Medical multiparametric time course prognoses applied to kidney function assessments.
In this paper, we describe an approach to utilize case-based reasoning methods for trend prognoses for the monitoring of the kidney function in an Intensive Care Unit (ICU) setting. Since using conventional methods for reasoning over time does not fit for course predictions with poor medical knowledge of typical course patterns, we have developed abstraction methods suitable for integration into our case-based reasoning system ICONS. These methods combine medical experience with prognoses of multiparametric courses. On the ICU, the monitoring system NIMON provides a daily report based on current measured and calculated kidney function parameters. Subsequently, we generate course-characteristic trend descriptions of the renal function over the course of time. Using case-based reasoning retrieval methods, we search in the case base for courses similar to the current trend descriptions. Finally, we present the current course together with similar courses as comparisons and as probable prognoses to the user. We applied case-based reasoning methods in a domain which seemed reserved for statistical methods and conventional temporal reasoning.
Schmidt
R
R
Institute for Medical Informatics and Biometry, University of Rostock, Germany.
Pollwein
B
B
Gierl
L
L
D001185:Artificial Intelligence; D006801:Humans; D016247:Information Storage and Retrieval; D007362:Intensive Care Units; D007668:Kidney / Q000502:physiology*; D007677:Kidney Function Tests; D016014:Linear Models; D008991:Monitoring, Physiologic; D011379:Prognosis; D013997:Time Factors