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BackgroundSubjective well-being (SWB), also known as happiness, plays an important role in evaluating both mental and physical health. Adolescents deserve specific attention because they are under a great variety of stresses and are at risk for mental disorders during adulthood.AimThe present paper aims to predict undergraduate students’ SWB by machine learning method.MethodsGradient Boosting Classifier which was an innovative yet validated machine learning approach was used to analyse data from 10 518 Chinese adolescents. The online survey included 298 factors such as depression and personality. Quality control procedure was used to minimise biases due to online survey reports. We applied feature selection to achieve the balance between optimal prediction and result interpretation.ResultsThe top 20 happiness risks and protective factors were finally brought into the predicting model. Approximately 90% individuals’ SWB can be predicted correctly, and the sensitivity and specificity were about 92% and 90%, respectively.ConclusionsThis result identifies at-risk individuals according to new characteristics and established the foundation for adolescent prevention strategies.

Named entity recognition (NER) is one of the basic techniques in natural language processing tasks. Chinese NER is complicated and difficult which remains a major challenge. One of the main reasons is that the boundaries of entities in Chinese are blurred and closely related to word segmentation results. Previous studies for this task have broadly divided into two categories, word-based, and character-based methods. However, the former class suffers from the word segmentation errors, and the latter cannot make full use of the information on multiple granularities. To address these problems, we investigate a new dynamic meta-embeddings method and apply it to Chinese NER task, which utilizes attention mechanism to combine features of both character and word granularity in embedding layer. The meta-embeddings created by our method are dynamic, data-specific, and task-specific, since the meta-embeddings for same characters in different sentence sequences are distinct. The experiments on MSRA and LiteratureNER datasets validate the effectiveness of our model, and this method achieves the state-of-the-art results on LiteratureNER.

Personality and subjective well-being (SWB) have been suggested to be strongly related in previous studies. This study was intended to confirm the relationship between personality and SWB and tried to seek out the genetic variants which underlie both personality and SWB. The subjects were 890 participants from Chinese Han population. We evaluated their personality using the Big Five Inventory (BFI) and used the Satisfaction With Life Scale (SWLS) to reflect their SWB. Five single nucleotide polymorphisms (SNPs) were selected from the literature (rs1426371, rs2164273, rs322931, rs3756290, rs490647) and genotyped for genetic association study. We found negative correlations between neuroticism and SWB. On the contrary, extraversion and agreeableness were positively associated with SWB. Three SNPs (rs2164273, rs3756290, rs490647) out of the five were found to connect with personality (extraversion, neuroticism, conscientiousness and openness to experience) and rs490647 variants of GRIK3 was also associated with SWB. Individuals carrying G allele at this site were predisposed to have lower risk to be neuroticism and greater chance to be extraverted, open and satisfied with their life. In summary, our study revealed that rs490647 might be a good candidate genetic variant for personality and SWB in Chinese Han population.

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