PDF(Pubmed)
Abstract：
UNASSIGNED: This study aimed to examine the performance of machine learning models in predicting the progression of knee pain, functional decline, and incidence of knee osteoarthritis (OA) in high-risk individuals, with automated machine learning (AutoML) being used to automate the prediction process.
UNASSIGNED: There were four stages in the process of our AutoML-integrated prediction. Stage 1-Data preparation: The data of 3200 eligible individuals in the Osteoarthritis Initiative (OAI) study who were considered at high risk of knee OA at the baseline visit were extracted and used. Specifically, 1094 variables from the OAI study were used to predict the changes in knee pain, physical function, and incidence of knee OA (i.e. the first occurrence of frequent knee symptoms and definite tibial osteophytes (Kellgren and Lawrence grade ≥2)) over a 9-year period. Stage 2-Model training: The AutoML approach was used to automatically train nine widely used machine learning (ML) models. Stage 3-Model testing: The AutoML approach was used to automatically test the performance of the ML models. Stage 4-Selection of important input variables: The AutoML approach automated the process of computing the importance scores of all input variables and identifying the most important ones, using the technique of permutation feature importance.
UNASSIGNED: Using the AutoML approach, the weighted ensemble model and the CatBoost model showed the best performance among all nine ML models. For the prediction of each outcome in each year, the five most important input variables were identified, most of which were obtained from self-reported questionnaire surveys and radiographic imaging reports.
UNASSIGNED: The AutoML approach has shown potential in automating the process of using ML models to predict long-term changes in knee OA-related outcomes. Its use could support the deployment of ML solutions, facilitating the provision of personalized interventions to prevent the deterioration of knee health and incident knee OA.
UNASSIGNED: There were four stages in the process of our AutoML-integrated prediction. Stage 1-Data preparation: The data of 3200 eligible individuals in the Osteoarthritis Initiative (OAI) study who were considered at high risk of knee OA at the baseline visit were extracted and used. Specifically, 1094 variables from the OAI study were used to predict the changes in knee pain, physical function, and incidence of knee OA (i.e. the first occurrence of frequent knee symptoms and definite tibial osteophytes (Kellgren and Lawrence grade ≥2)) over a 9-year period. Stage 2-Model training: The AutoML approach was used to automatically train nine widely used machine learning (ML) models. Stage 3-Model testing: The AutoML approach was used to automatically test the performance of the ML models. Stage 4-Selection of important input variables: The AutoML approach automated the process of computing the importance scores of all input variables and identifying the most important ones, using the technique of permutation feature importance.
UNASSIGNED: Using the AutoML approach, the weighted ensemble model and the CatBoost model showed the best performance among all nine ML models. For the prediction of each outcome in each year, the five most important input variables were identified, most of which were obtained from self-reported questionnaire surveys and radiographic imaging reports.
UNASSIGNED: The AutoML approach has shown potential in automating the process of using ML models to predict long-term changes in knee OA-related outcomes. Its use could support the deployment of ML solutions, facilitating the provision of personalized interventions to prevent the deterioration of knee health and incident knee OA.
摘要：
■这项研究旨在检查机器学习模型在预测膝关节疼痛进展方面的表现，功能衰退，和高风险个体的膝骨关节炎（OA）的发病率，自动机器学习(AutoML)用于自动化预测过程。
■我们的AutoML集成预测过程分为四个阶段。第1阶段-数据准备:提取并使用骨关节炎倡议(OAI)研究中的3200名合格个体的数据,这些个体在基线就诊时被认为是膝OA的高风险。具体来说,来自OAI研究的1094个变量用于预测膝关节疼痛的变化，物理功能，和膝关节OA的发生率（即在9年期间首次出现频繁的膝关节症状和明确的胫骨骨赘（Kellgren和Lawrence≥2级））。阶段2-模型训练：AutoML方法用于自动训练九种广泛使用的机器学习（ML）模型。第3阶段-模型测试：AutoML方法用于自动测试ML模型的性能。阶段4-重要输入变量的选择：AutoML方法自动计算所有输入变量的重要性得分并识别最重要的变量，使用置换特征重要性的技术。
■使用AutoML方法，加权集成模型和CatBoost模型在所有9个ML模型中表现最佳。对于每年每个结果的预测，确定了五个最重要的输入变量，其中大部分来自自我报告的问卷调查和影像学报告.
■AutoML方法在自动化使用ML模型预测膝关节OA相关结果的长期变化的过程中显示出潜力。它的使用可以支持ML解决方案的部署，促进提供个性化干预措施，以防止膝关节健康恶化和膝关节OA的发生。
■我们的AutoML集成预测过程分为四个阶段。第1阶段-数据准备:提取并使用骨关节炎倡议(OAI)研究中的3200名合格个体的数据,这些个体在基线就诊时被认为是膝OA的高风险。具体来说,来自OAI研究的1094个变量用于预测膝关节疼痛的变化，物理功能，和膝关节OA的发生率（即在9年期间首次出现频繁的膝关节症状和明确的胫骨骨赘（Kellgren和Lawrence≥2级））。阶段2-模型训练：AutoML方法用于自动训练九种广泛使用的机器学习（ML）模型。第3阶段-模型测试：AutoML方法用于自动测试ML模型的性能。阶段4-重要输入变量的选择：AutoML方法自动计算所有输入变量的重要性得分并识别最重要的变量，使用置换特征重要性的技术。
■使用AutoML方法，加权集成模型和CatBoost模型在所有9个ML模型中表现最佳。对于每年每个结果的预测，确定了五个最重要的输入变量，其中大部分来自自我报告的问卷调查和影像学报告.
■AutoML方法在自动化使用ML模型预测膝关节OA相关结果的长期变化的过程中显示出潜力。它的使用可以支持ML解决方案的部署，促进提供个性化干预措施，以防止膝关节健康恶化和膝关节OA的发生。
