Deparis Stéphane, Pascale Alessandra, Tommasi Pierpaolo, Kotoulas Spyros
IBM Research - Ireland, Dublin, Ireland.
Stud Health Technol Inform. 2018;247:820-824.
This paper describes an application of Bayesian Networks to mo-del persons with multimorbidity using measurements of vital signs and lifestyle assessments. The model was developed as part of a project on the use of wearable and home sensors and tablet applications to help persons with multimorbidity and their carers manage their conditions in daily life. The training data was extracted from TILDA, an open dataset collected from a longitudinal health study of the older Irish population. A categorical BN structure was learnt using a score-based approach, with constraints on the ordering of variables. The prediction accuracy of the model is assessed using the Brier score in a cross-validation experiment. Finally, a user inter-face that allows to set some observed levels and query the resulting margi-nal probabilities from the BN is presented.
本文描述了贝叶斯网络在使用生命体征测量和生活方式评估对患有多种疾病的人进行建模方面的应用。该模型是一个项目的一部分，该项目利用可穿戴设备、家庭传感器和平板应用程序来帮助患有多种疾病的人和他们的护理人员在日常生活中管理病情。训练数据从TILDA中提取，TILDA是一个从爱尔兰老年人群纵向健康研究中收集的开放数据集。使用基于分数的方法学习分类贝叶斯网络结构，并对变量顺序进行约束。在交叉验证实验中使用布里尔分数评估模型的预测准确性。最后，展示了一个用户界面，该界面允许设置一些观察到的水平并查询贝叶斯网络产生的边际概率。
