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

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

doi:10.1007/978-3-319-09891-3_13

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 Journal › Article › Academic › peer-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 language	English
Pages (from-to)	132-141
Journal	Lecture Notes in Computer Science
Volume	8609
DOIs	https://doi.org/10.1007/978-3-319-09891-3_13
Publication status	Published - 2014

Bibliographical note

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

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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. {\textcopyright} 2014 Springer International Publishing.",

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note = "Proceedings title: Brain Informatics and Health 2014 Publisher: Springer Editors: D. {\'S}lezak, A.-H. Tan, J.F. Peters, L. Schwabe",

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AU - Hoogendoorn, M.

AU - Moons, L.G.

AU - Numans, M.E.

AU - Sips, R.J.

N1 - Proceedings title: Brain Informatics and Health 2014 Publisher: Springer Editors: D. Ślezak, A.-H. Tan, J.F. Peters, L. Schwabe

PY - 2014

Y1 - 2014

N2 - 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.

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

M3 - Article

VL - 8609

SP - 132

EP - 141

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -

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

Abstract

Bibliographical note

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