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The early detection of anomalous events in time series data is essential inmany domains of application. In this paper we deal with critical health events,which represent a significant cause of mortality in intensive care units ofhospitals. The timely prediction of these events is crucial for mitigatingtheir consequences and improving healthcare. One of the most common approachesto tackle early anomaly detection problems is standard classification methods.In this paper we propose a novel method that uses a layered learningarchitecture to address these tasks. One key contribution of our work is theidea of pre-conditional events, which denote arbitrary but computable relaxedversions of the event of interest. We leverage this idea to break the originalproblem into two hierarchical layers, which we hypothesize are easier to solve.The results suggest that the proposed approach leads to a better performancerelative to state of the art approaches for critical health episode prediction.

Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes