Wang Yu, Li Peng-Fei, Tian Yu, Ren Jing-Jing, Li Jing-Song
IEEE J Biomed Health Inform. 2017 Sep;21(5):1280-1287. doi: 10.1109/JBHI.2016.2614991. Epub 2016 Oct 4.
The use of a shared decision-making (SDM) process in antihyperglycemic medication strategy decisions is necessary due to the complexity of the conditions of diabetes patients. Knowledge of guidelines is used as decision aids in clinical situations, and during this process, no patient health conditions are considered. In this paper, we propose an SDM system framework for type-2 diabetes mellitus (T2DM) patients that not only contains knowledge abstracted from guidelines but also employs a multilabel classification model that uses class-imbalanced electronic health record (EHR) data and that aims to provide a recommended list of available antihyperglycemic medications to help physicians and patients have an SDM conversation. The use of EHR data to serve as a decision-support component in decision aids helps physicians and patients to reach a more intuitive understanding of current health conditions and allows the tailoring of the available knowledge to each patient, leading to a more effective SDM. Real-world data from 2542 T2DM inpatient EHRs were substituted by 77 features and eight output labels, i.e., eight antihyperglycemic medications, and these data were utilized to build and validate the recommendation model. The multilabel recommendation model exhibited stable performance in every single-label classification and showed the ability to predict minority positive cases in which the average recall value of the eight classes was 0.9898. As a whole multilabel classifier, the recommendation model demonstrated outstanding performance, with scores of 0.0941 for Hamming Loss, 0.7611 for Accuracy, 0.9664 for Recall, and 0.8269 for F.
由于糖尿病患者病情的复杂性，在抗高血糖药物治疗策略决策中采用共享决策（SDM）过程是必要的。指南知识在临床情况下用作决策辅助工具，而在此过程中，未考虑患者的健康状况。在本文中，我们提出了一种针对2型糖尿病（T2DM）患者的SDM系统框架，该框架不仅包含从指南中提取的知识，还采用了多标签分类模型，该模型使用类别不平衡的电子健康记录（EHR）数据，旨在提供一份可用的抗高血糖药物推荐清单，以帮助医生和患者进行SDM对话。使用EHR数据作为决策辅助工具中的决策支持组件，有助于医生和患者更直观地了解当前健康状况，并允许根据每个患者的情况定制可用知识，从而实现更有效的SDM。来自2542例T2DM住院患者EHR的真实世界数据由77个特征和8个输出标签（即8种抗高血糖药物）替代，这些数据被用于构建和验证推荐模型。多标签推荐模型在每个单标签分类中都表现出稳定的性能，并显示出预测少数阳性病例的能力，其中8个类别的平均召回值为0.9898。作为一个整体的多标签分类器，推荐模型表现出卓越的性能，汉明损失得分为0.0941，准确率为0.7611，召回率为0.9664，F值为0.8269。
