在阿尔茨海默氏症临床试验中,人工智能支持的安全监测
Gustavo A Jimenez-Maggiora1, Michael C Donohue1, Michael S Rafii1
1Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, United States.
The journal of prevention of Alzheimer's disease
|January 12, 2025
概括
人工智能 (AI) 和自然语言处理 (NLP) 在阿尔茨海默病 (AD) 临床试验中显著改善了不良事件 (AE) 编码. 自动化方法提高了准确性,并降低了与临床医生传统手动审查相比的成本.
科学领域:
- 临床信息学 临床信息学
- 生物医学数据科学 生物医学数据科学
- 神经科学 临床试验 临床试验
背景情况:
- 临床试验参与者的安全依赖于准确的不良事件 (AE) 数据监测.
- 传统的AE数据编码涉及专家临床医生手动审查,这可能耗时且容易变化.
研究的目的:
- 探索自然语言处理 (NLP) 和人工智能 (AI) 的应用,以简化阿尔茨海默病 (AD) 临床试验中的AE数据编码.
- 评估自动编码的效率,准确性,可靠性和一致性,与传统的临床医生编码相比.
主要方法:
- 开发了针对AD不良事件的黄金标准数据集.
- 评估了基于NLP的AE分类模型,使用定量回顾性分析.
- 将自动编码性能与专家临床医生的编码进行比较,使用F1分数和预测准确度.
主要成果:
- 基于AI的AE编码显示了与手动临床医生编码相比,准确度显著增加 (约20%).
- 自动编码导致大幅降低成本 (约80%) 和提高效率.
结论:
- 将人工智能与人类专业知识相结合,提高了AE编码的效率和质量.
- 这种综合方法改善了AD研究中临床试验的整体安全监测.
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