@incollection{WeidlMadsenDahlquist2008, author = {Weidl, Galia and Madsen, Anders L. and Dahlquist, Erik}, title = {Decision Support on Complex Industrial Process Operation}, series = {Bayesian Networks: A Practical Guide to Applications}, booktitle = {Bayesian Networks: A Practical Guide to Applications}, publisher = {Wiley}, doi = {10.1002/9780470994559.ch18}, pages = {313 -- 328}, year = {2008}, abstract = {Introduction: A methodology for Root Cause AnalysisPulp and paper applicationThe ABB Industrial IT platformConclusion}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @article{RoehrlIglesiasRozasWeidl2008, author = {R{\"o}hrl, Norbert and Iglesias-Rozas, Jos{\´e} R. and Weidl, Galia}, title = {A modern reproducible method for the histologic grading of astrocytomas with statistical classification tools}, series = {Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology}, volume = {2008}, journal = {Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology}, number = {30/1}, pages = {33 -- 8}, year = {2008}, abstract = {To investigate whether statistical classification tools can infer the correct World Health Organization (WHO) grade from standardized histologic features in astrocytomas and how these tools compare with GRADO-IGL, an earlier computer-assisted method. A total of 794 human brain astrocytomas were studied between January 1976 and June 2005. The presence of 50 histologic features was rated in 4 categories from 0 (not present) to 3 (abundant) by visual inspection of the sections under a microscope. All tumors were also classified with the corresponding WHO grade between I and IV. We tested the prediction performance of several statistical classification tools (learning vector quantization [LVQ], supervised relevance neural gas [SRNG], support vector machines [SVM], and generalized regression neural network [GRNN]) for this data set. The WHO grade was predicted correctly from histologic features in close to 80\% of the cases by 2 modern classifiers (SRNG and SVM), and GRADO-IGL was predicted correctly in > 84\% of the cases by a GRNN. A standardized report, based the 50 histologic features, can be used in conjunction with modern classification tools as an objective and reproducible method for histologic grading of astrocytomas.}, subject = {Cytologie}, language = {en} }