S. J. Pöppl

University of Lübeck

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Heinz Handels

University of Lübeck

Roland Linder

Techniker Krankenkasse (Germany)

Jan Ehrhardt

University of Lübeck

W Plötz

Krankenhaus Barmherzige Brüder

Werner S. Tirsch

Institute of Bioinformatics and Systems Biology

Jürgen Kreusch

Berlin Heart (Germany)

Bernd Strathmann

University of Lübeck

Ehab I. Mohamed

Alexandria University

Antonino De Lorenzo

University of Rome Tor Vergata

T. Malina

Evangelisches Krankenhaus Alsterdorf

Cooperative Institutions

University of Lübeck

University Hospital Schleswig-Holstein

Ludwig-Maximilians-Universität München

Universität Hamburg

Johannes Gutenberg University Mainz

University of Rome Tor Vergata

Technical University of Munich

University Medical Center Hamburg-Eppendorf

University of Göttingen

Institute of Bioinformatics and Systems Biology

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ACMD: A Practical Tool for Automatic Neural Net Based Learning

Lecture notes in computer science (2001)

Roland Linder S. J. Pöppl

Benchmark (surveying)

10.1007/3-540-45497-7_25

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Citations (15)

Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis.

PubMed (2002)

Ehab I. Mohamed Roland Linder G. Perriello Nicola Di Daniele S. J. Pöppl

Diabetes is a major health problem in both industrial and developing countries, and its incidence is rising. Although detection of diabetes is improving, about half of the patients with Type 2 diabetes are undiagnosed and the delay from disease onset to diagnosis may exceed 10 yr. Thus, earlier detection of Type 2 diabetes and treatment of hyperglycaemia and related metabolic abnormalities is of vital importance. The objectives of the present study were to examine urine samples from Type 2 diabetic patients and healthy volunteers using the electronic nose technology and to evaluate possible application of data classification methods such as self-learning artificial neural networks (ANN) and logistic regression (LR) in comparison with principal components analysis (PCA). Urine samples from Type 2 diabetic patients and healthy controls were processed randomly using a simple 8-sensors electronic nose and individual electronic nose patterns were qualitatively classified using the "Approximation and Classification of Medical Data" (ACMD) network based on 2 output neurons, binary LR analysis and PCA. Distinct classes were found for Type 2 diabetic subjects and controls using PCA, which had a 96.0% successful classification percentage mean while qualitative ANN analysis and LR analysis had successful classification percentages of 92.0% and 88.0%, respectively. Therefore, the ACMD network is suitable for classifying medical and clinical data.

Electronic Nose

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Nicht-lineare voxelbasierte Registrierung unter Einbeziehung von Differentialeigenschaften

Informatik aktuell (2004)

Dennis Säring Jan Ehrhardt Heinz Handels S. J. Pöppl

10.1007/978-3-642-18536-6_58

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Citations (1)

Automatische Registrierung von CT- und MR-Bildfolgen für die dreidimensionale Planung von Hüftoperationen

Informatik aktuell (2000)

Jonas Ehrhardt Heinz Handels W Plötz S. J. Pöppl

10.1007/978-3-642-59757-2_3

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Citations (1)

Predicting the intracellular water compartment using artificial neural network analysis

Acta Diabetologica (2003)

Ehab I. Mohamed C. Maiolo Roland Linder S. J. Pöppl Antonino De Lorenzo

Body water

10.1007/s00592-003-0019-9

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Citations (3)

Computer-Supported Diagnosis of Melanoma in Profilometry

Methods of Information in Medicine (1999)

Heinz Handels Th. Roß Jürgen Kreusch Hendrik Wolff S. J. Pöppl

Laser profilometry offers new possibilities to improve non-invasive tumor diagnostics in dermatology. In this paper, a new approach to computer-supported analysis and interpretation of high-resolution skin-surface profiles of melanomas and nevocellular nevi is presented. Image analysis methods are used to describe the profile's structures by texture parameters based on co-occurrence matrices, features extracted from the Fourier power spectrum, and fractal features. Different feature selection strategies, including genetic algorithms, are applied to determine the best possible subsets of features for the classification task. Several architectures of multilayer perceptrons with error back-propagation as learning paradigm are trained for the automatic recognition of melanomas and nevi. Furthermore, network-pruning algorithms are applied to optimize the network topology. In the study, the best neural classifier showed an error rate of 4.5% and was obtained after network pruning. The smallest error rate in all, of 2.3%, was achieved with nearest neighbor classification.

Melanoma diagnosis

10.1055/s-0038-1634144

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Citations (14)

Biomedical vocabularies--the demand for differentiation.

Studies in health technology and informatics (2007)

Josef Ingenerf S. J. Pöppl

The need of biomedical vocabularies is well known for various tasks, e.g., supporting structured data entry, decision support and electronic data exchange as well as retrieval and statistical evaluation of the data. Due to a considerable diversity of artifacts like interface terminologies, classifications and thesauri it seems to be reasonable to demand for a massive reduction and, finally, to end up with just one unique "multi-purpose world terminology". Concept-based reference terminologies like SNOMED CT might be candidates for that idea. Unfortunately, the above mentioned kinds of vocabularies cannot be replaced because of their specific purpose-dependent nature. Their mutual distinctive characteristics are outlined and compared with inherent purpose-less concept systems that are based on formal approaches like description logics. For supporting interoperability the different kinds of purpose-dependent vocabularies can and should be improved by mappings to machine-processible reference terminologies. As a side-effect, this paper may contribute to the meta-terminology in this field of medical terminology.

Controlled vocabulary

Interface (matter)

Data Exchange

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Citations (6)

Atlasbasierte Erkennung anatomischer Landmarken

Informatik aktuell (2003)

Jan Ehrhardt Heinz Handels S. J. Pöppl

10.1007/978-3-642-18993-7_80

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Citations (5)