Abstract
This paper addresses a method of therapy planning applicable in the absence of an appropriate curve-fitting model. It incorporates knowledge about data points, data intervals, and expected qualitative trend description to arrive at unified qualitative descriptions of parameters (temporal data abstraction). Our approach benefits from derived qualitative values which can be used for recommending therapeutic actions as well as for assessing the effectiveness of these actions within a certain period. It results in an easily comprehensible and transparent concept of therapy planning. Furthermore, we improved the system model of data interpretation and therapy planning by using importance ranking of variables, priority lists of attainable goals, and pruning of contradictory therapy recommendations.
Our methods are applicable in domains where an appropriate curve-fitting model is not available in advance. We have applied them in the field of artificial ventilation of newborn infants. The utility of our approach is illustrated by VIE-VENT, an open-loop knowledge-based system for artificially ventilated newborn infants.
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© 1995 Springer-Verlag Berlin Heidelberg
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Miksch, S., Horn, W., Popow, C., Paky, F. (1995). Therapy planning using qualitative trend descriptions. In: Barahona, P., Stefanelli, M., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1995. Lecture Notes in Computer Science, vol 934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60025-6_137
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DOI: https://doi.org/10.1007/3-540-60025-6_137
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