Kramer Diether, Veeranki Sai, Hayn Dieter, Quehenberger Franz, Leodolter Werner, Jagsch Christian, Schreier Günter
Steirische Krankenanstaltengesellschaft m.b.H. (KAGes), Graz, Austria.
AIT Austrian Institute of Technology, Graz, Austria.
Stud Health Technol Inform. 2017;236:32-39.
Delirium is an acute confusion condition, which is common in elderly and often misdiagnosed in hospitalized patients. Early identification and prevention of delirium could reduce morbidity and mortality rates in those affected and reduce hospitalization costs. We have developed and validated a multivariate prediction model that predicts delirium and gives an early warning to physicians. A large set of patient electronic medical records have been used in developing the models. Classical learning algorithms have been used to develop the models and compared the results. Excellent results were obtained with the feature set and parameter settings attaining accuracy of 84%.
谵妄是一种急性意识模糊状态,在老年人中很常见,在住院患者中常被误诊。早期识别和预防谵妄可以降低患者的发病率和死亡率,并降低住院费用。我们开发并验证了一种多变量预测模型,该模型可预测谵妄并向医生发出早期预警。在开发这些模型时使用了大量患者电子病历。已使用经典学习算法来开发模型并比较结果。通过特征集和参数设置获得了优异的结果,准确率达到84%。