使用人工智能通过自然语言处理框架识别SLE的临床表型和疾病轨迹
Silvia Laura Bosello1, Augusta Ortolan1,2, Lucia Lanzo1
1Rheumatology Unit-Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy.
Rheumatology (Oxford, England)
|February 17, 2026
概括
这项研究开发了一种人工智能驱动的自然语言处理管道,用于分析系统性红斑狼 (SLE) 患者的电子健康记录. 该框架确定了临床表型和疾病轨迹,有助于理解SLE异质性和进展.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 风湿病学研究 风湿病学研究
背景情况:
- 电子健康记录 (EHR) 包含有价值的非结构化数据,用于患者分析.
- 利用人工智能 (AI) 和自然语言处理 (NLP) 可以从复杂的医疗数据中获得洞察力.
- 系统性红斑狼 (SLE) 是一种异质的自身免疫性疾病,需要详细的患者表型.
研究的目的:
- 开发一个NLP管道用于识别SLE患者的临床表型和疾病轨迹,使用EHR数据.
- 创建一个结合人工智能和人类智能 (HI) 的框架,以进行可靠的数据提取和分析.
- 在第一次接触时描述SLE患者的表型,并分析其纵向疾病进展.
主要方法:
- 采用了一种标准化,逐步的框架,集成AI和HI.
- 为临床领域,爆发和复杂性表型创建了基于本体学的定义.
- 使用NLP管道从符合特定纳入标准的SLE患者的EHR中提取相关数据.
主要成果:
- 该研究分析了262名SLE患者,随访时间中位数为6年.
- 血液学,关节学,皮肤学和脏学领域在第一次接触时经常受到影响.
- 很大一部分患者 (43%) 呈现出高度复杂的表型,与更多的爆发和随着时间的推移增加的药物使用有关.
结论:
- 开发的AI驱动的框架使SLE患者从现实世界EHR数据的纵向表型化成为可能.
- 这种方法为研究SLE异质性和疾病进展提供了一种可行的方法.
- 这些发现表明在临床研究和改善SLE患者管理策略中的潜在应用.
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