ABSTRACT
In recent years, the proliferation of Massive Open Online Courses (MOOC) platforms on a global scale has been remarkable. Learners can now meet their learning demands with the help of MOOC. However, learners might not understand the course material well if they have access to a lot of information due to their inadequate expertise and cognitive ability. Personalized Recommender Systems (RSs), a cutting-edge technology, can assist in addressing this issue. It greatly increases resource acquisition through personalized availability for various people of all ages. Intelligent learning methods, such as machine learning and Reinforcement Learning (RL) can be used in RS challenges. However, machine learning needs supervised data and classical RL is not suitable for multi-task recommendations in online learning platforms. To address these challenges, the proposed framework integrates a Deep Reinforcement Learning (DRL) and multi-agent approach. This adaptive system personalizes the learning experience by considering key factors such as learner sentiments, learning style, preferences, competency, and adaptive difficulty levels. We formulate the interactive RS problem using a DRL-based Actor-Critic model named DRR, treating recommendations as a sequential decision-making process. The DRR enables the system to provide top-N course recommendations and personalized learning paths, enriching the student's experience. Extensive experiments on a MOOC dataset such as the 100 K Coursera course review validate the proposed DRR model, demonstrating its superiority over baseline models in major evaluation metrics for long-term recommendations. The outcomes of this research contribute to the field of e-learning technology, guiding the design and implementation of course RSs, to facilitate personalized and relevant recommendations for online learning students.
Subject(s)
Education, Distance , Humans , Education, Distance/methods , Learning , Machine LearningABSTRACT
BACKGROUND: Vancomycin is a commonly used antibiotic in critically ill patients for various indications. Critical illness imposes pharmacokinetic-pharmacodynamics challenges, which makes optimizing vancomycin in this population cumbersome. Data are scarce on the clinical impact of time to therapeutic trough levels of vancomycin in critically ill patients. This study aims to evaluate the timing to achieve therapeutic trough level of vancomycin on 30-day mortality in critically ill patients. METHOD: A retrospective cohort study was conducted for all adult critically ill patients with confirmed Gram-positive infection who received IV vancomycin between January 1, 2017, and December 31, 2020. We compared early (< 48 h) versus late (≥ 48 h) attainment of vancomycin therapeutic trough levels. The primary outcome was the 30-day mortality in critically ill patients. Secondary outcomes were the development of resistant organisms, microorganisms eradication within 4-5 days of vancomycin initiation, acute kidney injury (AKI), and length of stay (LOS). Propensity score-matched (1:1 ratio) used based on patient's age, serum creatinine, and albumin values at baseline. RESULTS: A total of 326 patients were included; 110 patients attained the therapeutic trough levels within 48 h of vancomycin initiation. Late achievement of the therapeutic trough levels was associated with higher 30-day mortality (HR: 2.54; 95% CI [1.24-5.22]; p = 0.01). Additionally, patients who achieved therapeutic trough levels of vancomycin late were more likely to develop AKI (OR = 2.59; 95% CI [1.01-6.65]; p = 0.04). Other outcomes were not statistically significant between the two groups. CONCLUSION: Early achievement of vancomycin therapeutic levels in patients with confirmed Gram-positive infection was associated with possible survival benefits.
