Abstract
Much research has been performed in the field of medical temporal course analysis in the recent years. However, the methods developed so far either require a complete domain theory or well-known standards. Unfortunately, in many medical areas such knowledge is still missing. So, we have developed a method for predicting temporal courses – without well-known standards and without a complete domain theory. Our method combines Temporal Abstraction and Case-Based Reasoning. In the last decade Case-Based Reasoning, an artificial intelligence method that uses experiences in form of cases, has become successful in many areas. Temporal Abstraction is a medical informatics technique to generalise and describe sequences of events. Here we present our method and summarise two medical applications. The first one deals with multiparametric time courses of the kidney function. We apply the same ideas for the prognosis of the temporal spread of diseases like influenza.
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Schmidt, R., Gierl, L. (2003). Case-Based Reasoning for Time Courses Prognosis. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_131
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DOI: https://doi.org/10.1007/978-3-540-45224-9_131
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