Multiparametric time course prognoses by means of case-based reasoning and abstractions of data and time.
In this paper we describe an approach to utilize Case-Based Reasoning methods for trend prognoses for medical problems. Since using conventional methods for reasoning over time does not fit for course predictions without medical knowledge of typical course pattern, 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. We have chosen the monitoring of the kidney function in an Intensive Care Unit (ICU) setting as an example for diagnostic problems. On the ICU, the monitoring system NIMON provides a daily report based on current measured and calculated kidney function parameters. We abstract these parameters to a daily kidney function state. Subsequently, we use these states to 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 possible prognoses to the user.
Schmidt
R
R
Institut für Medizinische Informatik und Biometrie, Universität Rostock, Germany.
Heindl
B
B
Pollwein
B
B
Gierl
L
L
D001185:Artificial Intelligence; D019090:Case Management; D003661:Decision Support Techniques; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D016247:Information Storage and Retrieval; D007362:Intensive Care Units; D007677:Kidney Function Tests; D011379:Prognosis; D013997:Time Factors