@inproceedings{44ed4d032ab84ad583385e3e247020de,
title = "Utilizing data mining for predictive modeling of colorectal cancer using electronic medical records",
abstract = "Colorectal cancer (CRC) is a relatively common cause of death around the globe. Predictive models for the development of CRC could be highly valuable and could facilitate an early diagnosis and increased survival rates. Currently available predictive models are improving, but do not fully utilize the wealth of data available about patients in routine care nor do they take advantage of the developments in the area of data mining. In this paper, a first attempt to generate a predictive model using the CHAID decision tree learner based on anonymously extracted Electronic Medical Records is reported, showing an area under the curve (AUC) of .839 for the adult population and .702 for the age group between 55 and 75.",
author = "Mark Hoogendoorn and Moons, {Leon M G} and Numans, {Mattijs E.} and Sips, {Robert Jan}",
year = "2014",
month = jan,
day = "1",
doi = "10.1007/978-3-319-09891-3_13",
language = "English",
isbn = "9783319098906",
volume = "8609 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "132--141",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
note = "2014 International Conference on Brain Informatics and Health, BIH 2014 ; Conference date: 11-08-2014 Through 14-08-2014",
}