The Insight Centre for Data Analytics, Dublin City University, Dublin 9, Ireland.
HealthBeacon Ltd, Dublin, Ireland.
Sci Rep. 2021 Sep 23;11(1):18961. doi: 10.1038/s41598-021-98387-w.
Clinical studies from WHO have demonstrated that only 50-70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390).
世界卫生组织的临床研究表明，只有 50-70%的患者能够正确遵循规定的药物治疗方案。这种用药依从性失败可能会影响患者的治疗效果，并影响针对目标适应症的药物在人群水平上的疗效数据质量。在这项研究中，我们应用了各种集成学习和深度学习模型来预测患者的用药依从性。我们的贡献在于针对在家中自行注射药物的一类特别具有挑战性的患者，解决用药依从性预测的问题。我们的预测管道基于事件历史，包含一个连接的锐器箱，旨在帮助患者更好地管理病情并改善结果。换句话说，通过优先考虑最有可能不遵守治疗方案的患者，可以显著提高干预措施的效率。所收集的数据包含丰富的事件特征集，可用于预测个别患者下一个用药依从状态的情况。本文报告了如何通过使用广泛的集成学习和深度学习模型在来自此类系统的真实数据集上进行调查来实现这一概念。所研究的数据集包含超过 5 年期间收集的 342,174 份历史注射处置记录。本文对不同模型进行了全面比较。此外，我们通过将选定的最佳表现者——长短时记忆网络（LSTM）部署在真实的未来测试数据集中，证明了它具有很好的泛化能力。该端到端管道能够以 77.35%的准确率预测患者在坚持治疗方案方面的失败（特异性：78.28%，敏感性：76.42%，精度：77.87%，F1 得分：0.7714，ROC AUC：0.8390）。
