PDF(Pubmed)
Abstract：
Hypokalaemia is a side-effect of diuretics. We aimed to use machine learning to identify features predicting hypokalaemia risk in hypertensive patients.
Participants with hypertension in the United States National Health and Nutrition Examination Survey 1999-2018 were included for analysis. To select the most suitable algorithm, we tested and evaluated five machine learning algorithms commonly employed in epidemiological studies: Logistic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and Regression Trees, and eXtreme Gradient Boosting. These algorithms were accessed using a set of 38 screened features. We then selected the key hypokalaemia-associated features in the hypertension group and their cardiovascular diseases (CVD) subgroup using the SHapley Additive exPlanations (SHAP) values. Using SHAP values, the key features and their impact pattern on hypokalaemia risk were determined.
A total of 25,326 hypertensive participants were included for analysis, of whom 4,511 had known CVD. The Random Forest algorithm had the highest AUROC (hypertension dataset: 0.73 [95%CI, 0.71-0.76]; CVD subgroup: 0.72 [95%CI, 0.66-0.78]). Moreover, the nomogram based on the top twelve key features screened by random forest retained good performance: age, sex, race, poverty income ratio, body mass index, systolic and diastolic blood pressure, non-potassium-sparing diuretics use and duration, renin-angiotensin blockers use and duration, and CVD history in hypertension dataset; while in CVD subgroup, the additional key features were comorbid diabetes, education level, smoking status, and use of bronchodilators.
Our predictive model based on the random forest algorithm performed best among the tested and evaluated five algorithms. Hypokalaemia-associated key features have been identified in hypertensive patients and the subgroup with CVD. These findings from machine learning facilitate the development of artificial intelligence to highlight hypokalaemia risk in hypertension patients.
Our predictive model based on the random forest algorithm performed best among the tested and evaluated five algorithms, and hypokalemia-associated key features have been identified in hypertensive patients and the subgroup with cardiovascular disease.The nomogram we developed including twelve key features might be useful and applied in primary clinical consultations to identify the hypertensive patients at risk of hypokalaemia.These findings from machine learning facilitate the development of artificial intelligence to highlight hypokalaemia risk in hypertension patients.
Participants with hypertension in the United States National Health and Nutrition Examination Survey 1999-2018 were included for analysis. To select the most suitable algorithm, we tested and evaluated five machine learning algorithms commonly employed in epidemiological studies: Logistic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and Regression Trees, and eXtreme Gradient Boosting. These algorithms were accessed using a set of 38 screened features. We then selected the key hypokalaemia-associated features in the hypertension group and their cardiovascular diseases (CVD) subgroup using the SHapley Additive exPlanations (SHAP) values. Using SHAP values, the key features and their impact pattern on hypokalaemia risk were determined.
A total of 25,326 hypertensive participants were included for analysis, of whom 4,511 had known CVD. The Random Forest algorithm had the highest AUROC (hypertension dataset: 0.73 [95%CI, 0.71-0.76]; CVD subgroup: 0.72 [95%CI, 0.66-0.78]). Moreover, the nomogram based on the top twelve key features screened by random forest retained good performance: age, sex, race, poverty income ratio, body mass index, systolic and diastolic blood pressure, non-potassium-sparing diuretics use and duration, renin-angiotensin blockers use and duration, and CVD history in hypertension dataset; while in CVD subgroup, the additional key features were comorbid diabetes, education level, smoking status, and use of bronchodilators.
Our predictive model based on the random forest algorithm performed best among the tested and evaluated five algorithms. Hypokalaemia-associated key features have been identified in hypertensive patients and the subgroup with CVD. These findings from machine learning facilitate the development of artificial intelligence to highlight hypokalaemia risk in hypertension patients.
Our predictive model based on the random forest algorithm performed best among the tested and evaluated five algorithms, and hypokalemia-associated key features have been identified in hypertensive patients and the subgroup with cardiovascular disease.The nomogram we developed including twelve key features might be useful and applied in primary clinical consultations to identify the hypertensive patients at risk of hypokalaemia.These findings from machine learning facilitate the development of artificial intelligence to highlight hypokalaemia risk in hypertension patients.
