Similar Publications

DOME: Directional medical embedding vectors from electronic health records.

J Biomed Inform

January 2025

Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address:

Motivation: The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data.

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With the continuous development of intelligent transportation systems, traffic safety has become a major societal concern, and vehicle trajectory anomaly detection technology has emerged as a crucial method to ensure safety. However, current technologies face significant challenges in handling spatiotemporal data and multi-feature fusion, including difficulties in big data processing, and have room for improvement in these areas. To address these issues, this paper proposes a novel method that combines autoencoders, Mahalanobis distance, and dynamic Bayesian networks for anomaly detection.

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AI and Uncertain Motivation: Hidden allies that impact EFL argumentative essays using the Toulmin Model.

Acta Psychol (Amst)

January 2025

Department of English Language, College of Arts, King Faisal University, Al Ahsa, Saudi Arabia.

This study investigates the combined impact of artificial intelligence (AI) tools and Uncertain Motivation (UM) strategies on the argumentative writing performance of Saudi EFL learners, using the Toulmin Model. Sixty Saudi EFL students participated in four writing tasks, with results demonstrating significant improvements in essay quality, particularly in clarity, structure, and depth. AI tools provided real-time feedback, enhancing students' ability to refine claims, data, backing, and counterarguments.

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Does the stop-signal P3 reflect inhibitory control?

Cortex

December 2024

Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA; Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA; Cognitive Control Collaborative, University of Iowa, Iowa City, IA, USA. Electronic address:

The ability to stop already-initiated actions is paramount to adaptive behavior. In psychology and neuroscience alike, action-stopping is a popular model behavior to probe inhibitory control - the underlying cognitive control process that is purportedly vital to regulating thoughts and actions. Starting with seminal work in the 1990s, the frontocentral stop-signal P3 - an event-related potential derived from scalp EEG - has been proposed as a neurophysiological index of inhibitory control during action-stopping.

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