Multiparametric time course prognoses by means of case-based reasoning and Abstractions of data and time
| Autor: | B. Pollwein, Rainer Schmidt, B. Heindl, Lothar Gierl |
|---|---|
| Rok vydání: | 1997 |
| Předmět: |
Medical knowledge
Decision support system Time Factors Computer science Information Storage and Retrieval Kidney Function Tests computer.software_genre Machine learning Decision Support Techniques Artificial Intelligence Humans Case-based reasoning Diagnosis Computer-Assisted Abstraction Reasoning system business.industry Decision Trees Cognition Prognosis Intensive Care Units Time course Data mining State (computer science) Artificial intelligence business Case Management computer |
| Zdroj: | Medical Informatics. 22:237-250 |
| ISSN: | 0307-7640 |
| DOI: | 10.3109/14639239709010896 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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