On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer. Kop, R.; Hoogendoorn, M.; Moons; G, L. M; Numans; E, M.; and ten Teije, A. In Artificial Intelligence in Medicine, 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015, Proceedings AIME, 2015. Springer.
On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer [pdf]Paper  abstract   bibtex   
Electronic Medical Records (EMRs) provide a wealth of data that can be used to generate predictive models for diseases. Quite some studies have been performed that use EMRs to generate such models for specific diseases, but most of them are based on more traditional tech- niques used in medical domain, such as logistic regression. This paper studies the benefit of using advanced data mining techniques for Col- orectal Cancer (CRC). CRC is the second most common cause of death in the EU and is known to be a disease with very a-specific predictors, making it dicult to generate good predictive models. In addition, the EMR data itself has its own challenges, including the sparsity, the di↵er- ences in which physicians code the data, the temporal nature of the data, and the imbalance in the data. Results show that state-of-the-art data mining techniques, including temporal data mining, are able to generate better predictive models than currently available in the literature.
@inproceedings{ Kop,
  abstract = {Electronic Medical Records (EMRs) provide a wealth of data that can be used to generate predictive models for diseases. Quite some studies have been performed that use EMRs to generate such models for specific diseases, but most of them are based on more traditional tech- niques used in medical domain, such as logistic regression. This paper studies the benefit of using advanced data mining techniques for Col- orectal Cancer (CRC). CRC is the second most common cause of death in the EU and is known to be a disease with very a-specific predictors, making it dicult to generate good predictive models. In addition, the EMR data itself has its own challenges, including the sparsity, the di↵er- ences in which physicians code the data, the temporal nature of the data, and the imbalance in the data. Results show that state-of-the-art data mining techniques, including temporal data mining, are able to generate better predictive models than currently available in the literature.},
  author = {Kop, Reinier and Hoogendoorn, Mark and Moons, Leon M G and Numans, Matthijs E and ten Teije, Annette},
  booktitle = {Artificial Intelligence in Medicine, 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015, Proceedings AIME},
  file = {:Users/annette/Dropbox/AnnetteDropBoxVU/personal/Annette-www/papers-pdf/2015AIME-Kop.pdf:pdf},
  keywords = {colorectal cancer,data mining,machine learning},
  publisher = {Springer},
  title = {{On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer}},
  url = {http://www.cs.vu.nl/~annette/papers-pdf/2015AIMEKop.pdf},
  year = {2015}
}
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