Abstract：
BACKGROUND: Medication prescription is a complex process that could benefit from current research and development in machine learning through decision support systems. Particularly pediatricians are forced to prescribe medications \"off-label\" as children are still underrepresented in clinical studies, which leads to a high risk of an incorrect dose and adverse drug effects.
METHODS: PubMed, IEEE Xplore and PROSPERO were searched for relevant studies that developed and evaluated well-performing machine learning algorithms following the PRISMA statement. Quality assessment was conducted in accordance with the IJMEDI checklist. Identified studies were reviewed in detail, including the required variables for predicting the correct dose, especially of pediatric medication prescription.
RESULTS: The search identified 656 studies, of which 64 were reviewed in detail and 36 met the inclusion criteria. According to the IJMEDI checklist, five studies were considered to be of high quality. 19 of the 36 studies dealt with the active substance warfarin. Overall, machine learning algorithms based on decision trees or regression methods performed superior regarding their predictive power than algorithms based on neural networks, support vector machines or other methods. The use of ensemble methods like bagging or boosting generally enhanced the accuracy of the dose predictions. The required input and output variables of the algorithms were considerably heterogeneous and differ strongly among the respective substance.
CONCLUSIONS: By using machine learning algorithms, the prescription process could be simplified and dosing correctness could be enhanced. Despite the heterogenous results among the different substances and cases and the lack of pediatric use cases, the identified approaches and required variables can serve as an excellent starting point for further development of algorithms predicting drug doses, particularly for children. Especially the combination of physiologically-based pharmacokinetic models with machine learning algorithms represents a great opportunity to enhance the predictive power and accuracy of the developed algorithms.
METHODS: PubMed, IEEE Xplore and PROSPERO were searched for relevant studies that developed and evaluated well-performing machine learning algorithms following the PRISMA statement. Quality assessment was conducted in accordance with the IJMEDI checklist. Identified studies were reviewed in detail, including the required variables for predicting the correct dose, especially of pediatric medication prescription.
RESULTS: The search identified 656 studies, of which 64 were reviewed in detail and 36 met the inclusion criteria. According to the IJMEDI checklist, five studies were considered to be of high quality. 19 of the 36 studies dealt with the active substance warfarin. Overall, machine learning algorithms based on decision trees or regression methods performed superior regarding their predictive power than algorithms based on neural networks, support vector machines or other methods. The use of ensemble methods like bagging or boosting generally enhanced the accuracy of the dose predictions. The required input and output variables of the algorithms were considerably heterogeneous and differ strongly among the respective substance.
CONCLUSIONS: By using machine learning algorithms, the prescription process could be simplified and dosing correctness could be enhanced. Despite the heterogenous results among the different substances and cases and the lack of pediatric use cases, the identified approaches and required variables can serve as an excellent starting point for further development of algorithms predicting drug doses, particularly for children. Especially the combination of physiologically-based pharmacokinetic models with machine learning algorithms represents a great opportunity to enhance the predictive power and accuracy of the developed algorithms.
摘要：
背景：药物处方是一个复杂的过程，可以通过决策支持系统从当前的机器学习研究和开发中受益。特别是儿科医生被迫开出“标签外”的药物，因为儿童在临床研究中的代表性仍然不足，这导致了不正确剂量和药物不良反应的高风险。
方法：PubMed，在IEEEXplore和PROSPERO中搜索了相关研究，这些研究遵循PRISMA声明开发和评估了性能良好的机器学习算法。根据IJMEDI检查表进行质量评估。详细回顾了已确定的研究，包括预测正确剂量所需的变量，尤其是儿科用药处方。
结果：搜索确定了656项研究，其中64项进行了详细审查，36项符合纳入标准。根据IJMEDI核对表,五项研究被认为是高质量的。36项研究中有19项涉及华法林活性物质。总的来说,基于决策树或回归方法的机器学习算法在预测能力方面优于基于神经网络的算法，支持向量机或其他方法。使用诸如装袋或增强之类的集成方法通常增强了剂量预测的准确性。算法所需的输入和输出变量具有很大的异质性，并且在各个物质之间存在很大差异。
结论：通过使用机器学习算法，可以简化处方过程，提高剂量的正确性。尽管不同物质和病例之间的结果不均匀，并且缺乏儿科用例，确定的方法和所需的变量可以作为进一步开发预测药物剂量的算法的极好起点，特别是对于儿童。特别是基于生理学的药代动力学模型与机器学习算法的组合代表了增强所开发算法的预测能力和准确性的绝佳机会。
方法：PubMed，在IEEEXplore和PROSPERO中搜索了相关研究，这些研究遵循PRISMA声明开发和评估了性能良好的机器学习算法。根据IJMEDI检查表进行质量评估。详细回顾了已确定的研究，包括预测正确剂量所需的变量，尤其是儿科用药处方。
结果：搜索确定了656项研究，其中64项进行了详细审查，36项符合纳入标准。根据IJMEDI核对表,五项研究被认为是高质量的。36项研究中有19项涉及华法林活性物质。总的来说,基于决策树或回归方法的机器学习算法在预测能力方面优于基于神经网络的算法，支持向量机或其他方法。使用诸如装袋或增强之类的集成方法通常增强了剂量预测的准确性。算法所需的输入和输出变量具有很大的异质性，并且在各个物质之间存在很大差异。
结论：通过使用机器学习算法，可以简化处方过程，提高剂量的正确性。尽管不同物质和病例之间的结果不均匀，并且缺乏儿科用例，确定的方法和所需的变量可以作为进一步开发预测药物剂量的算法的极好起点，特别是对于儿童。特别是基于生理学的药代动力学模型与机器学习算法的组合代表了增强所开发算法的预测能力和准确性的绝佳机会。
