PDF(Pubmed)
Abstract：
Computerized adaptive testing (CAT) has been shown to deliver short, accurate, and personalized versions of the CLEFT-Q patient-reported outcome measure for children and young adults born with a cleft lip and/or palate. Decision trees may integrate clinician-reported data (eg, age, gender, cleft type, and planned treatments) to make these assessments even shorter and more accurate.
We aimed to create decision tree models incorporating clinician-reported data into adaptive CLEFT-Q assessments and compare their accuracy to traditional CAT models.
We used relevant clinician-reported data and patient-reported item responses from the CLEFT-Q field test to train and test decision tree models using recursive partitioning. We compared the prediction accuracy of decision trees to CAT assessments of similar length. Participant scores from the full-length questionnaire were used as ground truth. Accuracy was assessed through Pearson\'s correlation coefficient of predicted and ground truth scores, mean absolute error, root mean squared error, and a two-tailed Wilcoxon signed-rank test comparing squared error.
Decision trees demonstrated poorer accuracy than CAT comparators and generally made data splits based on item responses rather than clinician-reported data.
When predicting CLEFT-Q scores, individual item responses are generally more informative than clinician-reported data. Decision trees that make binary splits are at risk of underfitting polytomous patient-reported outcome measure data and demonstrated poorer performance than CATs in this study.
We aimed to create decision tree models incorporating clinician-reported data into adaptive CLEFT-Q assessments and compare their accuracy to traditional CAT models.
We used relevant clinician-reported data and patient-reported item responses from the CLEFT-Q field test to train and test decision tree models using recursive partitioning. We compared the prediction accuracy of decision trees to CAT assessments of similar length. Participant scores from the full-length questionnaire were used as ground truth. Accuracy was assessed through Pearson\'s correlation coefficient of predicted and ground truth scores, mean absolute error, root mean squared error, and a two-tailed Wilcoxon signed-rank test comparing squared error.
Decision trees demonstrated poorer accuracy than CAT comparators and generally made data splits based on item responses rather than clinician-reported data.
When predicting CLEFT-Q scores, individual item responses are generally more informative than clinician-reported data. Decision trees that make binary splits are at risk of underfitting polytomous patient-reported outcome measure data and demonstrated poorer performance than CATs in this study.
摘要：
计算机自适应测试(CAT)已被证明能够提供短期、准确,和个性化版本的CLEFT-Q患者报告的儿童和年轻成人唇裂和/或腭裂出生。决策树可以整合临床医生报告的数据(例如，年龄,性别,裂隙类型,和计划的治疗)，使这些评估更短、更准确。
我们旨在创建决策树模型，将临床医生报告的数据纳入自适应CLEFT-Q评估，并将其准确性与传统CAT模型进行比较。
我们使用来自CLEFT-Q现场测试的相关临床医生报告的数据和患者报告的项目响应，使用递归划分来训练和测试决策树模型。我们将决策树的预测精度与相似长度的CAT评估进行了比较。来自全长问卷的参与者得分被用作基本事实。通过Pearson预测和地面真实分数的相关系数评估准确性，平均绝对误差，均方根误差，和双尾Wilcoxon符号秩检验，比较平方误差。
决策树显示出比CAT比较器低的准确性，并且通常根据项目响应而不是临床医生报告的数据进行数据分割。
在预测CLEFT-Q分数时，单个项目的响应通常比临床医生报告的数据更有信息。在这项研究中，进行二元分割的决策树有可能无法满足患者报告的结果测量数据，并且表现出比CAT更差的表现。
我们旨在创建决策树模型，将临床医生报告的数据纳入自适应CLEFT-Q评估，并将其准确性与传统CAT模型进行比较。
我们使用来自CLEFT-Q现场测试的相关临床医生报告的数据和患者报告的项目响应，使用递归划分来训练和测试决策树模型。我们将决策树的预测精度与相似长度的CAT评估进行了比较。来自全长问卷的参与者得分被用作基本事实。通过Pearson预测和地面真实分数的相关系数评估准确性，平均绝对误差，均方根误差，和双尾Wilcoxon符号秩检验，比较平方误差。
决策树显示出比CAT比较器低的准确性，并且通常根据项目响应而不是临床医生报告的数据进行数据分割。
在预测CLEFT-Q分数时，单个项目的响应通常比临床医生报告的数据更有信息。在这项研究中，进行二元分割的决策树有可能无法满足患者报告的结果测量数据，并且表现出比CAT更差的表现。
