Hu Qiaozhi, Tian Fangyuan, Jin Zhaohui, Lin Gongchao, Teng Fei, Xu Ting
Department of Pharmacy, West China Hospital, Sichuan University, Chengdu 610041, China.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.
J Clin Med. 2023 Mar 30;12(7):2619. doi: 10.3390/jcm12072619.
Due to multiple comorbid illnesses, polypharmacy, and age-related changes in pharmacokinetics and pharmacodynamics in older adults, the prevalence of potentially inappropriate medications (PIMs) is high, which affects the quality of life of older adults. Building an effective warning model is necessary for the early identification of PIMs to prevent harm caused by medication in geriatric patients. The purpose of this study was to develop a machine learning-based model for the warning of PIMs in older Chinese outpatients. This retrospective study was conducted among geriatric outpatients in nine tertiary hospitals in Chengdu from January 2018 to December 2018. The Beers criteria 2019 were used to assess PIMs in geriatric outpatients. Three problem transformation methods were used to tackle the multilabel classification problem in prescriptions. After the division of patient prescriptions into the training and test sets (8:2), we adopted six widely used classification algorithms to conduct the classification task and assessed the discriminative performance by the accuracy, precision, recall, F1 scores, subset accuracy (ss Acc), and Hamming loss (hm) of each model. The results showed that among 11,741 older patient prescriptions, 5816 PIMs were identified in 4038 (34.39%) patient prescriptions. A total of 41 types of PIMs were identified in these prescriptions. The three-problem transformation methods included label power set (LP), classifier chains (CC), and binary relevance (BR). Six classification algorithms were used to establish the warning models, including Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), CatBoost, Deep Forest (DF), and TabNet. The CC + CatBoost model had the highest accuracy value (97.83%), recall value (89.34%), F1 value (90.69%), and ss Acc value (97.79%) with a good precision value (92.18%) and the lowest hm value (0.0006). Therefore, the CC + CatBoost model was selected to predict the occurrence of PIM in geriatric Chinese patients. This study's novelty establishes a warning model for PIMs in geriatric patients by using machine learning. With the popularity of electronic patient record systems, sophisticated computer algorithms can be implemented at the bedside to improve medication use safety in geriatric patients in the future.
由于老年人存在多种合并症、用药种类多以及药代动力学和药效学方面与年龄相关的变化，潜在不适当用药（PIMs）的发生率很高，这影响了老年人的生活质量。建立有效的预警模型对于早期识别PIMs以预防老年患者用药造成的危害是必要的。本研究的目的是开发一种基于机器学习的模型，用于对中国老年门诊患者的PIMs进行预警。这项回顾性研究于2018年1月至2018年12月在成都九家三级医院的老年门诊患者中进行。采用2019年版Beers标准评估老年门诊患者的PIMs。使用三种问题转换方法来处理处方中的多标签分类问题。在将患者处方划分为训练集和测试集（8:2）后，我们采用六种广泛使用的分类算法进行分类任务，并通过每个模型的准确率、精确率、召回率、F1分数、子集准确率（ss Acc）和汉明损失（hm）来评估判别性能。结果显示，在11741份老年患者处方中，4038份（34.39%）患者处方中识别出5816例PIMs。这些处方中共识别出41种PIMs。三种问题转换方法包括标签幂集（LP）、分类器链（CC）和二元相关性（BR）。使用六种分类算法建立预警模型，包括随机森林（RF）、轻量级梯度提升机（LightGBM）、极端梯度提升（XGBoost）、CatBoost、深度森林（DF）和TabNet。CC + CatBoost模型具有最高的准确率值（97.83%）、召回率值（89.34%）、F1值（90.69%）和ss Acc值（97.79%），精确率值良好（92.18%），hm值最低（0.0006）。因此，选择CC + CatBoost模型来预测中国老年患者PIMs的发生。本研究的创新之处在于利用机器学习建立了老年患者PIMs的预警模型。随着电子病历系统的普及，未来可以在床边实施复杂的计算机算法，以提高老年患者的用药安全。
