UTILIZING TEMPORAL DATA ABSTRACTION FOR DATA VALIDATION AND THERAPY PLANNING FOR ARTIFICIALLY VENTILATED NEWBORN-INFANTS

Medical diagnosis and therapy planning at modern intensive care units (ICUs) have been refined by the technical improvement of their equipme nt. However, the bulk of continuous data arising from complex monitori ng systems in combination with discontinuously assessed numerical and qualitative data creates a rising information management problem at ne onatal ICUs (NICUs). We developed methods for data validation and ther apy planning which incorporate knowledge about point and interval data , as well as expected qualitative trend descriptions to arrive at unif ied qualitative descriptions of parameters (temporal data abstraction) . Our methods are based on schemata for data-point transformation and curve fitting which express the dynamics of and the reactions to diffe rent degrees of parameters' abnormalities as well as on smoothing and adjustment mechanisms to keep the qualitative descriptions stable. We show their applicability in detecting anomalous system behavior early, in recommending therapeutic actions, and in assessing the effectivene ss of these actions within a certain period. We implemented our method s in VIE-VENT, an open-loop knowledge-based monitoring and therapy pla nning system for artificially ventilated newborn infants. The applicab ility and usefulness of our approach are illustrated by examples of VI E-VENT. Finally, we present our first experiences with using VIE-VENT in a real clinical setting.