Aims: The impact of the COVID-19 pandemic spans all aspects of life. This study aimed to investigate the mental health situation of Jordanian university students during the COVID-19 pandemic. Instrument & Methods: This cross-sectional study on 1000 university students from April to May 2020. A web-based survey that investigates students' psychological ...

The number of digital medical documents is increasing continuously; several medical websites share a lot of unclassified articles. These articles have very long texts that should be read to determine the topic of each document. The classification of these documents is important so researchers can use these documents easily and the effort and time in reading and searching for a specific topic will be reduced. Therefore, an automatic way to extract latent topics from these text documents is needed. Topic modeling is one of the techniques used to deal with this problem. In this paper, a medical collection of documents is used; this collection contains documents from three types of widespread diseases (Heart Diseases, Blood Pressure and Cholesterol). LDA topic modeling technique is applied to classify these documents into the previous mentioned topics. An evaluation of the algorithm’s results is done and the LDA shows a good level of classification accuracy.