改善了使用自然语言处理和机器学习方法对药物诱导的肝损伤文献的预测
Jung Hun Oh1, Allen Tannenbaum2,3, Joseph O Deasy1
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
Frontiers in genetics
|August 2, 2023
概括
这项研究引入了一个使用NLP和机器学习的AI驱动模型,自动识别药物诱导性肝损伤 (DILI) 文献. 该模型实现了高精度,简化了DILI的研究和药物开发.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算毒理学计算毒理学
- 药物监督 药物监督 药物监督
背景情况:
- 药物诱导性肝损伤 (DILI) 是急性肝衰竭的重要原因,也是药物开发中的一个挑战.
- 用于DILI研究的手册文献审查是耗时且效率低下的.
- 需要自动化方法来识别与DILI相关的科学文献.
研究的目的:
- 开发和验证一个集成的自然语言处理 (NLP) 和机器学习模型,用于自动识别DILI相关出版物.
- 提高DILI文献监测的效率,并支持药物安全性评估.
主要方法:
- 利用来自CAMDA模型开发挑战的14203个出版物.
- 采用了NLP技术,包括术语频率逆向文档频率 (TF-IDF) 和Word2Vec用于特征提取.
- 训练并验证了线性支向量机 (SVM) 分类模型.
主要成果:
- 在内部验证过程中,表现最好的模型获得了95.0%的准确性和95.0%的F1分数.
- 最终的SVM模型在独立测试集 (精度高达98.3%) 和外部验证集 (精度高达98.3%) 上显示出高性能.
结论:
- 开发的NLP/机器学习模型有效地使用标题和摘要识别了与DILI相关的文献.
- 这种自动化方法为加速DILI研究和加强药物安全监测提供了有价值的工具.
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