Publications by authors named "N Maglaveras"
Operant keypress tasks in a reinforcement-reward framework where behavior is shaped by its consequence, show lawful relationships in human preference behavior (i.e., approach/avoidance) and have been analogized to "wanting".
View Article and Find Full Text PDFAnxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)].
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- The Covid-19 pandemic made it hard for hospitals to care for patients with severe lung problems, especially in Intensive Care Units (ICUs).
- This study looked at how sounds from patients’ lungs could help doctors understand their lung damage and predict how long they would stay in the ICU and their chances of survival.
- The researchers found that certain lung sounds, like “squawks” and “crackles,” were important because they related to how patients were doing and could help doctors make better decisions about their care.
Background: Heart failure (HF), a global health challenge, requires innovative diagnostic and management approaches. The rapid evolution of deep learning (DL) in healthcare necessitates a comprehensive review to evaluate these developments and their potential to enhance HF evaluation, aligning clinical practices with technological advancements.
Objective: This review aims to systematically explore the contributions of DL technologies in the assessment of HF, focusing on their potential to improve diagnostic accuracy, personalize treatment strategies, and address the impact of comorbidities.
Background: Despite COVID-19 vaccine mandates, many chose to forgo vaccination, raising questions about the psychology underlying how judgment affects these choices. Research shows that reward and aversion judgments are important for vaccination choice; however, no studies have integrated such cognitive science with machine learning to predict COVID-19 vaccine uptake.
Objective: This study aims to determine the predictive power of a small but interpretable set of judgment variables using 3 machine learning algorithms to predict COVID-19 vaccine uptake and interpret what profile of judgment variables was important for prediction.