Abstract
We present our Interactive Medical Miner, a tool for classification and model drill-down, designed to study epidemiological data. Our tool encompasses supervised learning (with decision trees and classification rules), utilities for data selection, and a rich panel with options for inspecting individual classification rules, and for studying the distribution of variables in each of the target classes. Since some of the epidemiological data available to the medical researcher may be still unlabeled (e.g. because the medical recordings for some part of the cohort are still in progress), our Interactive Medical Miner also supports the juxtaposition of labeled and unlabeled data. The set of methods and scientific workflow supported with our tool have been published in [1].
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Niemann, U., Völzke, H., Kühn, J.-P., Spiliopoulou, M.: Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis. Expert Systems with Applications 41(11), 5405–5415 (2014)
Zhanga, C., Kodell, R.L.: Subpopulation-specific confidence designation for more informative biomedical classification. Artificial Intelligence in Medicine 58(3), 155–163 (2013)
Pinheiro, F., Kuo, M.-H., Thomo, A., Barnett, J.: Extracting association rules from liver cancer data using the FP-growth algorithm. In: 3rd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS (2013)
Quinlan, J.R.: Learning with continuous classes. In: 5th Australian Joint Conference on Artificial Intelligence, vol. 92, pp. 343–348 (1992)
Völzke, H., Alte, D., Schmidt, C.O., Radke, D., Lorbeer, R., Friedrich, N., et al.: Cohort Profile: The Study of Health in Pomerania. International Journal of Epidemiology 40(2), 294–307 (2011)
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Niemann, U., Spiliopoulou, M., Völzke, H., Kühn, JP. (2014). Interactive Medical Miner: Interactively Exploring Subpopulations in Epidemiological Datasets. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science(), vol 8726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44845-8_35
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DOI: https://doi.org/10.1007/978-3-662-44845-8_35
Publisher Name: Springer, Berlin, Heidelberg
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