用数据增强提升新冠患者住院时长预测准确率
Oversampling techniques for predicting COVID-19 patient length of stay
- 通过合成数据扩充样本，解决住院时长分布不均问题
- 采用贝叶斯优化调参的神经网络模型，F1分数表现最佳
- 适合医疗数据不平衡场景下的重症风险预测研究
COVID-19是一种2019年引发全球大流行的呼吸道疾病，症状包括发热、咳嗽、呼吸急促、疲劳等，严重程度差异大。本文分析电子健康记录（EHR），以住院时长（LOS）作为疾病严重程度的衡量指标，构建了一个类别不平衡的分类任务。为缓解样本不均衡问题，我们采用合成方法生成扩增训练数据集。随后将扩增数据输入人工神经网络（ANN），利用贝叶斯优化进行超参数调优。最终选取F1分数最高的模型进行评估与讨论。
原文摘要 · Abstract (English)
COVID-19 is a respiratory disease that caused a global pandemic in 2019. It is highly infectious and has the following symptoms: fever or chills, cough, shortness of breath, fatigue, muscle or body aches, headache, the new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea. These symptoms vary in severity; some people with many risk factors have been known to have lengthy hospital stays or die from the disease. In this paper, we analyze patients' electronic health records (EHR) to predict the severity of their COVID-19 infection using the length of stay (LOS) as our measurement of severity. This is an imbalanced classification problem, as many people have a shorter LOS rather than a longer one. To combat this problem, we synthetically create alternate oversampled training data sets. Once we have this oversampled data, we run it through an Artificial Neural Network (ANN), which during training has its hyperparameters tuned using Bayesian optimization. We select the model with the best F1 score and then evaluate it and discuss it.
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