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Fatal diseases, as Critical Health Episodes (CHEs), represent real dangersfor patients hospitalized in Intensive Care Units. These episodes can lead toirreversible organ damage and death. Nevertheless, diagnosing them in timewould greatly reduce their inconvenience. This study therefore focused onbuilding a highly effective early warning system for CHEs such as AcuteHypotensive Episodes and Tachycardia Episodes. To facilitate the precocity ofthe prediction, a gap of one hour was considered between the observationperiods (Observation Windows) and the periods during which a critical event canoccur (Target Windows). The MIMIC II dataset was used to evaluate theperformance of the proposed system. This system first includes extractingadditional features using three different modes. Then, the feature selectionprocess allowing the selection of the most relevant features was performedusing the Mutual Information Gain feature importance. Finally, thehigh-performance predictive model LightGBM was used to perform episodeclassification. This approach called MIG-LightGBM was evaluated using fivedifferent metrics: Event Recall (ER), Reduced Precision (RP), averageAnticipation Time (aveAT), average False Alarms (aveFA), and Event F1-score(EF1-score). A method is therefore considered highly efficient for the earlyprediction of CHEs if it exhibits not only a large aveAT but also a largeEF1-score and a low aveFA. Compared to systems using Extreme Gradient Boosting,Support Vector Classification or Naive Bayes as a predictive model, theproposed system was found to be highly dominant. It also confirmed itssuperiority over the Layered Learning approach.

Efficient Novelty Detection Methods for Early Warning of Potential Fatal Diseases