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Title:

A machine learning approach to risk assessment for alcohol withdrawal syndrome.

Document type:
Journal Article; Research Support, Non-U.S. Gov't
Author(s):
Burkhardt, Gerrit; Adorjan, Kristina; Kambeitz, Joseph; Kambeitz-Ilankovic, Lana; Falkai, Peter; Eyer, Florian; Koller, Gabi; Pogarell, Oliver; Koutsouleris, Nikolaos; Dwyer, Dominic B
Abstract:
At present, risk assessment for alcohol withdrawal syndrome relies on clinical judgment. Our aim was to develop accurate machine learning tools to predict alcohol withdrawal outcomes at the individual subject level using information easily attainable at patients' admission. An observational machine learning analysis using nested cross-validation and out-of-sample validation was applied to alcohol-dependent patients at two major detoxification wards (LMU, n = 389; TU, n = 805). 121 retrospective...     »
Journal title abbreviation:
Eur Neuropsychopharmacol
Year:
2020
Journal volume:
35
Pages contribution:
61-70
Fulltext / DOI:
doi:10.1016/j.euroneuro.2020.03.016
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/32418843
Print-ISSN:
0924-977X
TUM Institution:
2. Medizinische Klinik Toxikologische Abteilung (alt) (Prof. Zilker)
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