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

被引：0
|
作者：
Thomas Hillemacher
Helge Frieling
Julia Wilhelm
Annemarie Heberlein
Deniz Karagülle
Stefan Bleich
Bernd Lenz
Johannes Kornhuber
机构：
[1] Hannover Medical School,Center for Addiction Research (CARe), Department for Psychiatry, Social Psychiatry and Psychotherapy
[2] University Hospital Erlangen,Department for Psychiatry and Psychotherapy
来源：
Journal of Neural Transmission | 2012年 / 119卷
关键词：
Alcohol withdrawal; Seizure; Homocysteine; Detoxification; Random forest; Data mining; Machine learning; Prolactin;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：1449 / 1453
页数：4
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