摘要：
■低钾血症是利尿剂的副作用。我们旨在使用机器学习来识别预测高血压患者低钾血症风险的特征。
■1999-2018年美国国家健康和营养检查调查中的高血压参与者被纳入分析。要选择最合适的算法，我们测试和评估了流行病学研究中常用的五种机器学习算法：Logistic回归，k-最近邻居,随机森林,递归分区和回归树，和极限梯度提升。使用一组38个筛选的特征来访问这些算法。然后，我们使用SHapley加法扩张（SHAP）值选择了高血压组及其心血管疾病（CVD）亚组中与低钾血症相关的关键特征。使用SHAP值，我们确定了主要特征及其对低钾血症风险的影响模式.
■总共25,326名高血压参与者被纳入分析，其中4,511人已知CVD。随机森林算法的AUROC最高（高血压数据集：0.73[95CI,0.71-0.76]；CVD亚组：0.72[95CI,0.66-0.78]）。此外，基于随机森林筛选的前12个关键特征的列线图保留了良好的性能：年龄，性别,种族,贫困收入比，身体质量指数,收缩压和舒张压,非保钾利尿剂的使用和持续时间，肾素-血管紧张素阻滞剂的使用和持续时间，和高血压数据集中的CVD病史；而在CVD亚组中，额外的关键特征是合并糖尿病，教育水平,吸烟状况,使用支气管扩张剂。
■我们基于随机森林算法的预测模型在经过测试和评估的五种算法中表现最好。已在高血压患者和CVD亚组中确定了低钾血症相关的关键特征。机器学习的这些发现促进了人工智能的发展，以突出高血压患者的低钾血症风险。
我们基于随机森林算法的预测模型在经过测试和评估的五种算法中表现最好，在高血压患者和心血管疾病亚组中已经确定了低钾血症相关的关键特征。我们开发的包括十二个关键特征的列线图可能有用，并应用于初级临床咨询中，以识别有低钾血症风险的高血压患者。机器学习的这些发现促进了人工智能的发展，以突出高血压患者的低钾血症风险。
■1999-2018年美国国家健康和营养检查调查中的高血压参与者被纳入分析。要选择最合适的算法，我们测试和评估了流行病学研究中常用的五种机器学习算法：Logistic回归，k-最近邻居,随机森林,递归分区和回归树，和极限梯度提升。使用一组38个筛选的特征来访问这些算法。然后，我们使用SHapley加法扩张（SHAP）值选择了高血压组及其心血管疾病（CVD）亚组中与低钾血症相关的关键特征。使用SHAP值，我们确定了主要特征及其对低钾血症风险的影响模式.
■总共25,326名高血压参与者被纳入分析，其中4,511人已知CVD。随机森林算法的AUROC最高（高血压数据集：0.73[95CI,0.71-0.76]；CVD亚组：0.72[95CI,0.66-0.78]）。此外，基于随机森林筛选的前12个关键特征的列线图保留了良好的性能：年龄，性别,种族,贫困收入比，身体质量指数,收缩压和舒张压,非保钾利尿剂的使用和持续时间，肾素-血管紧张素阻滞剂的使用和持续时间，和高血压数据集中的CVD病史；而在CVD亚组中，额外的关键特征是合并糖尿病，教育水平,吸烟状况,使用支气管扩张剂。
■我们基于随机森林算法的预测模型在经过测试和评估的五种算法中表现最好。已在高血压患者和CVD亚组中确定了低钾血症相关的关键特征。机器学习的这些发现促进了人工智能的发展，以突出高血压患者的低钾血症风险。
我们基于随机森林算法的预测模型在经过测试和评估的五种算法中表现最好，在高血压患者和心血管疾病亚组中已经确定了低钾血症相关的关键特征。我们开发的包括十二个关键特征的列线图可能有用，并应用于初级临床咨询中，以识别有低钾血症风险的高血压患者。机器学习的这些发现促进了人工智能的发展，以突出高血压患者的低钾血症风险。
