Bohlmann Aaron, Mostafa Javed, Kumar Manish
Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Public Health Leadership Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
JMIRx Med. 2021 Nov 24;2(4):e26993. doi: 10.2196/26993.
This is the first scoping review to focus broadly on the topics of machine learning and medication adherence.
This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence.
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.
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.
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.
这是首次广泛聚焦机器学习与药物依从性主题的范围综述。
本综述旨在对专注于使用机器学习进行与药物依从性相关行动的文献进行分类、总结和分析。
检索了PubMed、Scopus、ACM数字图书馆、IEEE和科学网，以查找符合纳入标准的研究。经过全文评审，最终分析纳入了43项研究。在纳入最终草案之前，对相关信息进行了系统梳理。根据与药物依从性相关行动的组合，将研究归入自然类别进行进一步分析。本范围综述的方案是根据PRISMA - ScR（系统评价和Meta分析扩展版的首选报告项目）指南制定的。
专注于预测药物依从性的出版物发现了20个强有力的预测因素，这些因素在两项或更多研究中具有显著性。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报销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逻辑回归、人工神经网络、随机森林或支持向量机进行预测。在15项预测药物依从性的研究中，6项报告了预测因素的准确性，其中最低为77.6%。在13个监测系统中，12个使用药物容器传感器或消费电子产品（如智能手表或智能手机）中的传感器来确定用药情况。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用药情况。4个监测吸入器使用的系统报告的分类准确率为93.75%或更高。2个监测帕金森病患者药物状态的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用药情况，报告的分类准确率为78.6%或更高。2个提供情境感知药物提醒的系统帮助患者实现了92%或更高的依从水平。与传统提醒系统相比，2个对话式人工智能提醒系统显著提高了依从率。
由于预测因素在多项研究中保持强劲，创建能够跨多个数据集准确预测药物依从性的系统可能是可行的。应尽可能采用更高质量的依从性测量方法，以便预测算法基于准确信息。目前，药物依从性能够以较高的准确率进行预测，这可能有助于开发旨在预防不依从的干预措施。目前，跟踪吸入器使用的监测系统对与吸入器相关行动的分类准确率极高，能够跟踪依从性并可能规范吸入器使用技术。目前，监测帕金森病患者药物状态的系统能够达到较好的分类准确率，并有可能为未来的药物治疗调整提供依据。仅使用智能手表中的运动传感器的用药监测系统目前能够达到较好的分类准确率，但仅在区分少数可能活动时有效。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，这可能不为用户所接受。对话式人工智能提醒系统可以显著提高依从性。
