药物处方中的机器学习:系统性审查
Alexa Iancu1, Ines Leb1, Hans-Ulrich Prokosch1
1Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany.
International journal of medical informatics
|November 8, 2023
概括
机器学习算法可以提高药物剂量准确性,特别是在儿童中. 决策树和回归方法显示出药物剂量预测的优越预测能力.
科学领域:
- 药理学和计算科学 药理学和计算科学
- 人工智能在医学中的应用
背景情况:
- 药物处方是复杂的,儿科医生由于儿科数据有限,经常处方非标签.
- 在儿童中非标签处方增加了错误剂量和不良药物事件的风险.
研究的目的:
- 审查用于预测药物剂量的机器学习 (ML) 算法.
- 为了确定准确的儿科药物处方所需的变量.
主要方法:
- 在PubMed,IEEE Xplore和PROSPERO的系统文献搜索中,寻找药物剂量的ML算法.
- 使用IJMEDI检查清单进行质量评估,并对36项符合条件的研究进行详细审查.
- 对基于ML的剂量预测的输入/输出变量的分析.
主要成果:
- 36项研究符合纳入标准,其中5项是高质量的.
- 决策树和回归ML算法超过了神经网络和支持矢量机器.
- 组合方法 (包装,提升) 提高了剂量预测的准确性.
结论:
- ML算法可以简化处方并提高剂量正确性.
- 识别的ML方法和变量是儿科药物剂量预测的起点.
- 将基于生理学的药理动力学模型与ML相结合,为提高准确性提供了显著的潜力.
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