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If you use CaTabRa in your research, we would appreciate citing the following conference paper:

• A. Maletzky, S. Kaltenleithner, P. Moser and M. Giretzlehner. CaTabRa: Efficient Analysis and Predictive Modeling of Tabular Data. In: I. Maglogiannis, L. Iliadis, J. MacIntyre and M. Dominguez (eds), Artificial Intelligence Applications and Innovations (AIAI 2023). IFIP Advances in Information and Communication Technology, vol 676, pp 57-68, 2023. DOI:10.1007/978-3-031-34107-6_5

  @inproceedings{CaTabRa2023,
    author = {Maletzky, Alexander and Kaltenleithner, Sophie and Moser, Philipp and Giretzlehner, Michael},
    editor = {Maglogiannis, Ilias and Iliadis, Lazaros and MacIntyre, John and Dominguez, Manuel},
    title = {{CaTabRa}: Efficient Analysis and Predictive Modeling of Tabular Data},
    booktitle = {Artificial Intelligence Applications and Innovations},
    year = {2023},
    publisher = {Springer Nature Switzerland},
    address = {Cham},
    pages = {57--68},
    isbn = {978-3-031-34107-6},
    doi = {10.1007/978-3-031-34107-6_5}
  }
  

The following publications used CaTabRa for data analysis and model development:

• N. Stroh, H. Stefanits, A. Maletzky, S. Kaltenleithner, S. Thumfart, M. Giretzlehner, R. Drexler, F. Ricklefs, L. Dührsen, S. Aspalter, P. Rauch, A. Gruber and M. Gmeiner. Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms. Scientific Reports 13:22641, 2023. DOI:10.1038/s41598-023-50012-8

• T. Tschoellitsch, P. Moser, A. Maletzky, P. Seidl, C. Böck, T. Roland, H. Ludwig, S. Süssner, S. Hochreiter and J. Meier. Potential Predictors for Deterioration of Renal Function After Transfusion. Anesthesia & Analgesia 138(3):145-154, 2024. DOI:10.1213/ANE.0000000000006720

• T. Tschoellitsch, A. Maletzky, P. Moser, P. Seidl, C. Böck, T. Tomic Mahečić, S. Thumfart, M. Giretzlehner, S. Hochreiter and J. Meier. Machine Learning Prediction of Unsafe Discharge from Intensive Care: a retrospective cohort study. Journal of Clinical Anesthesia 99:111654, 2024. DOI:10.1016/j.jclinane.2024.111654

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Acknowledgments

This project is financed by research subsidies granted by the government of Upper Austria. RISC Software GmbH is Member of UAR (Upper Austrian Research) Innovation Network.