Publications by authors named "Zhongbing Yang"
Background: Adolescents often experience difficulties with sleep quality. The existing literature on predicting severe sleep disturbance is limited, primarily due to the absence of reliable tools.
Methods: This study analyzed 1966 university students.
Article Synopsis
- * Researchers analyzed data from 2,088 college students using various machine learning models, with the extreme gradient boosting machine (eXGBM) model outperforming the others, achieving an area under the curve (AUC) of 0.932.
- * The AI tool showed strong predictive abilities during external validation, confirming its effectiveness in identifying students at risk of severe mental health issues.
Background: Sleep problems are prevalent among university students, yet there is a lack of effective models to assess the risk of sleep disturbance. Artificial intelligence (AI) provides an opportunity to develop a platform for evaluating the risk. This study aims to develop and validate an AI platform to stratify the risk of experiencing sleep disturbance for university students.
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