Mining temporal patterns from health care data

Weiqiang Lin; Mehmet A. Orgun; Graham J. Williams

Mining temporal patterns from health care data

Weiqiang Lin^*, Mehmet A. Orgun, Graham J. Williams

^*Corresponding author for this work

Department of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution

8 Citations (Scopus)

Abstract

This paper describes temporal data mining techniques for extracting information from temporal health records consisting of a time series of elderly diabetic patients' tests.We propose a data mining procedure to analyse these time sequences in three steps to identify patterns from any longitudinal data set. The first step is a structure-based search using wavelets to find pattern structures. The second step employs a value-based search over the discovered patterns using the statistical distribution of data values. The third step combines the results from the first two steps to form a hybrid model. The hybrid model has the expressive power of both wavelet analysis and the statistical distribution of the values. Global patterns are therefore identified.

Original language	English
Title of host publication	Data Warehousing and Knowledge Discovery - 4th International Conference, DaWaK 2002, Proceedings
Editors	Y. Kambayashi, W. Winiwarter, M. Arikawa
Place of Publication	Berlin; Heidelberg
Publisher	Springer, Springer Nature
Pages	222-231
Number of pages	10
Volume	2454 LNCS
ISBN (Print)	3540441239, 9783540441236
Publication status	Published - 2002
Event	4th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2002 - Aix-en-Provence, France Duration: 4 Sep 2002 → 6 Sep 2002

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2454 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	4th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2002
Country	France
City	Aix-en-Provence
Period	4/09/02 → 6/09/02

Access to Document

Link to publication in Scopus

Fingerprint Dive into the research topics of 'Mining temporal patterns from health care data'. Together they form a unique fingerprint.

Cite this

Lin, W., Orgun, M. A., & Williams, G. J. (2002). Mining temporal patterns from health care data. In Y. Kambayashi, W. Winiwarter, & M. Arikawa (Eds.), Data Warehousing and Knowledge Discovery - 4th International Conference, DaWaK 2002, Proceedings (Vol. 2454 LNCS, pp. 222-231). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2454 LNCS). Berlin; Heidelberg: Springer, Springer Nature.

Lin, Weiqiang ; Orgun, Mehmet A. ; Williams, Graham J. / Mining temporal patterns from health care data. Data Warehousing and Knowledge Discovery - 4th International Conference, DaWaK 2002, Proceedings. editor / Y. Kambayashi ; W. Winiwarter ; M. Arikawa. Vol. 2454 LNCS Berlin; Heidelberg : Springer, Springer Nature, 2002. pp. 222-231 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5ee4532bcff34cfab62ebb8f74e018f9,

title = "Mining temporal patterns from health care data",

abstract = "This paper describes temporal data mining techniques for extracting information from temporal health records consisting of a time series of elderly diabetic patients' tests.We propose a data mining procedure to analyse these time sequences in three steps to identify patterns from any longitudinal data set. The first step is a structure-based search using wavelets to find pattern structures. The second step employs a value-based search over the discovered patterns using the statistical distribution of data values. The third step combines the results from the first two steps to form a hybrid model. The hybrid model has the expressive power of both wavelet analysis and the statistical distribution of the values. Global patterns are therefore identified.",

author = "Weiqiang Lin and Orgun, {Mehmet A.} and Williams, {Graham J.}",

year = "2002",

language = "English",

isbn = "3540441239",

volume = "2454 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer, Springer Nature",

pages = "222--231",

editor = "Y. Kambayashi and W. Winiwarter and M. Arikawa",

booktitle = "Data Warehousing and Knowledge Discovery - 4th International Conference, DaWaK 2002, Proceedings",

address = "United States",

}

Lin, W, Orgun, MA & Williams, GJ 2002, Mining temporal patterns from health care data. in Y Kambayashi, W Winiwarter & M Arikawa (eds), Data Warehousing and Knowledge Discovery - 4th International Conference, DaWaK 2002, Proceedings. vol. 2454 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2454 LNCS, Springer, Springer Nature, Berlin; Heidelberg, pp. 222-231, 4th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2002, Aix-en-Provence, France, 4/09/02.

Mining temporal patterns from health care data. / Lin, Weiqiang; Orgun, Mehmet A.; Williams, Graham J.

Data Warehousing and Knowledge Discovery - 4th International Conference, DaWaK 2002, Proceedings. ed. / Y. Kambayashi; W. Winiwarter; M. Arikawa. Vol. 2454 LNCS Berlin; Heidelberg : Springer, Springer Nature, 2002. p. 222-231 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2454 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution

TY - GEN

T1 - Mining temporal patterns from health care data

AU - Lin, Weiqiang

AU - Orgun, Mehmet A.

AU - Williams, Graham J.

PY - 2002

Y1 - 2002

N2 - This paper describes temporal data mining techniques for extracting information from temporal health records consisting of a time series of elderly diabetic patients' tests.We propose a data mining procedure to analyse these time sequences in three steps to identify patterns from any longitudinal data set. The first step is a structure-based search using wavelets to find pattern structures. The second step employs a value-based search over the discovered patterns using the statistical distribution of data values. The third step combines the results from the first two steps to form a hybrid model. The hybrid model has the expressive power of both wavelet analysis and the statistical distribution of the values. Global patterns are therefore identified.

AB - This paper describes temporal data mining techniques for extracting information from temporal health records consisting of a time series of elderly diabetic patients' tests.We propose a data mining procedure to analyse these time sequences in three steps to identify patterns from any longitudinal data set. The first step is a structure-based search using wavelets to find pattern structures. The second step employs a value-based search over the discovered patterns using the statistical distribution of data values. The third step combines the results from the first two steps to form a hybrid model. The hybrid model has the expressive power of both wavelet analysis and the statistical distribution of the values. Global patterns are therefore identified.

UR - http://www.scopus.com/inward/record.url?scp=84864863551&partnerID=8YFLogxK

M3 - Conference proceeding contribution

SN - 3540441239

SN - 9783540441236

VL - 2454 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 222

EP - 231

BT - Data Warehousing and Knowledge Discovery - 4th International Conference, DaWaK 2002, Proceedings

A2 - Kambayashi, Y.

A2 - Winiwarter, W.

A2 - Arikawa, M.

PB - Springer, Springer Nature

CY - Berlin; Heidelberg

ER -

Lin W, Orgun MA, Williams GJ. Mining temporal patterns from health care data. In Kambayashi Y, Winiwarter W, Arikawa M, editors, Data Warehousing and Knowledge Discovery - 4th International Conference, DaWaK 2002, Proceedings. Vol. 2454 LNCS. Berlin; Heidelberg: Springer, Springer Nature. 2002. p. 222-231. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).