Innovation Empowerment and Design Application (IDEA) Lab Center for Information Systems & Technology Claremont Graduate University, Claremont, California, USA.
AMIA Annu Symp Proc. 2024 Jan 11;2023:243-252. eCollection 2023.
Cancer caregivers are often informal family members who may not be prepared to adequately meet the needs of patients and often experience high stress along with significant physical, emotional, and financial burdens. Accurate prediction of caregiver's burden level is highly valuable for early intervention and support. In this study, we used several machine learning approaches to build prediction models from the National Alliance for Caregiving/AARP dataset. We performed data cleansing and imputation on the raw data to give us a working dataset of cancer caregivers. Then a series of feature selection methods were used to identify predictive risk factors for burden level. Using supervised machine learning classifiers, we achieved reasonably good prediction performance (Accuracy ∼ 0.94; AUC ∼ 0.97; F1∼ 0.93). We identify a small set of 15 features that are strong predictors of burden and can be used to build Clinical Decision Support Systems.
癌症护理人员通常是非正式的家庭成员，他们可能没有准备好充分满足患者的需求，并且经常承受高压力以及巨大的身体、情感和经济负担。准确预测护理人员的负担水平对于早期干预和支持非常有价值。在这项研究中，我们使用了几种机器学习方法，从全国照顾者联盟/美国退休人员协会（National Alliance for Caregiving/AARP）数据集构建预测模型。我们对原始数据进行了数据清理和插补，为我们提供了一个可用于癌症护理人员的工作数据集。然后，使用了一系列特征选择方法来确定负担水平的预测风险因素。使用监督机器学习分类器，我们实现了相当不错的预测性能（Accuracy∼0.94；AUC∼0.97；F1∼0.93）。我们确定了一小部分 15 个特征，这些特征是负担的强预测因子，可以用于构建临床决策支持系统。
