Association rule learning and frequent sequence mining of cancer diagnoses in New York State

Yu Wang; Fusheng Wang

doi:10.1007/978-3-319-67186-4_10

Association rule learning and frequent sequence mining of cancer diagnoses in New York State

Yu Wang
, Fusheng Wang

Biomedical Informatics

Stony Brook University

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

5 Scopus citations

Abstract

Analyzing large scale diagnosis histories of patients could help to discover comorbidity or disease progression patterns. Recently, open data initiatives make it possible to access statewide patient data at individual level, such as New York State SPARCS data. The goal of this study is to explore frequent disease co-occurrence and sequence patterns of cancer patients in New York State using SPARCS data. Our collection includes 18,208,830 discharge records from 1,565,237 patients with cancer-related diagnoses during 2011–2015. We use Apriori algorithm to discover top disease co-occurrences for common cancer categories based on support. We generate top frequent sequences of diagnoses with at least one cancer related diagnosis from patients’ diagnosis histories using the cSPADE algorithm. Our data driven approach provides essential knowledge to support the investigation of disease co-occurrence and progression patterns for improving the management of multiple diseases.

Original language	English
Title of host publication	Data Management and Analytics for Medicine and Healthcare - 3rd International Workshop, DMAH 2017 Held at VLDB 2017, Proceedings
Editors	Edmon Begoli, Gang Luo, Fusheng Wang
Publisher	Springer Verlag
Pages	121-135
Number of pages	15
ISBN (Print)	9783319671857
DOIs	https://doi.org/10.1007/978-3-319-67186-4_10
State	Published - 2017
Event	3rd International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2017 held in conjunction with the 43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany Duration: Sep 1 2017 → Sep 1 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10494 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2017 held in conjunction with the 43rd International Conference on Very Large Data Bases, VLDB 2017
Country/Territory	Germany
City	Munich
Period	09/1/17 → 09/1/17

Keywords

Association rule learning
Sequence mining
SPARCS

Access to Document

10.1007/978-3-319-67186-4_10

Cite this

Wang, Y., & Wang, F. (2017). Association rule learning and frequent sequence mining of cancer diagnoses in New York State. In E. Begoli, G. Luo, & F. Wang (Eds.), Data Management and Analytics for Medicine and Healthcare - 3rd International Workshop, DMAH 2017 Held at VLDB 2017, Proceedings (pp. 121-135). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10494 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67186-4_10

Wang, Yu ; Wang, Fusheng. / Association rule learning and frequent sequence mining of cancer diagnoses in New York State. Data Management and Analytics for Medicine and Healthcare - 3rd International Workshop, DMAH 2017 Held at VLDB 2017, Proceedings. editor / Edmon Begoli ; Gang Luo ; Fusheng Wang. Springer Verlag, 2017. pp. 121-135 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{75d402490d784c6480303b5ab61d705b,

title = "Association rule learning and frequent sequence mining of cancer diagnoses in New York State",

abstract = "Analyzing large scale diagnosis histories of patients could help to discover comorbidity or disease progression patterns. Recently, open data initiatives make it possible to access statewide patient data at individual level, such as New York State SPARCS data. The goal of this study is to explore frequent disease co-occurrence and sequence patterns of cancer patients in New York State using SPARCS data. Our collection includes 18,208,830 discharge records from 1,565,237 patients with cancer-related diagnoses during 2011–2015. We use Apriori algorithm to discover top disease co-occurrences for common cancer categories based on support. We generate top frequent sequences of diagnoses with at least one cancer related diagnosis from patients{\textquoteright} diagnosis histories using the cSPADE algorithm. Our data driven approach provides essential knowledge to support the investigation of disease co-occurrence and progression patterns for improving the management of multiple diseases.",

keywords = "Association rule learning, Sequence mining, SPARCS",

author = "Yu Wang and Fusheng Wang",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 3rd International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2017 held in conjunction with the 43rd International Conference on Very Large Data Bases, VLDB 2017 ; Conference date: 01-09-2017 Through 01-09-2017",

year = "2017",

doi = "10.1007/978-3-319-67186-4\_10",

language = "English",

isbn = "9783319671857",

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

publisher = "Springer Verlag",

pages = "121--135",

editor = "Edmon Begoli and Gang Luo and Fusheng Wang",

booktitle = "Data Management and Analytics for Medicine and Healthcare - 3rd International Workshop, DMAH 2017 Held at VLDB 2017, Proceedings",

