用自监督编码器提升心脏病患者心血管事件预测准确率
MIEO: encoding clinical data to enhance cardiovascular event prediction
- 用无标签数据训练自编码器构建患者潜空间表征
- 在心肌缺血患者数据上实现比原始数据更高的平衡准确率
- 适合标签数据少、缺失值多的临床预测场景
随着临床数据日益丰富，机器学习被用于从中提取知识并预测临床事件。然而现有方法面临两大挑战：标注数据稀缺和数据异质性导致的缺失值问题。本文提出使用自监督自编码器有效应对这些挑战。方法应用于缺血性心脏病患者的临床数据集，将患者数据嵌入由无标签数据构建的潜在空间，再用该表示训练神经网络分类器以预测心血管死亡。结果表明，相比直接在原始数据上训练分类器，该方法显著提升了平衡准确率，验证了其在无标签数据丰富的条件下具有广阔应用前景。
原文摘要 · Abstract (English)
As clinical data are becoming increasingly available, machine learning methods have been employed to extract knowledge from them and predict clinical events. While promising, approaches suffer from at least two main issues: low availability of labelled data and data heterogeneity leading to missing values. This work proposes the use of self-supervised auto-encoders to efficiently address these challenges. We apply our methodology to a clinical dataset from patients with ischaemic heart disease. Patient data is embedded in a latent space, built using unlabelled data, which is then used to train a neural network classifier to predict cardiovascular death. Results show improved balanced accuracy compared to applying the classifier directly to the raw data, demonstrating that this solution is promising, especially in conditions where availability of unlabelled data could increase.
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