Abstract：
Machine learning offers new solutions for predicting life-threatening, unpredictable amiodarone-induced thyroid dysfunction. Traditional regression approaches for adverse-effect prediction without time-series consideration of features have yielded suboptimal predictions. Machine learning algorithms with multiple data sets at different time points may generate better performance in predicting adverse effects.
We aimed to develop and validate machine learning models for forecasting individualized amiodarone-induced thyroid dysfunction risk and to optimize a machine learning-based risk stratification scheme with a resampling method and readjustment of the clinically derived decision thresholds.
This study developed machine learning models using multicenter, delinked electronic health records. It included patients receiving amiodarone from January 2013 to December 2017. The training set was composed of data from Taipei Medical University Hospital and Wan Fang Hospital, while data from Taipei Medical University Shuang Ho Hospital were used as the external test set. The study collected stationary features at baseline and dynamic features at the first, second, third, sixth, ninth, 12th, 15th, 18th, and 21st months after amiodarone initiation. We used 16 machine learning models, including extreme gradient boosting, adaptive boosting, k-nearest neighbor, and logistic regression models, along with an original resampling method and 3 other resampling methods, including oversampling with the borderline-synthesized minority oversampling technique, undersampling-edited nearest neighbor, and over- and undersampling hybrid methods. The model performance was compared based on accuracy; Precision, recall, F1-score, geometric mean, area under the curve of the receiver operating characteristic curve (AUROC), and the area under the precision-recall curve (AUPRC). Feature importance was determined by the best model. The decision threshold was readjusted to identify the best cutoff value and a Kaplan-Meier survival analysis was performed.
The training set contained 4075 patients from Taipei Medical University Hospital and Wan Fang Hospital, of whom 583 (14.3%) developed amiodarone-induced thyroid dysfunction, while the external test set included 2422 patients from Taipei Medical University Shuang Ho Hospital, of whom 275 (11.4%) developed amiodarone-induced thyroid dysfunction. The extreme gradient boosting oversampling machine learning model demonstrated the best predictive outcomes among all 16 models. The accuracy; Precision, recall, F1-score, G-mean, AUPRC, and AUROC were 0.923, 0.632, 0.756, 0.688, 0.845, 0.751, and 0.934, respectively. After readjusting the cutoff, the best value was 0.627, and the F1-score reached 0.699. The best threshold was able to classify 286 of 2422 patients (11.8%) as high-risk subjects, among which 275 were true-positive patients in the testing set. A shorter treatment duration; higher levels of thyroid-stimulating hormone and high-density lipoprotein cholesterol; and lower levels of free thyroxin, alkaline phosphatase, and low-density lipoprotein were the most important features.
Machine learning models combined with resampling methods can predict amiodarone-induced thyroid dysfunction and serve as a support tool for individualized risk prediction and clinical decision support.
We aimed to develop and validate machine learning models for forecasting individualized amiodarone-induced thyroid dysfunction risk and to optimize a machine learning-based risk stratification scheme with a resampling method and readjustment of the clinically derived decision thresholds.
This study developed machine learning models using multicenter, delinked electronic health records. It included patients receiving amiodarone from January 2013 to December 2017. The training set was composed of data from Taipei Medical University Hospital and Wan Fang Hospital, while data from Taipei Medical University Shuang Ho Hospital were used as the external test set. The study collected stationary features at baseline and dynamic features at the first, second, third, sixth, ninth, 12th, 15th, 18th, and 21st months after amiodarone initiation. We used 16 machine learning models, including extreme gradient boosting, adaptive boosting, k-nearest neighbor, and logistic regression models, along with an original resampling method and 3 other resampling methods, including oversampling with the borderline-synthesized minority oversampling technique, undersampling-edited nearest neighbor, and over- and undersampling hybrid methods. The model performance was compared based on accuracy; Precision, recall, F1-score, geometric mean, area under the curve of the receiver operating characteristic curve (AUROC), and the area under the precision-recall curve (AUPRC). Feature importance was determined by the best model. The decision threshold was readjusted to identify the best cutoff value and a Kaplan-Meier survival analysis was performed.
The training set contained 4075 patients from Taipei Medical University Hospital and Wan Fang Hospital, of whom 583 (14.3%) developed amiodarone-induced thyroid dysfunction, while the external test set included 2422 patients from Taipei Medical University Shuang Ho Hospital, of whom 275 (11.4%) developed amiodarone-induced thyroid dysfunction. The extreme gradient boosting oversampling machine learning model demonstrated the best predictive outcomes among all 16 models. The accuracy; Precision, recall, F1-score, G-mean, AUPRC, and AUROC were 0.923, 0.632, 0.756, 0.688, 0.845, 0.751, and 0.934, respectively. After readjusting the cutoff, the best value was 0.627, and the F1-score reached 0.699. The best threshold was able to classify 286 of 2422 patients (11.8%) as high-risk subjects, among which 275 were true-positive patients in the testing set. A shorter treatment duration; higher levels of thyroid-stimulating hormone and high-density lipoprotein cholesterol; and lower levels of free thyroxin, alkaline phosphatase, and low-density lipoprotein were the most important features.
