Identifying diseases that cause psychological trauma and social avoidance by GCN-Xgboost
- PDF / 1,338,648 Bytes
- 16 Pages / 595.276 x 790.866 pts Page_size
- 9 Downloads / 176 Views
RESEARCH
Open Access
Identifying diseases that cause psychological trauma and social avoidance by GCN‑Xgboost Huijuan Xu1, Hairong Wang2*, Chenshan Yuan3, Qinghua Zhai4, Xufeng Tian5, Lei Wu5 and Yuanyuan Mi5
From Biological Ontologies and Knowledge bases workshop 2019 San Diego, CA, USA. 18-21 November 2019 *Correspondence: [email protected] 2 Department of Nursing, Shanxi Provincial Cancer Hospital, Taiyuan, People’s Republic of China Full list of author information is available at the end of the article
Abstract Background: With the rapid development of medical treatment, many patients not only consider the survival time, but also care about the quality of life. Changes in physical, psychological and social functions after and during treatment have caused a lot of troubles to patients and their families. Based on the bio-psycho-social medical model theory, mental health plays an important role in treatment. Therefore, it is necessary for medical staff to know the diseases which have high potential to cause psychological trauma and social avoidance (PTSA). Results: Firstly, we obtained diseases which can cause PTSA from literatures. Then, we calculated the similarities of related-diseases to build a disease network. The similarities between diseases were based on their known related genes. Then, we obtained these diseases-related proteins from UniProt. These proteins were extracted as the features of diseases. Therefore, in the disease network, each node denotes a disease and contains the information of its related proteins, and the edges of the network are the similarities of diseases. Then, graph convolutional network (GCN) was used to encode the disease network. In this way, each disease’s own feature and its relationship with other diseases were extracted. Finally, Xgboost was used to identify PTSA diseases. Conclusion: We developed a novel method ‘GCN-Xgboost’ and compared it with some traditional methods. Using leave-one-out cross-validation, the AUC and AUPR were higher than some existing methods. In addition, case studies have been done to verify our results. We also discussed the trajectory of social avoidance and distress during acute survival of breast cancer patients. Keywords: Graph convolutional network, Xgboost, Psychological trauma, Breast cancer
Background When people experience some sudden diseases, catastrophic injury or sexual violence, they are very likely to get post-traumatic stress disorder (PTSD) [1] which is a series of mental disorders. PTSD is a kind of delayed psychogenic response which is hard to
© The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative C
Data Loading...
