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Asthma Exacerbation Prediction and Interpretation based on Time-sensitive Attentive Neural Network: A Retrospective Cohort Study

By Yang Xiang, Hangyu Ji, Yujia Zhou, Fang Li, Jingcheng Du, Laila Rasmy, Stephen Wu, Wenjin.Jim Zheng, Hua Xu, Degui Zhi, Yaoyun Zhang, Cui Tao

Posted 29 Nov 2019
medRxiv DOI: 10.1101/19012161

BackgroundAsthma exacerbation is an acute or sub-acute episode of progressive worsening of asthma symptoms and can have significant impacts on patients daily life. In 2016, 12.4 million current asthmatics (46.9%) in the U.S. had at least one asthma exacerbation in the previous year. ObjectiveThe objectives of this study were to predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma. MethodsWe proposed a time-sensitive attentive neural network to predict asthma exacerbation using clinical variables from electronic health records (EHRs). The clinical variables were collected from the Cerner Health Facts(R) database between 1992 and 2015 including 31,433 asthmatic adult patients. Interpretations on both the patient level and the cohort level were investigated based on the model parameters. ResultsThe proposed model obtains an AUC value of 0.7003 through 5-fold cross-validation, which outperforms the baseline methods. The results also demonstrate that the addition of elapsed time embeddings considerably improves the performance on this dataset. Through further analysis, it was witnessed that risk factors behaved distinctly along the timeline and across patients. We also found supporting evidence from peer-reviewed literature for some possible cohort-level risk factors such as respiratory syndromes and esophageal reflux. ConclusionsThe proposed time-sensitive attentive neural network is superior to traditional machine learning methods and performs better than state-of-the-art deep learning methods in realizing effective predictive models for the prediction of asthma exacerbation. We believe that the interpretation and visualization of risk factors can help the clinical community to better understand the underlying mechanisms of the disease progression.

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