Predictive modelling in times of public health emergencies: patients’ non-transport decisions during the COVID-19 pandemic by Hassan Farhat (9000509)

“…This study aimed to utilise an extensive dataset, which included the period of the COVID-19 pandemic, in a modern Middle Eastern Emergency Medical Service to comprehend and predict the behaviour of non-transport decisions, a major multi-variable factor in pre-hospital emergency medicine. </p><h3>Methods</h3><p dir="ltr">Using Python® programming language, this study employed various supervised machine-learning algorithms, including parametric probabilistic models, such as logistic regression, and non-parametric models, including decision trees, random forest (RF), extra trees, AdaBoost, and k-nearest neighbours (KNN), using a dataset of non-transported patients (refused transport and did not receive treatment versus those who refused transport and received treatment) between 2018 and 2022. …”

Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach by Arfan Ahmed (17541309)

“…A methodological workflow is provided for the future analysis of review comments.</p><h2>Other Information</h2><p dir="ltr">Published in: JMIR Formative Research<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/27654" target="_blank">https://dx.doi.org/10.2196/27654</a></p>…”

Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study by Alaa Abd-Alrazaq (17430900)

“…</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Medical Internet Research<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/19016" target="_blank">https://dx.doi.org/10.2196/19016</a></p>…”