioning from lecture-based teaching to active learning in the preclinical, foundational science curriculum. The authors explored how faculty describe changing from lecture to active learning and how that change relates to the loci of control and basic psychological needs of faculty.
Method: Using a phenomenographic approach, the authors interviewed faculty at 3 medical schools who taught before, during, and after required shifts to active learning.
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