Identifying and Predicting Changes in Behavioral Patterns for Temporal Data in Treatment of Neonatal Respiratory Failure

In this paper, we present the findings of a study focused on discovering process models and tracking their evolution over time. The research specifically targets a distinct category of these models known as behavioral patterns. Consequently, the challenges and techniques addressed here involve temporal data. To demonstrate the issues and methodologies associated with identifying process patterns and their changes, we use illustrative data from the treatment of respiratory failure in premature infants. The main achievement of the paper is to successfully model behavioral patterns using machine learning models and predict changes in a neonate’s state. That was justified by comparing the classifier’s sensitivity for cases of deterioration, improvement, and stability. Classification quality is best when the pattern remains constant. However, in the proposed model, when the pattern deteriorates, the classification quality decreases only slightly.