Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

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Gewählte Publikation:

Tafeit, E; Reibnegger, G.
Artificial neural networks in laboratory medicine and medical outcome prediction.
Clin Chem Lab Med. 1999; 37(9):845-853 Doi: 10.1515/CCLM.1999.128
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Führende Autor*innen der Med Uni Graz
Reibnegger Gilbert
Tafeit Erwin
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Abstract:
Since the early nineties the number of scientific papers reporting on artificial neural network (ANN) applications in medicine has been quickly increasing. In the present paper, we describe in some detail the architecture of network types used most frequently in ANN applications in the broad field of laboratory medicine and clinical chemistry, present a technique-structured review about the recent ANN applications in the field, and give information about the improvements of available ANN software packages. ANN applications are divided into two main classes: supervised and unsupervised methods. Most of the described supervised applications belong to the fields of medical diagnosis (n = 7) and outcome prediction (n = 9). Laboratory and clinical data are presented to multilayer feed-forward ANNs which are trained by the back propagation algorithm. Results are often better than those of traditional techniques such as linear discriminant analysis, classification and regression trees (CART), Cox regression analysis, logistic regression, clinical judgement or expert systems. Unsupervised ANN applications provide the ability of reducing the dimensionality of a dataset. Low-dimensional plots can be generated and visually understood and compared. Results are very similar to that of cluster analysis and factor analysis. The ability of Kohonen's self-organizing maps to generate 2D maps of molecule surface properties was successfully applied in drug design.
Find related publications in this database (using NLM MeSH Indexing)
Chemistry, Clinical - methods
Laboratories - methods
MEDLINE - methods
Neural Networks (Computer) - methods
Software - methods
Treatment Outcome - methods

Find related publications in this database (Keywords)
supervised learning
feed-forward neural networks
unsupervised learning
Kohonen's self-organizing map
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