Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter
| العنوان: | Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter |
|---|---|
| المؤلفون: | Martín Pérez-Pérez, Gael Pérez-Rodríguez, Anália Lourenço, Florentino Fdez-Riverola |
| المساهمون: | Universidade do Minho |
| المصدر: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| بيانات النشر: | Elsevier, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Topic model, Computer Networks and Communications, Computer science, Applied psychology, Twitter, 02 engineering and technology, Disease, Type 2 diabetes, Health informatics, Diabetes mellitus, Knowledge graphs, 0202 electrical engineering, electronic engineering, information engineering, medicine, Social media, Type 1 diabetes, Science & Technology, Community detection, business.industry, Emotional intelligence, Diabetes, 020206 networking & telecommunications, medicine.disease, 3. Good health, Knowledge graph, Hardware and Architecture, 020201 artificial intelligence & image processing, Sociome, business, Software, Topic modelling |
| الوصف: | Supplementary material related to this article can be found online at https://doi.org/10.1016/j.future.2020.04.025.SupplementaryTest material 1: this file contains the 23 user communities detected using the GLay algorithm. In recent years, the number of active users in social media has grown exponentially. Despite the thematic diversity of the messages, social media have become an important vehicle to disseminate health information as well as to gather insights about patients experiences and emotional intelligence. Therefore, the present work proposes a new methodology of analysis to identify and interpret the behaviour, perceptions and appreciations of patients and close relatives towards a health condition through their social interactions. At the core of this methodology are techniques of natural language processing and machine learning as well as the reconstruction of knowledge graphs, and further graph mining. The case study is the diabetes community, and more specifically, the patients communicating about type 1 diabetes (T1D) and type 2 diabetes (T2D). The results produced in this study show the effectiveness of the proposed method to discover useful and non-trivial knowledge about patient perceptions of disease. Such knowledge may be used in the context of Health Informatics to promote healthy lifestyles in more efficient ways as well as to improve communication with the patients. This work was partially supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684), the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2019–2022) and the European Union (European Regional Development Fund - ERDF)- Ref. ED431G2019/06, and Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group. The authors also acknowledge the Postdoc contract of Martín Pérez-Pérez, funded by the Xunta de Galicia. info:eu-repo/semantics/publishedVersion |
| وصف الملف: | application/pdf |
| اللغة: | English |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::907e111120cc1f7c448d946e11469a43Test https://hdl.handle.net/1822/65086Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....907e111120cc1f7c448d946e11469a43 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |