论文标题
对心电图数据负担得起的模型的监督弱
Weak Supervision for Affordable Modeling of Electrocardiogram Data
论文作者
论文摘要
分析心电图（ECG）是一种廉价且无创，但有力的诊断心脏病的方法。到目前为止，使用机器学习自动检测异常心跳的ECG研究取决于大型手动注释的数据集。虽然收集大量未标记的数据可能很简单，但异常心跳的逐点注释既乏味又昂贵。我们探索了多个弱监督源的使用，通过人类设计的启发式方法学习异常心跳的诊断模型，而无需在各个数据点上使用地面真相标签。我们的工作是最早在时间序列数据上直接定义弱监督资源的人之一。结果表明，只需六个直观的时间序列启发式方法，我们就可以在人力努力的情况下推断出超过100,000个心跳的高质量概率标签估计，并使用估计的标签来训练对持有测试数据评估的竞争性分类器。
Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet powerful way to diagnose heart disease. ECG studies using Machine Learning to automatically detect abnormal heartbeats so far depend on large, manually annotated datasets. While collecting vast amounts of unlabeled data can be straightforward, the point-by-point annotation of abnormal heartbeats is tedious and expensive. We explore the use of multiple weak supervision sources to learn diagnostic models of abnormal heartbeats via human designed heuristics, without using ground truth labels on individual data points. Our work is among the first to define weak supervision sources directly on time series data. Results show that with as few as six intuitive time series heuristics, we are able to infer high quality probabilistic label estimates for over 100,000 heartbeats with little human effort, and use the estimated labels to train competitive classifiers evaluated on held out test data.
