Context-Sensitive and Expectation-Guided Temporal Abstraction of High-Frequency Data

Contents

Therapy planning benefits from derived qualitative values or patterns which can be used for recommending therapeutic actions as well as for assessing the effectiveness of these actions within a certain period. Dealing with highfrequency data, shifting contexts, and different expectations of the development of parameters requires particular temporal abstraction methods to arrive at unified qualitative values or patterns. This paper addresses context-sensitive and expectationguided temporal abstraction methods. They incorporate knowledge about data points, data intervals, and expected qualitative trend patterns to arrive at unified qualitative descriptions of parameters (temporal data abstraction). Our methods are based on context-sensitive schemata for data-point transformation and curve fitting which express the dynamics of and the reactions to different degrees of parameters’ abnormalities, as well as on smoothing and adjustment mechanisms to keep the qualitative descriptions stable in case of shifting contexts or data oscillating near thresholds. The temporal abstraction methods are integrated and implemented in VIE-VENT, an open-loop knowledgebased monitoring and therapy planning system for artificially ventilated newborn infants. The applicability and usefulness of our approach are illustrated by examples of VIE-VENT.