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01-11-2012 | Biological Psychiatry - Original Article

Indicators for elevated risk factors for alcohol-withdrawal seizures: an analysis using a random forest algorithm

Published in: Journal of Neural Transmission | Issue 11/2012

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.

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Title: Indicators for elevated risk factors for alcohol-withdrawal seizures: an analysis using a random forest algorithm
Publication date: 01-11-2012
Published in: Journal of Neural Transmission / Issue 11/2012
Print ISSN: 0300-9564
Electronic ISSN: 1435-1463
DOI: https://doi.org/10.1007/s00702-012-0825-8

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Indicators for elevated risk factors for alcohol-withdrawal seizures: an analysis using a random forest algorithm

Abstract

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

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