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This study aimed to establish a risk prediction model of PTSD based on a machine learning algorithm to provide a basis for the extensive assessment and prediction of the PTSD risk status in adults during a pandemic. Materials and Methods: Model indexes were screened based on the cognitive\u2013phenomenological\u2013transactional (CPT) theoretical model. During the study period (1 March to 15 March 2020), 2067 Chinese residents were recruited using Research Electronic Data Capture (REDCap). Socio-demographic characteristics, PTSD, depression, anxiety, social support, general self-efficacy, coping style, and other indicators were collected in order to establish a neural network model to predict and evaluate the risk of PTSD. Results: The research findings showed that 368 of the 2067 participants (17.8%) developed PTSD. The model correctly predicted 90.0% (262) of the outcomes. Receiver operating characteristic (ROC) curves and their associated area under the ROC curve (AUC) values suggested that the prediction model possessed an accurate discrimination ability. In addition, depression, anxiety, age, coping style, whether the participants had seen a doctor during the COVID-19 quarantine period, and self-efficacy were important indexes. Conclusions: The high prediction accuracy of the model, constructed based on a machine learning algorithm, indicates its applicability in screening the public mental health status during the COVID-19 pandemic quickly and effectively. This model could also predict and identify high-risk groups early to prevent the worsening of PTSD symptoms.<\/jats:p>","DOI":"10.3390\/medicina58121704","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T02:10:54Z","timestamp":1669169454000},"page":"1704","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Machine Learning-Based Risk Prediction Model for Post-Traumatic Stress Disorder during the COVID-19 Pandemic"],"prefix":"10.3390","volume":"58","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Nursing, School of Medicine, Xiamen University, Xiamen 361102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya-Nan","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Nursing, School of Medicine, Xiamen University, Xiamen 361102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Gang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Clinical Medicine, School of Medicine, Xiamen University, Xiamen 361102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"He","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Informatics, Xiamen University, Xiamen 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Gu","family":"He","sequence":"additional","affiliation":[{"name":"Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long-Biao","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Informatics, Xiamen University, Xiamen 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qu","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Nursing, School of Medicine, Xiamen University, Xiamen 361102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2020). 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