Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes
This addresses the problem of predicting critical health episodes to reduce mortality in hospitals, representing an incremental improvement over existing methods.
The paper tackles early anomaly detection for critical health events in intensive care units by proposing a hierarchical method using pre-conditional events, resulting in better performance compared to state-of-the-art approaches.
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.