Mario Ziller

Freie Universität Berlin

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Georg Winterer

Charité - Universitätsmedizin Berlin

W.M. Herrmann

University of Zurich

Andreas Heinz

Charité - Universitätsmedizin Berlin

K. Frick

Christian Doppler Klinik

B. Klöppel

University of Kassel

Christoph Mulert

University of Giessen

Michael N. Smolka

TU Dresden

Yvonne Wuebben

Freie Universität Berlin

Jerzy Samochowiec

Pomeranian Medical University

Christoph Staubach

Friedrich-Loeffler-Institut

Cooperative Institutions

Freie Universität Berlin

Charité - Universitätsmedizin Berlin

Humboldt-Universität zu Berlin

Universität Hamburg

Klinik und Poliklinik für Psychiatrie und Psychotherapie

University Medical Center Hamburg-Eppendorf

Max Delbrück Center

Friedrich-Loeffler-Institut

Medizinische Hochschule Hannover

St. Hedwig-Krankenhaus

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Frontal dysfunction in schizophrenia - a new electrophysiological classifier for research and clinical applications

European Archives of Psychiatry and Clinical Neuroscience (2000)

Georg Winterer Mario Ziller Hans Dorn K. Frick Christoph Mulert

10.1007/s004060070026

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Citations (56)

Quantitative EEG (qEEG) predicts relapse in patients with primary alcoholism — a comparison between multivariate statistics and artificial neural network approaches

Psychiatry Research Neuroimaging (1997)

Georg Winterer B. Klöppel Andreas Heinz Mario Ziller L.G. Schmidt

Methemoglobinemia

10.1016/s0925-4927(97)81603-4

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Citations (0)

P219 Quantitative EEG predicts relapse in patients with chronic alcoholism

Electroencephalography and Clinical Neurophysiology (1996)

Georg Winterer B. Klöppel Andreas Heinz Mario Ziller L.G. Schmidt

Chronic alcoholism

10.1016/0013-4694(96)88345-3

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Citations (1)

Multinomial regression analysis of individual host factors and paratuberculosis test results: Merkal Award Lecture

Franziska Gierke Mario Ziller Heike Köhler

Regression testing

Multinomial distribution

Source

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Classification of Observational Data with Artificial Neural Networks Versus Discriminant Analysis in Pharmacoepidemiological Studies - Can Outcome of Fluoxetine Treatment be Predicted?

Pharmacopsychiatry (1998)

Georg Winterer Mario Ziller María Lindén

For several years, there has been an ongoing discussion about appropriate methodological tools to be applied to observational data in pharmacoepidemiological studies. It is now suggested by our research group that artificial neural networks (ANN) might be advantageous in some cases for classification purposes when compared with discriminant analysis. This is due to their inherent capability to detect complex linear and nonlinear functions in multivariate data sets, the possibility of including data on different scales in the same model, as well as their relative resistance to "noisy" input. In this paper, a short introduction is given to the basics of neural networks and possible applications. For demonstration, a comparison between artificial neural networks and discriminant analysis was performed on a multivariate data set, consisting of observational data of 19738 patients treated with fluoxetine. It was tested, which of the two statistical tools outperforms the two other in regard to the therapeutic response prediction from the clinical input data. Essentially, it was found that neither discriminant analysis nor ANN are able to predict the clinical outcome on the basis of the employed clinical variables. Applying ANN, we were able to rule out the possibility of undetected suppressor effects to a greater extent than would have been possible by the exclusive application of discriminant analysis.

Data set

Optimal discriminant analysis

10.1055/s-2007-979333

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Citations (13)

Association analysis of exonic variants of the GABA(B)-receptor gene and alpha electroencephalogram voltage in normal subjects and alcohol-dependent patients.

Behavior Genetics (2003)

Georg Winterer Richard Mahlberg Michael N. Smolka Jerzy Samochowiec Mario Ziller

Alpha (finance)

Association (psychology)

10.1023/a:1021043315012

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