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在克罗恩病和性结肠炎中,对肠外表现和相关自身免疫性疾病的网络分析
Daniel C Baumgart1,2,3, C Hing Cheng4,5, Tian X Du4,5
1Department of Gastroenterology and Hepatology, Charité Medical School, Humboldt-University of Berlin, Berlin, Germany. daniel.baumgart@charite.de.
人工智能 (AI) 在超过30,000名炎症性肠病 (IBD) 患者中确定了常见的肠外表现 (EIM) 和相关的免疫障碍 (AID). 人工智能工具可视化疾病联系和时间模式,揭示以前未知的患者子组关系.
科学领域:
- 医疗信息学 医疗信息学
- 免疫学 免疫学 免疫学
- 胃肠病学 胃肠病学
背景情况:
- 炎症性肠病 (IBD) 包括克罗恩氏病 (CD) 和性结肠炎 (UC),通常呈现出肠外表现 (EIM) 和相关免疫障碍 (AID).
- 了解EIM/AID在大型IBD队列中的复杂相互作用和集群对于个性化医学和治疗策略至关重要.
研究的目的:
- 利用人工智能 (AI) 来检测,可视化和分析IBD患者的大队伍中EIM和AID的发生,频率,序列和集群.
- 开发一个交互式的网络应用程序来探索这些复杂的疾病关系,称为"diseasome"和"interactome".
主要方法:
- 对30,334名炎症性肠病 (IBD) 患者的大数据集的分析,包括克罗恩病 (CD),性结肠炎 (UC) 和IBD未分类 (IBD-U).
- 应用人工智能 (AI) 算法来识别和可视化肠外表现 (EIM) 和相关免疫障碍 (AID) 的模式.
- 开发一个免费的交互式Web应用程序,用于可视化疾病群体,关系和时间模式.
主要成果:
- 超过57%的IBD患者表现出一个或多个EIM和/或AID,与UC相比,CD的患病率更高.
- 精神,肌肉骨和泌尿器官疾病是与IBD相关的最常见的EIM/AID.
- 人工智能在CD,UC和整体IBD队列中确定了不同的EIM/AID社区,揭示了疾病关联和时间模式的复杂网络.
结论:
- 人工智能是一个强大的工具,可以在大型患者数据集中发现复杂的模式和关系,特别是在具有各种表现的IBD等疾病中.
- 开发的交互式可视化工具为IBD的"疾病体"和"相互作用体"提供了新的见解,促进了进一步的研究,并可能改善患者的护理.
- 这些发现突显了IBD患者中EIM和AID的重大负担,并强调了需要全面的管理策略的需要.
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