Utilizing Data Mining for Predictive Modeling of Colorectal Cancer using Electronic Medical Records

M. Hoogendoorn, L.G. Moons, M.E. Numans, R.J. Sips

Research output: Contribution to JournalArticleAcademicpeer-review

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. © 2014 Springer International Publishing.
Original languageEnglish
Pages (from-to)132-141
JournalLecture Notes in Computer Science
Volume8609
DOIs
Publication statusPublished - 2014

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Predictive Modeling
Electronic medical equipment
Colorectal Cancer
Predictive Model
Data mining
Data Mining
Electronics
Globe
Decision trees
Decision tree
Curve

Bibliographical note

Proceedings title: Brain Informatics and Health 2014
Publisher: Springer
Editors: D. Ślezak, A.-H. Tan, J.F. Peters, L. Schwabe

Cite this

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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. {\circledC} 2014 Springer International Publishing.",
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Utilizing Data Mining for Predictive Modeling of Colorectal Cancer using Electronic Medical Records. / Hoogendoorn, M.; Moons, L.G.; Numans, M.E.; Sips, R.J.

In: Lecture Notes in Computer Science, Vol. 8609, 2014, p. 132-141.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Utilizing Data Mining for Predictive Modeling of Colorectal Cancer using Electronic Medical Records

AU - Hoogendoorn, M.

AU - Moons, L.G.

AU - Numans, M.E.

AU - Sips, R.J.

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AB - 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. © 2014 Springer International Publishing.

U2 - 10.1007/978-3-319-09891-3_13

DO - 10.1007/978-3-319-09891-3_13

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VL - 8609

SP - 132

EP - 141

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

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