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Assessing the difficulty of annotating medical data in crowdworking with help of experiments, , , , , and . PLOS ONE, 16 (7): 1-26 (July 2021)Identifying Relevant Features for a Multi-factorial Disorder with Constraint-Based Subspace Clustering., , , and . CBMS, page 207-212. IEEE Computer Society, (2016)Mining longitudinal epidemiological data to understand a reversible disorder, , , and . Proc. of the 13th Int. Symposium on Intelligent Data Analysis (IDA'14), Leuven, Belgium, Springer, (2014)accepted 08/2014, to appear.Mining Longitudinal Epidemiological Data to Understand a Reversible Disorder., , , and . IDA, volume 8819 of Lecture Notes in Computer Science, page 120-130. Springer, (2014)Using Participant Similarity for the Classification of Epidemiological Data on Hepatic Steatosis., , , and . CBMS, page 1-7. IEEE Computer Society, (2014)Discovering, selecting and exploiting feature sequence records of study participants for the classification of epidemiological data on hepatic steatosis., , , and . SAC, page 6-13. ACM, (2018)Can We Classify the Participants of a Longitudinal Epidemiological Study from Their Previous Evolution?, , , , and . CBMS, page 121-126. IEEE Computer Society, (2015)Exploiting background knowledge on evolving objects to identify relevant dimensions for classification.. Otto-von-Guericke University Magdeburg, Germany, (2019)