Morik, K., Imho , M., Brockhausen, P., Joachims, T., and Gather, U. (2000). Knowledge discovery and knowledge validation in intensive care. Arti cial Intelligence in Medicine. accepted for publication. Home/Search Document Details and Download
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Using real world data for modeling a protocol for ICU monitoring - Scholz (2002) (Correct)
....to repair data, as much as possible. For this task a variety of mainly statistical approaches has been suggested ( 16] 10] 11] 2] 3] An approach combining a statistical signal to symbol method, machine learning for state action rules, and declarative domain knowledge is presented in [7]. 3 THE MODELING APPROACH The approach presented in this paper aims at drastically reducing the necessary efforts for building such protocols in the ICU context, especially in early stages of development. The idea is twofold. First of all, we conceive clinical protocols as expert knowledge, and ....
K.Morik, M.Imhoff, P.Brockhausen, T.Joachims, and U.Gather. Knowledge discovery and knowledge validation in intensive care. Artificial Intelligence in Medicine, 19, 2000.
The Maximum-Margin Approach to Learning Text Classifiers -.. - Joachims (2000) (17 citations) Self-citation (Morik Joachims) (Correct)
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Morik, K., Imho , M., Brockhausen, P., Joachims, T., and Gather, U. (2000). Knowledge discovery and knowledge validation in intensive care. Arti cial Intelligence in Medicine. accepted for publication.
Support Vector Machines And Learning About Time - Stefan Uping And (2003) Self-citation (Morik) (Correct)
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Katharina Morik, Michael Imhoff, Peter Brockhausen, Thorsten Joachims, and Ursula Gather, "Knowledge discovery and knowledge validation in intensive care," Artificial Intelligence in Medicine, 2000.
Support Vector Machines and Learning about Time - Rüping, Morik (2003) Self-citation (Morik) (Correct)
....Sometimes, best results are achieved if one drops the idea of a time series at all. For example, for the task of recommending drug administration from recorded vital signs of intensive care patients a high dimensional, noisy classi cation problem on multivariate time series it was found in [16] that the best representation was to ignore time dependencies completely and make a non temporal classi cation based on the last obeservation only. In the eld of chromatography, Rittho et al. 20] solved the problem of predicting certain chemical coecients based on the chromatographical ....
Katharina Morik, Michael Imho , Peter Brockhausen, Thorsten Joachims, and Ursula Gather. Knowledge discovery and knowledge validation in intensive care. Arti cial Intelligence in Medicine, 19(3):225{ 249, 2000.
Pattern Recognition in Intensive Care Online Monitoring - Fried, Gather, Imhoff Self-citation (Imho Gather) (Correct)
....with high probability and with a short time delay as is needed for life threatening complications may be insensitive against small or moderate shifts. Reliable detection of the latter is important for assessing intervention e#ects and as an input for knowledge based bedside decision support [69]. A particularly di#cult problem is the fast and correct detection of a slow trend. Makivirta [1] stated that the trend detectors developed at that time had little practical use. Moreover, a useful system should not only detect a trend, but it should also be able to quantify it. In view of all ....
....here. These techniques could be combined with methods of artificial intelligence which use the patterns found in the statistical analysis to assess the current state of the patient. By classifying these patterns according to existing knowledge gained from physicians and former data analysis [69] the physician in charge might then be given options of how to respond properly. Acknowledgements The financial support of the Deutsche Forschungsgemeinschaft (SFB 475, Reduction of complexity in multivariate data structures ) is gratefully acknowledged. ....
Morik, K., Imho#, M., Brockhausen, P., Joachims, T., and Gather, U. (2000), "Knowledge discovery and knowledge validation in intensive care," Art. Int. Med., vol. 19, pp. 225-249.
SVM-based Filtering of E-mail Spam with Content-specic - Misclassication Costs.. (2001) (Correct)
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K. Morik, M. Imhoff, P. Brockhausen, T. Joachims, and U. Gather, Knowledge discovery and knowledge validation in intensive care,Articial Intelligence in Medicine, 19 (2000), pp. 225249.
Asymmetric Missing-Data Problems: Overcoming the Lack of.. - Aleksander Kocz And (2002) (Correct)
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Morik, K., Imboff, M., Brockhausen, P., Joachims, T., and Gather, U.: 2000, Knowledge discovery and knowledge validation in intensive care, Articial Intelligence in Medicine 19(3), 225249.
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