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Abstract：
BACKGROUND: This is the first scoping review to focus broadly on the topics of machine learning and medication adherence.
OBJECTIVE: This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence.
METHODS: PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.
RESULTS: Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 monitoring systems, 12 determined medication administration using medication container sensors or sensors in consumer electronics, like smartwatches or smartphones. A total of 11 monitoring systems used logistic regression, artificial neural networks, support vector machines, or random forest algorithms to determine medication administration. The 4 systems that monitored inhaler administration reported a classification accuracy of 93.75% or higher. The 2 systems that monitored medication status in patients with Parkinson disease reported a classification accuracy of 78% or higher. A total of 3 studies monitored medication administration using only smartwatch sensors and reported a classification accuracy of 78.6% or higher. Two systems that provided context-aware medication reminders helped patients to achieve an adherence level of 92% or higher. Two conversational artificial intelligence reminder systems significantly improved adherence rates when compared against traditional reminder systems.
CONCLUSIONS: Creation of systems that accurately predict medication adherence across multiple data sets may be possible due to predictors remaining strong across multiple studies. Higher quality measures of adherence should be adopted when possible so that prediction algorithms are based on accurate information. Currently, medication adherence can be predicted with a good level of accuracy, potentially allowing for the development of interventions aimed at preventing nonadherence. Monitoring systems that track inhaler use currently classify inhaler-related actions with an excellent level of accuracy, allowing for tracking of adherence and potentially proper inhaler technique. Systems that monitor medication states in patients with Parkinson disease can currently achieve a good level of classification accuracy and have the potential to inform medication therapy changes in the future. Medication administration monitoring systems that only use motion sensors in smartwatches can currently achieve a good level of classification accuracy but only when differentiating between a small number of possible activities. Context-aware reminder systems can help patients achieve high levels of medication adherence but are also intrusive, which may not be acceptable to users. Conversational artificial intelligence reminder systems can significantly improve adherence.
OBJECTIVE: This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence.
METHODS: PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.
RESULTS: Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 monitoring systems, 12 determined medication administration using medication container sensors or sensors in consumer electronics, like smartwatches or smartphones. A total of 11 monitoring systems used logistic regression, artificial neural networks, support vector machines, or random forest algorithms to determine medication administration. The 4 systems that monitored inhaler administration reported a classification accuracy of 93.75% or higher. The 2 systems that monitored medication status in patients with Parkinson disease reported a classification accuracy of 78% or higher. A total of 3 studies monitored medication administration using only smartwatch sensors and reported a classification accuracy of 78.6% or higher. Two systems that provided context-aware medication reminders helped patients to achieve an adherence level of 92% or higher. Two conversational artificial intelligence reminder systems significantly improved adherence rates when compared against traditional reminder systems.
CONCLUSIONS: Creation of systems that accurately predict medication adherence across multiple data sets may be possible due to predictors remaining strong across multiple studies. Higher quality measures of adherence should be adopted when possible so that prediction algorithms are based on accurate information. Currently, medication adherence can be predicted with a good level of accuracy, potentially allowing for the development of interventions aimed at preventing nonadherence. Monitoring systems that track inhaler use currently classify inhaler-related actions with an excellent level of accuracy, allowing for tracking of adherence and potentially proper inhaler technique. Systems that monitor medication states in patients with Parkinson disease can currently achieve a good level of classification accuracy and have the potential to inform medication therapy changes in the future. Medication administration monitoring systems that only use motion sensors in smartwatches can currently achieve a good level of classification accuracy but only when differentiating between a small number of possible activities. Context-aware reminder systems can help patients achieve high levels of medication adherence but are also intrusive, which may not be acceptable to users. Conversational artificial intelligence reminder systems can significantly improve adherence.
