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Identifying Informative Coronavirus Tweets Using Recurrent Neural Network Document Embedding, Rami Yousuf

Palestine Technical University Research Journal

The coronavirus pandemic has led to the spread of tremendous fake news and misleading information through tweets. Hence, an interesting task of classifying tweets into informative and uninformative has motivated researchers to employ machine learning techniques. The state-of-the-art studies showed high dependency on transformers architecture. However, the transformers architecture suffers from the catastrophic forgetting problem where important contextual information is being forgotten by the gradients. Therefore, this paper proposes a document embedding using Recurrent Neural Network. Lastly, three classifiers of LR, SVM and MLP have been used to classify documents into Informative and Uninformative. Using the benchmark dataset of WNUT-2020 …

Wellness Review 2021, Part 2, Brian A. Ferguson, Martin Huecker

Journal of Wellness

Introduction: This article presents Part 2 of the biannual JWellness Review of literature from 2021 (July – December). We emphasize new science and resilience initiatives published outside of JWellness that seek understanding of burnout and thriving among healthcare professionals (HCPs).

Methods: For the interval of July 1 to December 30, 2021, PubMed was queried for empirical and observational research studies, review articles, guideline summaries, letters, and editorials. Of 93 results, we reviewed methods and salient points to arrive at a final list of 48 articles for inclusion.

Literature in Review: Common themes that emerged included teamwork, EMR optimization, group decompression, …