Machine learning models combined with resampling methods can predict amiodarone-induced thyroid dysfunction and serve as a support tool for individualized risk prediction and clinical decision support.
摘要：
背景：机器学习为预测威胁生命提供了新的解决方案，不可预知的胺碘酮引起的甲状腺功能障碍。没有时间序列考虑特征的用于不利影响预测的传统回归方法产生了次优预测。在不同时间点具有多个数据集的机器学习算法可以在预测不利影响方面产生更好的性能。
目的：我们旨在开发和验证用于预测个体化胺碘酮诱发的甲状腺功能障碍风险的机器学习模型，并通过重采样方法和重新调整临床得出的决策阈值来优化基于机器学习的风险分层方案。
方法：这项研究使用多中心开发了机器学习模型，删除电子健康记录。包括2013年1月至2017年12月接受胺碘酮治疗的患者。训练集由台北医学大学医院和万芳医院的数据组成,而台北医科大学双河医院的数据被用作外部测试集。该研究首先收集了基线的固定特征和动态特征，第二,第三,第六，第九,12th,15th,18日,胺碘酮启动后21个月。我们使用了16个机器学习模型，包括极端梯度增强，自适应提升，k-最近邻,和逻辑回归模型，以及原始的重采样方法和其他3种重采样方法，包括用边界合成的少数过采样技术进行过采样，欠采样编辑的最近邻，以及过采样和欠采样混合方法。根据精度比较了模型性能；精度，召回，F1分数，几何平均值，接收器工作特性曲线（AUROC）的曲线下面积，以及精确召回率曲线下的面积(AUPRC)。特征重要性由最佳模型确定。重新调整决策阈值以确定最佳临界值，并进行Kaplan-Meier生存分析。
结果:训练集包含台北医学大学医院和万方医院的4075名患者,其中583人（14.3%）出现胺碘酮诱发的甲状腺功能异常，而外部测试装置包括台北医学大学双河医院的2422名患者，其中275人（11.4%）发生胺碘酮诱导的甲状腺功能障碍。极端梯度提升过采样机器学习模型在所有16个模型中表现出最佳的预测结果。准确性；精度，召回，F1分数，G-mean,AUPRC,AUROC分别为0.923、0.632、0.756、0.688、0.845、0.751和0.934。在重新调整截止线后,最佳值为0.627，F1评分达到0.699。最佳阈值能够将2422名患者中的286名（11.8％）归类为高危受试者，其中275例为检测组中的真阳性患者.治疗时间较短；促甲状腺激素和高密度脂蛋白胆固醇水平较高；游离甲状腺素水平较低，碱性磷酸酶,低密度脂蛋白是最重要的特征。
结论：机器学习模型结合重采样方法可以预测胺碘酮引起的甲状腺功能障碍，并可作为个体化风险预测和临床决策支持的支持工具。
目的：我们旨在开发和验证用于预测个体化胺碘酮诱发的甲状腺功能障碍风险的机器学习模型，并通过重采样方法和重新调整临床得出的决策阈值来优化基于机器学习的风险分层方案。
方法：这项研究使用多中心开发了机器学习模型，删除电子健康记录。包括2013年1月至2017年12月接受胺碘酮治疗的患者。训练集由台北医学大学医院和万芳医院的数据组成,而台北医科大学双河医院的数据被用作外部测试集。该研究首先收集了基线的固定特征和动态特征，第二,第三,第六，第九,12th,15th,18日,胺碘酮启动后21个月。我们使用了16个机器学习模型，包括极端梯度增强，自适应提升，k-最近邻,和逻辑回归模型，以及原始的重采样方法和其他3种重采样方法，包括用边界合成的少数过采样技术进行过采样，欠采样编辑的最近邻，以及过采样和欠采样混合方法。根据精度比较了模型性能；精度，召回，F1分数，几何平均值，接收器工作特性曲线（AUROC）的曲线下面积，以及精确召回率曲线下的面积(AUPRC)。特征重要性由最佳模型确定。重新调整决策阈值以确定最佳临界值，并进行Kaplan-Meier生存分析。
结果:训练集包含台北医学大学医院和万方医院的4075名患者,其中583人（14.3%）出现胺碘酮诱发的甲状腺功能异常，而外部测试装置包括台北医学大学双河医院的2422名患者，其中275人（11.4%）发生胺碘酮诱导的甲状腺功能障碍。极端梯度提升过采样机器学习模型在所有16个模型中表现出最佳的预测结果。准确性；精度，召回，F1分数，G-mean,AUPRC,AUROC分别为0.923、0.632、0.756、0.688、0.845、0.751和0.934。在重新调整截止线后,最佳值为0.627，F1评分达到0.699。最佳阈值能够将2422名患者中的286名（11.8％）归类为高危受试者，其中275例为检测组中的真阳性患者.治疗时间较短；促甲状腺激素和高密度脂蛋白胆固醇水平较高；游离甲状腺素水平较低，碱性磷酸酶,低密度脂蛋白是最重要的特征。
结论：机器学习模型结合重采样方法可以预测胺碘酮引起的甲状腺功能障碍，并可作为个体化风险预测和临床决策支持的支持工具。