摘要：
背景：这是第一个广泛关注机器学习和药物依从性主题的范围审查。
目的：这篇综述旨在对，总结，并分析了有关使用机器学习进行与药物依从性相关的操作的文献。
方法：PubMed，Scopus,ACM数字图书馆,IEEE,和WebofScience进行了搜索，以找到符合入选标准的作品。经过全文审查,最终分析包括43件作品。在列入最后草案之前，系统地绘制了感兴趣的信息。根据与药物依从性相关的行动的组合，将研究分为自然类别以进行其他分析。此范围审查的方案是使用PRISMA-ScR（系统审查的首选报告项目和范围审查的荟萃分析扩展）指南创建的。
结果：专注于预测药物依从性的出版物揭示了在两项或多项研究中具有重要意义的20个强有力的预测因子。共有13项预测药物依从性的研究使用自我报告问卷或药房索赔数据来确定药物依从性状态。此外,13项预测药物依从性的研究使用了两种逻辑回归，人工神经网络,随机森林,或支持向量机。在15项预测药物依从性的研究中，6个报告的预测精度，最低的是77.6%。在13个监测系统中，12使用药物容器传感器或消费电子产品中的传感器确定药物施用，比如智能手表或智能手机。共有11个监测系统使用逻辑回归，人工神经网络,支持向量机，或随机森林算法来确定药物管理。监测吸入器给药的4个系统报告的分类准确度为93.75%或更高。监测帕金森病患者药物状态的2个系统报告的分类准确率为78%或更高。共有3项研究仅使用智能手表传感器监测药物管理，并报告分类准确率为78.6%或更高。提供情境感知药物提醒的两个系统帮助患者达到92%或更高的依从性水平。与传统提醒系统相比，两个会话人工智能提醒系统显着提高了依从率。
结论：由于在多项研究中预测因素仍然很强，因此可以创建跨多个数据集准确预测药物依从性的系统。在可能的情况下，应采用更高质量的依从性措施，以便预测算法基于准确的信息。目前,药物依从性可以预测具有良好的准确性，可能允许开发旨在防止不依从性的干预措施。跟踪吸入器使用的监测系统目前以极好的准确度对吸入器相关行为进行分类。允许跟踪依从性和潜在适当的吸入器技术。监测帕金森病患者的药物状态的系统目前可以达到良好的分类准确性水平，并有可能在未来告知药物治疗的变化。仅在智能手表中使用运动传感器的药物管理监测系统目前可以实现良好的分类精度水平，但只有在区分少量可能的活动时。情境感知提醒系统可以帮助患者实现高水平的药物依从性，但也具有侵入性，这可能是用户无法接受的。对话式人工智能提醒系统可以显著提高依从性。
目的：这篇综述旨在对，总结，并分析了有关使用机器学习进行与药物依从性相关的操作的文献。
方法：PubMed，Scopus,ACM数字图书馆,IEEE,和WebofScience进行了搜索，以找到符合入选标准的作品。经过全文审查,最终分析包括43件作品。在列入最后草案之前，系统地绘制了感兴趣的信息。根据与药物依从性相关的行动的组合，将研究分为自然类别以进行其他分析。此范围审查的方案是使用PRISMA-ScR（系统审查的首选报告项目和范围审查的荟萃分析扩展）指南创建的。
结果：专注于预测药物依从性的出版物揭示了在两项或多项研究中具有重要意义的20个强有力的预测因子。共有13项预测药物依从性的研究使用自我报告问卷或药房索赔数据来确定药物依从性状态。此外,13项预测药物依从性的研究使用了两种逻辑回归，人工神经网络,随机森林,或支持向量机。在15项预测药物依从性的研究中，6个报告的预测精度，最低的是77.6%。在13个监测系统中，12使用药物容器传感器或消费电子产品中的传感器确定药物施用，比如智能手表或智能手机。共有11个监测系统使用逻辑回归，人工神经网络,支持向量机，或随机森林算法来确定药物管理。监测吸入器给药的4个系统报告的分类准确度为93.75%或更高。监测帕金森病患者药物状态的2个系统报告的分类准确率为78%或更高。共有3项研究仅使用智能手表传感器监测药物管理，并报告分类准确率为78.6%或更高。提供情境感知药物提醒的两个系统帮助患者达到92%或更高的依从性水平。与传统提醒系统相比，两个会话人工智能提醒系统显着提高了依从率。
结论：由于在多项研究中预测因素仍然很强，因此可以创建跨多个数据集准确预测药物依从性的系统。在可能的情况下，应采用更高质量的依从性措施，以便预测算法基于准确的信息。目前,药物依从性可以预测具有良好的准确性，可能允许开发旨在防止不依从性的干预措施。跟踪吸入器使用的监测系统目前以极好的准确度对吸入器相关行为进行分类。允许跟踪依从性和潜在适当的吸入器技术。监测帕金森病患者的药物状态的系统目前可以达到良好的分类准确性水平，并有可能在未来告知药物治疗的变化。仅在智能手表中使用运动传感器的药物管理监测系统目前可以实现良好的分类精度水平，但只有在区分少量可能的活动时。情境感知提醒系统可以帮助患者实现高水平的药物依从性，但也具有侵入性，这可能是用户无法接受的。对话式人工智能提醒系统可以显著提高依从性。
