Identifying diseases that cause psychological trauma and social avoidance by Xgboost With the development of modern medical technology, the survival time of patients with most diseases is significantly prolonged. Changes in the physical, psychological, and social functions that occur after treatment and during treatment are a serious problem for patients and their families. Under the guidance of biopsychosocial medical model theory, patients need not only disease care, but also mental health. Therefore, identifying diseases that can cause psychological trauma and social avoidance will provide great guidance for medical staff and greatly improve the quality of life of patients. In this paper, Xgboost is introduced to identify diseases that cause psychological trauma and social avoidance. Firstly, the diseases are known to cause psychological problems are extracted from literatures, and then the relevant diseases are extracted in the Disease Ontology (DO), and finally the proteins corresponding to each disease are obtained from Uniprot. We encoded proteins as the feature of each disease and put them into Xgboost. Finally, we identified the diseases that cause psychological trauma and social avoidance. Leaveoneout cross validation has been introduced to test the performance of Xgboost. We achieved AUC 0.95 and 0.61 AUPR. In addition, several case studies have been done to prove the accuracy of our result.

Computational modeling of stigmatized behaviour in provaccination and antivaccination discussions on social media Much research has been done within the social sciences on the interpretation and influence of stigma on human behaviour and health, which result in outofgroup exclusion, distancing, cognitive separation, status loss, discrimination, ingroup pressure, and often lead to disengagement, nonadherence to treatment plan, and prescriptions by the doctor. However, little work has been conducted on computational identification of stigma in general and in social media discourse in particular. In this paper, we develop the annotation scheme for stigma based on social science theories, and perform a corpus study on the data from Facebook groups on vaccination. The data from provaccination and antivaccination discussion groups are annotated by trained annotators and by MTurk annotators. We analyze the annotations using LIWC (Linguistic Inquiry and Word Count) software and TFIDF in order to identify differentiating features between stigmatizing vs. nonstigmatizing content. Our corpus study lays a valuable foundation in computational modeling of social stigma, as it can serve as validationinterpretation of the social science theories through the prism of laypeople understanding. The annotated corpora can be subsequently used for automatic stigma identification. Moreover, the annotation scheme can be applied to study stigma across different themes and diseases and can be utilized during public health informational campaigns and health interventions.