ABSTRACT
Mental health is directly related to people's physical health, social adaptation and study and work efficiency. Mental health education for college students has also become the focus of education in China. By combining the knowledge network with the psychological scales, this study tried to not only find the most significant abnormal psychology but also evaluate the relationship between different abnormal psychology of college students. The data of this study were the 'The symptom Checklist scale (SCL-90)' questionnaire of the 2017 freshman census of a university in China. The data were statistically analyzed by means of knowledge network method including centrality, systematic clustering and K-plex analysis. From the results, we can see that the 'phobic anxiety' was the core dimension; all the mild symptoms were clustered into one type, and all the severe symptoms were clustered into another type; other dimensions were more closely related in addition to 'interpersonal sensitivity' and 'somatization'. In psychological measurement research, knowledge network method can be combined to better evaluate the psychological state of the groups and obtain more complete assessment results, which provides a basis for the revision of the psychological scales in terms of balance and item setting.
Subject(s)
Mental Health , Students/psychology , Universities , Adult , Anxiety/psychology , China , Cluster Analysis , Depression/psychology , Female , Hostility , Humans , Male , Models, Statistical , Obsessive-Compulsive Disorder/psychology , Paranoid Disorders/psychology , Phobic Disorders/psychology , Psychiatric Status Rating Scales , Psychotic Disorders/psychology , Severity of Illness Index , Somatoform Disorders/psychology , Surveys and Questionnaires , Young AdultABSTRACT
Mental disorders are important diseases with a high prevalence rate in the general population. Common mental disorders are complex diseases with high heritability, and their pathogenesis is the result of interactions between genetic and environmental factors. However, the relationship between mental disorders and genes is complex and difficult to evaluate. Additionally, some mental disorders involve numerous genes, and a single gene can also be associated with different types of mental disorders.This study used text mining (including word frequency analysis, cluster analysis, and association analysis) of the PubMed database to identify genes related to mental disorders.Word frequency analysis revealed 52 high-frequency genes important in studies of mental disorders. Cluster analysis showed that 5-HTT, SLC6A4, and MAOA are common genetic factors in most mental disorders; the intra-group genes in each cluster were highly correlated. Some mental disorders may have common genetic factors; for example, there may be common genetic factors between 'Affective Disorders' and 'Schizophrenia.' Association analysis revealed 35 frequent itemsets and 25 association rules, indicating close associations among genes. The results of association rules showed that CCK, MAOA, and 5-HTT are the most closely related.We used text mining technology to analyze genes related to mental disorders to further summarize and clarify the relationships between mental disorders and genes as well as identify potential relationships, providing a foundation for future experiments. The results of the associative analysis also provide a reference for multi-gene studies of mental disorders.
