Analyzing differences in operational disease definitions using ontological modeling

Linda Peelen; Michel C A Klein; Stefan Schlobach; Nicolette F. De Keizer; Niels Peek

Analyzing differences in operational disease definitions using ontological modeling

Linda Peelen^*, Michel C A Klein, Stefan Schlobach, Nicolette F. De Keizer, Niels Peek

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

Abstract

In medicine, there are many diseases which cannot be precisely characterized but are considered as natural kinds. In the communication between health care professionals, this is generally not problematic. In biomedical research, however, crisp definitions are required to unambiguously distinguish patients with and without the disease. In practice, this results in different operational definitions being in use for a single disease. This paper presents an approach to compare different operational definitions of a single disease using ontological modeling. The approach is illustrated with a case-study in the area of severe sepsis.

Original language	English
Title of host publication	Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings
Pages	297-302
Number of pages	6
Volume	4594 LNAI
Publication status	Published - 2007
Event	11th Conference on Artificial Intelligence in Medicine, AIME 2007 - Amsterdam, Netherlands Duration: 7 Jul 2007 → 11 Jul 2007

Conference

Conference	11th Conference on Artificial Intelligence in Medicine, AIME 2007
Country/Territory	Netherlands
City	Amsterdam
Period	7/07/07 → 11/07/07

Cite this

@inproceedings{c86117ff773f4dfaa469857cb26f224f,

title = "Analyzing differences in operational disease definitions using ontological modeling",

abstract = "In medicine, there are many diseases which cannot be precisely characterized but are considered as natural kinds. In the communication between health care professionals, this is generally not problematic. In biomedical research, however, crisp definitions are required to unambiguously distinguish patients with and without the disease. In practice, this results in different operational definitions being in use for a single disease. This paper presents an approach to compare different operational definitions of a single disease using ontological modeling. The approach is illustrated with a case-study in the area of severe sepsis.",

author = "Linda Peelen and Klein, {Michel C A} and Stefan Schlobach and {De Keizer}, {Nicolette F.} and Niels Peek",

year = "2007",

language = "English",

isbn = "3540735984",

volume = "4594 LNAI",

pages = "297--302",

booktitle = "Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings",

note = "11th Conference on Artificial Intelligence in Medicine, AIME 2007 ; Conference date: 07-07-2007 Through 11-07-2007",

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Peelen, L, Klein, MCA, Schlobach, S, De Keizer, NF & Peek, N 2007, Analyzing differences in operational disease definitions using ontological modeling. in Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings. vol. 4594 LNAI, pp. 297-302, 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Amsterdam, Netherlands, 7/07/07.

Analyzing differences in operational disease definitions using ontological modeling. / Peelen, Linda; Klein, Michel C A; Schlobach, Stefan et al.
Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings. Vol. 4594 LNAI 2007. p. 297-302.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Analyzing differences in operational disease definitions using ontological modeling

AU - Peelen, Linda

AU - Klein, Michel C A

AU - Schlobach, Stefan

AU - De Keizer, Nicolette F.

AU - Peek, Niels

PY - 2007

Y1 - 2007

N2 - In medicine, there are many diseases which cannot be precisely characterized but are considered as natural kinds. In the communication between health care professionals, this is generally not problematic. In biomedical research, however, crisp definitions are required to unambiguously distinguish patients with and without the disease. In practice, this results in different operational definitions being in use for a single disease. This paper presents an approach to compare different operational definitions of a single disease using ontological modeling. The approach is illustrated with a case-study in the area of severe sepsis.

AB - In medicine, there are many diseases which cannot be precisely characterized but are considered as natural kinds. In the communication between health care professionals, this is generally not problematic. In biomedical research, however, crisp definitions are required to unambiguously distinguish patients with and without the disease. In practice, this results in different operational definitions being in use for a single disease. This paper presents an approach to compare different operational definitions of a single disease using ontological modeling. The approach is illustrated with a case-study in the area of severe sepsis.

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M3 - Conference contribution

AN - SCOPUS:35148851997

SN - 3540735984

SN - 9783540735984

VL - 4594 LNAI

SP - 297

EP - 302

BT - Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings

T2 - 11th Conference on Artificial Intelligence in Medicine, AIME 2007

Y2 - 7 July 2007 through 11 July 2007

ER -

Analyzing differences in operational disease definitions using ontological modeling

Abstract

Conference

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