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首页> 外文期刊>Future generation computer systems >Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter
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Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter

机译：挖掘健康信息学的社会学：糖尿病患者在Twitter中的治疗生活方式依恋分析

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摘要

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.
机译：近年来，社交媒体中活跃用户的数量是指数增长的。尽管消息的主题多样性，但社交媒体已成为传播健康信息的重要载体，并聚集有关患者经历和情绪智力的见解。因此，本工作提出了一种新的分析方法，以通过社会互动来识别和解释患者的行为，看法和欣赏患者的近亲和近亲。在该方法的核心，是自然语言处理和机器学习的技术以及知识图的重建以及进一步的图形挖掘。案例研究是糖尿病群落，更具体地说，患者对1型糖尿病（T1D）和2型糖尿病（T2D）进行沟通。本研究中产生的结果表明了拟议方法发现有关患者对疾病的有用和非琐碎知识的有效性。这些知识可以在健康信息学的背景下使用，以更有效的方式促进健康的生活方式，以及改善与患者的沟通。

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  • 来源
    《Future generation computer systems》 |2020年第9期|214-232|共19页
  • 作者

    Gael Perez-Rodriguez; Martin Perez-Perez; Florentino Fdez-Riverola; Analia Lourenco;

  • 作者单位

    Department of Computer Science University of Vigo ESEI Campus As Lagoas 32004 Ourense Spain The Biomedical Research Centre (CINBIO) Campus Universitario Lagoas-Marcosende 36310 Vigo Spain SING Research Group. Galicia Sur Health Research Institute (ISS Galicia Sur) SERGAS-UVIGO Spain;

    Department of Computer Science University of Vigo ESEI Campus As Lagoas 32004 Ourense Spain The Biomedical Research Centre (CINBIO) Campus Universitario Lagoas-Marcosende 36310 Vigo Spain SING Research Group. Galicia Sur Health Research Institute (ISS Galicia Sur) SERGAS-UVIGO Spain;

    Department of Computer Science University of Vigo ESEI Campus As Lagoas 32004 Ourense Spain The Biomedical Research Centre (CINBIO) Campus Universitario Lagoas-Marcosende 36310 Vigo Spain SING Research Group. Galicia Sur Health Research Institute (ISS Galicia Sur) SERGAS-UVIGO Spain;

    Department of Computer Science University of Vigo ESEI Campus As Lagoas 32004 Ourense Spain The Biomedical Research Centre (CINBIO) Campus Universitario Lagoas-Marcosende 36310 Vigo Spain SING Research Group. Galicia Sur Health Research Institute (ISS Galicia Sur) SERGAS-UVIGO Spain Centre of Biological Engineering (CEB) University of Minho Campus de Gualtar 4710-057 Braga Portugal;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Sociome; Community detection; Topic modelling; Knowledge graphs; Diabetes; Twitter;

    机译：社会组;社区检测;主题建模;知识图表;糖尿病;推特;

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