Ferreira-Santos Daniela, Monteiro-Soares Matilde, Rodrigues Pedro Pereira
CINTESIS - Centre for Health Technology and Services Research, Portugal.
Stud Health Technol Inform. 2018;247:126-130.
Numerous diagnostic decisions are made every day by healthcare professionals. Bayesian networks can provide a useful aid to the process, but learning their structure from data generally requires the absence of missing data, a common problem in medical data. We have studied missing data imputation using a step-wise nearest neighbors' algorithm, which we recommended given its limited impact on the assessed validity of structure learning Bayesian network classifiers for Obstructive Sleep Apnea diagnosis.
医疗保健专业人员每天都要做出众多诊断决策。贝叶斯网络可为这一过程提供有益帮助，但从数据中学习其结构通常要求不存在缺失数据，而这在医学数据中是个常见问题。我们研究了使用逐步最近邻算法进行缺失数据插补，鉴于其对用于阻塞性睡眠呼吸暂停诊断的贝叶斯网络结构学习评估有效性的影响有限，我们推荐使用该算法。
