人工智能驱动的药物监测:通过深度学习和NLP提高药物不良反应的检测
Dr Bharti Khemani1, Dr Sachin Malave2, Samyukta Shinde3
1Assistant Professor, A. P. SHAH Institute of Technology, Survey No 12, 13, Opp. Hypercity Mall, Kasarvadavali, Ghodbunder Road, Thane West, Thane, Maharashtra 400615, India.
MethodsX
|July 18, 2025
概括
这项研究引入了一个AI框架,通过在临床试验中检测药物不良反应 (ADRs) 来提高药物安全性. 混合模型通过整合多样化的数据和深度学习来增强药物监管,以更准确和更易于解释的ADR检测.
科学领域:
- 药监和药物安全 药监和药物安全
- 医疗保健中的人工智能
- 临床试验数据分析
背景情况:
- 随着临床试验数据量不断增加,传统的药物安全监测面临挑战.
- 药物不良反应 (ADR) 的低报告和延迟检测是关键问题.
- 不同质的数据,阶级不平衡和有限的传统方法阻碍了准确的ADR检测.
研究的目的:
- 开发一种由人工智能驱动的混合框架,用于准确地检测药监中的严重不良事件 (SAE).
- 整合结构化和非结构化临床数据,以加强ADR识别.
- 通过可解释的AI预测来超越传统的信号检测方法,以便实时临床使用.
主要方法:
- 利用混合人工智能框架,结合深度学习和自然语言处理 (NLP).
- 综合结构化 (人口统计,实验室结果) 和非结构化 (临床注释) 数据.
- 采用机器学习 (ML) 和深度学习 (DL) 模型,包括随机森林,梯度增强机器,卷积神经网络 (CNNs),BERT和GPT.
- 应用特征工程和数据失衡技术.
主要成果:
- 在CNN模型实现了85%的准确性,超越了后勤回归 (78%) 和支持矢量机 (80%).
- 与BERT一起的CNN模型在检测ADR方面表现出最高的准确性.
- 确定了影响不良反应可能性的显著的人口统计和临床因素.
- 在预测ADR方面证明了更好的准确性和可解释性.
结论:
- 使用先进的ML和NLP的预测建模显著增强了药监工作.
- 将各种临床数据与人工智能集成,可以改善ADR检测,与良好的临床实践 (GCP) 保持一致.
- 研究结果为药物安全的有针对性的监测和风险减轻策略提供了宝贵的见解.
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