期刊
CLINICAL OTOLARYNGOLOGY
卷 43, 期 3, 页码 868-874出版社
WILEY
DOI: 10.1111/coa.13068
关键词
hearing loss; sudden; outcome prediction; machine learning
资金
- National Natural Science Foundation of China [81530032, 81500794, 61501518]
- National Key Basic Research Program of China [2014CB943001]
- China Postdoctoral Science Foundation [2017M613326]
- New Researcher Foundation of The PLA General Hospital [14KMZ04]
ObjectiveSudden sensorineural hearing loss (SSHL) is a multifactorial disorder with high heterogeneity, thus the outcomes vary widely. This study aimed to develop predictive models based on four machine learning methods for SSHL, identifying the best performer for clinical application. DesignSingle-centre retrospective study. SettingChinese People's liberation army (PLA) hospital, Beijing, China. ParticipantsA total of 1220 in-patient SSHL patients were enrolled between June 2008 and December 2015. Main outcome measuresAn advanced deep learning technique, deep belief network (DBN), together with the conventional logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) were developed to predict the dichotomised hearing outcome of SSHL by inputting six feature collections derived from 149 potential predictors. Accuracy, precision, recall, F-score and the area under the receiver operator characteristic curves (ROC-AUC) were exploited to compare the prediction performance of different models. ResultsOverall the best predictive ability was provided by the DBN model when tested in the raw data set with 149 variables, achieving an accuracy of 77.58% and AUC of 0.84. Nevertheless, DBN yielded inferior performance after feature pruning. In contrast, the LR, SVM and MLP models demonstrated opposite trend as the greatest individual prediction powers were obtained when included merely three variables, with the ROC-AUC ranging from 0.79 to 0.81, and then decreased with the increasing size of input features combinations. ConclusionsWith the input of enough features, DBN can be a robust prediction tool for SSHL. But LR is more practical for early prediction in routine clinical application using three readily available variables, that is time elapse between symptom onset and study entry, initial hearing level and audiogram.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据