Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis

@article{Niemann2014LearningAI,
  title={Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis},
  author={Uli Niemann and Henry V{\"o}lzke and Jens-Peter K{\"u}hn and Myra Spiliopoulou},
  journal={Expert Syst. Appl.},
  year={2014},
  volume={41},
  pages={5405-5415}
}
Personalized Medicine requires the analysis of epidemiological data for the identification of subgroups sharing some risk factors and exhibiting dedicated outcome risks. We investigate the potential of data mining methods for the analysis of subgroups of cohort participants on hepatic steatosis. We propose a workflow for data preparation and mining on epidemiological data and we present InteractiveRuleMiner, an interactive tool for the inspection of rules in each subpopulation, including… CONTINUE READING
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