Erschienen in:
01.11.2012 | Biological Psychiatry - Original Article
Indicators for elevated risk factors for alcohol-withdrawal seizures: an analysis using a random forest algorithm
verfasst von:
Thomas Hillemacher, Helge Frieling, Julia Wilhelm, Annemarie Heberlein, Deniz Karagülle, Stefan Bleich, Bernd Lenz, Johannes Kornhuber
Erschienen in:
Journal of Neural Transmission
|
Ausgabe 11/2012
Einloggen, um Zugang zu erhalten
Abstract
Alcohol-withdrawal seizures (AWS) are an important and relevant complication during detoxification in alcohol-dependent patients. Therefore, it is important to evaluate the individual risk for AWS. We apply a random forest algorithm to assess possible predictive markers in a large sample of 200 alcohol-dependent patients undergoing alcohol withdrawal. This analysis showed that the combination of homocysteine, prolactin, blood alcohol concentration on admission, number of preceding withdrawals, age and the number of cigarettes smoked may successfully predict AWS. In conclusion, the results of this analysis allow for origination of further research, which should include additional biological and psychosocial parameters as well as consumption behaviour.