论文标题
时间序列的早期异常检测：预测关键健康发作的层次结构方法
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes
论文作者
论文摘要
时间序列数据中对异常事件的早期检测至关重要。在本文中，我们处理了关键的健康事件，这代表了医院重症监护病房死亡的重要原因。这些事件的及时预测对于减轻其后果和改善医疗保健至关重要。解决早期异常检测问题的最常见方法之一是标准分类方法。在本文中，我们提出了一种新的方法，该方法使用分层学习体系结构来解决这些任务。我们作品的一个关键贡献是条件前事件的想法，该事件表示感兴趣的事件的任意但可计算的放松版本。我们利用这个想法将原始问题分解为两个分层层，我们假设这更容易解决。结果表明，相对于关键健康事件预测的艺术方法，所提出的方法导致了更好的性能。
The early detection of anomalous events in time series data is essential in many domains of application. In this paper we deal with critical health events, which represent a significant cause of mortality in intensive care units of hospitals. The timely prediction of these events is crucial for mitigating their consequences and improving healthcare. One of the most common approaches to tackle early anomaly detection problems is standard classification methods. In this paper we propose a novel method that uses a layered learning architecture to address these tasks. One key contribution of our work is the idea of pre-conditional events, which denote arbitrary but computable relaxed versions of the event of interest. We leverage this idea to break the original problem into two hierarchical layers, which we hypothesize are easier to solve. The results suggest that the proposed approach leads to a better performance relative to state of the art approaches for critical health episode prediction.
