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谈论疾病;开发一种患者和公众优先考虑的疾病表型模型
Karin Slater1,2,3,4, Paul N Schofield5, James Wright6
1Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK. k.slater@bham.ac.uk.
NPJ digital medicine
|September 30, 2024
概括
社交媒体数据揭示了公众对疾病表型的观点,识别了在学术文献中没有发现的新兴关联. 这些公共卫生数据为临床意识和患者护理提供了宝贵的见解.
科学领域:
- 生物医学信息学是生物医学信息学.
- 计算语言学计算语言学
- 公共卫生 公共卫生
背景情况:
- 深度表型化使用标准化术语进行全面的疾病描述,帮助二次分析和患者护理.
- 目前的深度表型化严重依赖学术文献和数据库,可能缺少公众的观点.
- 医疗保健利益相关者的优先事项和看法之间存在很大的差距.
研究的目的:
- 利用社交媒体数据开发一个公共视角的疾病表型模型.
- 将这种社交媒体衍生模型与现有的学术表型数据库进行比较.
- 从公众的角度来看,识别新的疾病现象型关联.
主要方法:
- 从311种疾病的社交媒体中提取了52198个积极的疾病表型关联.
- 确定了24618个在学术/文献衍生模型中缺少的新型表型关联.
- 对来自社交媒体的关联进行了专家临床审查,以确定其有效性和临床相关性.
主要成果:
- 与学术资源相比,社交媒体数据产生了一个独特的疾病表型模型.
- 确定了超过14,500个重要的新型表型关联,特别是关于生活质量和内分泌,消化和生殖系统疾病.
- 社交媒体衍生出的关联被认为具有临床相关性,与基于文献的相关性相比较,并且在患者接触中经常观察到.
结论:
- 社交媒体数据为深度表型化提供了与传统学术来源有价值的,互补的视角.
- 整合公众观点可以提高临床意识,改善数据分析,并调整医疗保健优先事项.
- 这种方法通过结合患者报告的经验,弥合了各种医疗保健利益相关者之间的理解.
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