Application of PrefixSpan Algorithms for Disease Pattern Analysis

Chi Jane Chen; Tun Wen Pai; Shih Syun Lin; Chun Chao Yeh; Min Hui Liu; Chao Hung Wang

doi:10.1109/ICS.2016.0062

Application of PrefixSpan Algorithms for Disease Pattern Analysis

Chi Jane Chen, Tun Wen Pai, Shih Syun Lin, Chun Chao Yeh, Min Hui Liu, Chao Hung Wang

School of Medicine

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

3 Scopus citations

Abstract

PrefixSpan is a pattern-growth method for mining sequential patterns, and it is employed in this research for identifying disease trajectory patterns based on frequent subsequence analysis. One of the most beneficial features of this algorithm is the maintainable characteristics of original data order, especially for effectively and efficiently searching sequential patterns within a huge database. In this study, a medical database was adopted for disease transition analysis, and seven chronic diseases including diabetes, hyperlipidemia, hypertension, cerebrovascular disease, kidney disease, heart failure, and chronic obstructive pulmonary disease were mainly considered. By employing PrefixSpan algorithms, the statistical results of various combinations of chronic diseases with specific orders could be observed and compared. The results shows that patients suffered from hypertension (HTN) and followed by hyperlipidemia (DP) possess the most proportion among all subjects with a percentage of 37% (89,058/241,017). All statistical results of different combinations of seven chronic diseases, transition order, and proportional ranking were shown and discussed.

Original language	English
Title of host publication	Proceedings - 2016 International Computer Symposium, ICS 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	274-278
Number of pages	5
ISBN (Electronic)	9781509034383
DOIs	https://doi.org/10.1109/ICS.2016.0062
State	Published - 16 02 2017
Event	2016 International Computer Symposium, ICS 2016 - Chiayi, Taiwan Duration: 15 12 2016 → 17 12 2016

Publication series

Name	Proceedings - 2016 International Computer Symposium, ICS 2016

Conference

Conference	2016 International Computer Symposium, ICS 2016
Country/Territory	Taiwan
City	Chiayi
Period	15/12/16 → 17/12/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

comorbidity
disease trajectory pattern
prefixSpan
sequential pattern mining

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICS.2016.0062

Cite this

Chen, C. J., Pai, T. W., Lin, S. S., Yeh, C. C., Liu, M. H., & Wang, C. H. (2017). Application of PrefixSpan Algorithms for Disease Pattern Analysis. In Proceedings - 2016 International Computer Symposium, ICS 2016 (pp. 274-278). Article 7858484 (Proceedings - 2016 International Computer Symposium, ICS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICS.2016.0062

@inproceedings{80f62aabf22441898996bfd31b4a3d09,

title = "Application of PrefixSpan Algorithms for Disease Pattern Analysis",

abstract = "PrefixSpan is a pattern-growth method for mining sequential patterns, and it is employed in this research for identifying disease trajectory patterns based on frequent subsequence analysis. One of the most beneficial features of this algorithm is the maintainable characteristics of original data order, especially for effectively and efficiently searching sequential patterns within a huge database. In this study, a medical database was adopted for disease transition analysis, and seven chronic diseases including diabetes, hyperlipidemia, hypertension, cerebrovascular disease, kidney disease, heart failure, and chronic obstructive pulmonary disease were mainly considered. By employing PrefixSpan algorithms, the statistical results of various combinations of chronic diseases with specific orders could be observed and compared. The results shows that patients suffered from hypertension (HTN) and followed by hyperlipidemia (DP) possess the most proportion among all subjects with a percentage of 37% (89,058/241,017). All statistical results of different combinations of seven chronic diseases, transition order, and proportional ranking were shown and discussed.",

keywords = "comorbidity, disease trajectory pattern, prefixSpan, sequential pattern mining",

author = "Chen, {Chi Jane} and Pai, {Tun Wen} and Lin, {Shih Syun} and Yeh, {Chun Chao} and Liu, {Min Hui} and Wang, {Chao Hung}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Computer Symposium, ICS 2016 ; Conference date: 15-12-2016 Through 17-12-2016",

year = "2017",

month = feb,

day = "16",

doi = "10.1109/ICS.2016.0062",

language = "英语",

series = "Proceedings - 2016 International Computer Symposium, ICS 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings - 2016 International Computer Symposium, ICS 2016",

address = "美国",

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Chen, CJ, Pai, TW, Lin, SS, Yeh, CC, Liu, MH & Wang, CH 2017, Application of PrefixSpan Algorithms for Disease Pattern Analysis. in Proceedings - 2016 International Computer Symposium, ICS 2016., 7858484, Proceedings - 2016 International Computer Symposium, ICS 2016, Institute of Electrical and Electronics Engineers Inc., pp. 274-278, 2016 International Computer Symposium, ICS 2016, Chiayi, Taiwan, 15/12/16. https://doi.org/10.1109/ICS.2016.0062

Application of PrefixSpan Algorithms for Disease Pattern Analysis. / Chen, Chi Jane; Pai, Tun Wen; Lin, Shih Syun et al.
Proceedings - 2016 International Computer Symposium, ICS 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 274-278 7858484 (Proceedings - 2016 International Computer Symposium, ICS 2016).

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

TY - GEN

T1 - Application of PrefixSpan Algorithms for Disease Pattern Analysis

AU - Chen, Chi Jane

AU - Pai, Tun Wen

AU - Lin, Shih Syun

AU - Yeh, Chun Chao

AU - Liu, Min Hui

AU - Wang, Chao Hung

PY - 2017/2/16

Y1 - 2017/2/16

N2 - PrefixSpan is a pattern-growth method for mining sequential patterns, and it is employed in this research for identifying disease trajectory patterns based on frequent subsequence analysis. One of the most beneficial features of this algorithm is the maintainable characteristics of original data order, especially for effectively and efficiently searching sequential patterns within a huge database. In this study, a medical database was adopted for disease transition analysis, and seven chronic diseases including diabetes, hyperlipidemia, hypertension, cerebrovascular disease, kidney disease, heart failure, and chronic obstructive pulmonary disease were mainly considered. By employing PrefixSpan algorithms, the statistical results of various combinations of chronic diseases with specific orders could be observed and compared. The results shows that patients suffered from hypertension (HTN) and followed by hyperlipidemia (DP) possess the most proportion among all subjects with a percentage of 37% (89,058/241,017). All statistical results of different combinations of seven chronic diseases, transition order, and proportional ranking were shown and discussed.

AB - PrefixSpan is a pattern-growth method for mining sequential patterns, and it is employed in this research for identifying disease trajectory patterns based on frequent subsequence analysis. One of the most beneficial features of this algorithm is the maintainable characteristics of original data order, especially for effectively and efficiently searching sequential patterns within a huge database. In this study, a medical database was adopted for disease transition analysis, and seven chronic diseases including diabetes, hyperlipidemia, hypertension, cerebrovascular disease, kidney disease, heart failure, and chronic obstructive pulmonary disease were mainly considered. By employing PrefixSpan algorithms, the statistical results of various combinations of chronic diseases with specific orders could be observed and compared. The results shows that patients suffered from hypertension (HTN) and followed by hyperlipidemia (DP) possess the most proportion among all subjects with a percentage of 37% (89,058/241,017). All statistical results of different combinations of seven chronic diseases, transition order, and proportional ranking were shown and discussed.

KW - comorbidity

KW - disease trajectory pattern

KW - prefixSpan

KW - sequential pattern mining

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ER -

Application of PrefixSpan Algorithms for Disease Pattern Analysis

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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