https://socictopen.socict.org/files/original/677336e446826269cb59075a49c26c12.pdf 9ba071f1a3dee89280dbb6d4f9cc0f26 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning Creator An entity primarily responsible for making the resource Ge F, Zhang D, Wu L, Mu H Description An account of the resource Fenfen Ge,1,* Di Zhang,2,* Lianhai Wu,2 Hongwei Mu2 1Clinical Psychology Department, Qingdao Municipal Hospital, Qingdao 266000, Shandong, People’s Republic of China; 2Mental Health Education and Counseling Center, Ocean University of China, Qingdao 266100, Shandong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hongwei MuMental Health Education and Counseling Center, Ocean University of China, Qingdao 266100, Shandong, People’s Republic of ChinaTel +86-0532-66782024Email mhw.1230@163.comBackground: The outbreak of the 2019 novel coronavirus disease (COVID-19) not only caused physical abnormalities, but also caused psychological distress, especially for undergraduate students who are facing the pressure of academic study and work. We aimed to explore the prevalence rate of probable anxiety and probable insomnia and to find the risk factors among a longitudinal study of undergraduate students using the approach of machine learning.Methods: The baseline data (T1) were collected from freshmen who underwent psychological evaluation at two months after entering the university. At T2 stage (February 10th to 13th, 2020), we used a convenience cluster sampling to assess psychological state (probable anxiety was assessed by general anxiety disorder-7 and probable insomnia was assessed by insomnia severity index-7) based on a web survey. We integrated information attained at T1 stage to predict probable anxiety and probable insomnia at T2 stage using a machine learning algorithm (XGBoost).Results: Finally, we included 2009 students (response rate: 80.36%). The prevalence rate of probable anxiety and probable insomnia was 12.49% and 16.87%, respectively. The XGBoost algorithm predicted 1954 out of 2009 students (translated into 97.3% accuracy) and 1932 out of 2009 students (translated into 96.2% accuracy) who suffered anxiety and insomnia symptoms, respectively. The most relevant variables in predicting probable anxiety included romantic relationship, suicidal ideation, sleep symptoms, and a history of anxiety symptoms. The most relevant variables in predicting probable insomnia included aggression, psychotic experiences, suicidal ideation, and romantic relationship.Conclusion: Risks for probable anxiety and probable insomnia among undergraduate students can be identified at an individual level by baseline data. Thus, timely psychological intervention for anxiety and insomnia symptoms among undergraduate students is needed considering the above factors.Keywords: COVID-19, anxiety, insomnia, cohort, machine learning Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Anxiety, covid-19, Cohort, machine learning, insomnia Source A related resource from which the described resource is derived Biotemas Publisher An entity responsible for making the resource available Universidade Federal de Santa Catarina Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Neurology. Diseases of the nervous system, Neurosciences. Biological psychiatry. Neuropsychiatry