}

Wang, Y & Wang, F 2017, Association rule learning and frequent sequence mining of cancer diagnoses in New York State. in E Begoli, G Luo & F Wang (eds), Data Management and Analytics for Medicine and Healthcare - 3rd International Workshop, DMAH 2017 Held at VLDB 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10494 LNCS, Springer Verlag, pp. 121-135, 3rd International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2017 held in conjunction with the 43rd International Conference on Very Large Data Bases, VLDB 2017, Munich, Germany, 09/1/17. https://doi.org/10.1007/978-3-319-67186-4_10

Association rule learning and frequent sequence mining of cancer diagnoses in New York State. / Wang, Yu; Wang, Fusheng.
Data Management and Analytics for Medicine and Healthcare - 3rd International Workshop, DMAH 2017 Held at VLDB 2017, Proceedings. ed. / Edmon Begoli; Gang Luo; Fusheng Wang. Springer Verlag, 2017. p. 121-135 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10494 LNCS).

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

TY - GEN

T1 - Association rule learning and frequent sequence mining of cancer diagnoses in New York State

AU - Wang, Yu

AU - Wang, Fusheng

PY - 2017

Y1 - 2017

N2 - Analyzing large scale diagnosis histories of patients could help to discover comorbidity or disease progression patterns. Recently, open data initiatives make it possible to access statewide patient data at individual level, such as New York State SPARCS data. The goal of this study is to explore frequent disease co-occurrence and sequence patterns of cancer patients in New York State using SPARCS data. Our collection includes 18,208,830 discharge records from 1,565,237 patients with cancer-related diagnoses during 2011–2015. We use Apriori algorithm to discover top disease co-occurrences for common cancer categories based on support. We generate top frequent sequences of diagnoses with at least one cancer related diagnosis from patients’ diagnosis histories using the cSPADE algorithm. Our data driven approach provides essential knowledge to support the investigation of disease co-occurrence and progression patterns for improving the management of multiple diseases.

AB - Analyzing large scale diagnosis histories of patients could help to discover comorbidity or disease progression patterns. Recently, open data initiatives make it possible to access statewide patient data at individual level, such as New York State SPARCS data. The goal of this study is to explore frequent disease co-occurrence and sequence patterns of cancer patients in New York State using SPARCS data. Our collection includes 18,208,830 discharge records from 1,565,237 patients with cancer-related diagnoses during 2011–2015. We use Apriori algorithm to discover top disease co-occurrences for common cancer categories based on support. We generate top frequent sequences of diagnoses with at least one cancer related diagnosis from patients’ diagnosis histories using the cSPADE algorithm. Our data driven approach provides essential knowledge to support the investigation of disease co-occurrence and progression patterns for improving the management of multiple diseases.

KW - Association rule learning

KW - Sequence mining

KW - SPARCS

UR - https://www.scopus.com/pages/publications/85028981846

U2 - 10.1007/978-3-319-67186-4_10

DO - 10.1007/978-3-319-67186-4_10

M3 - Conference contribution

AN - SCOPUS:85028981846

SN - 9783319671857

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

SP - 121

EP - 135

BT - Data Management and Analytics for Medicine and Healthcare - 3rd International Workshop, DMAH 2017 Held at VLDB 2017, Proceedings

A2 - Begoli, Edmon

A2 - Luo, Gang

A2 - Wang, Fusheng

PB - Springer Verlag

T2 - 3rd International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2017 held in conjunction with the 43rd International Conference on Very Large Data Bases, VLDB 2017

Y2 - 1 September 2017 through 1 September 2017

ER -

Wang Y, Wang F. Association rule learning and frequent sequence mining of cancer diagnoses in New York State. In Begoli E, Luo G, Wang F, editors, Data Management and Analytics for Medicine and Healthcare - 3rd International Workshop, DMAH 2017 Held at VLDB 2017, Proceedings. Springer Verlag. 2017. p. 121-135. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67186-4_10

Association rule learning and frequent sequence mining of cancer diagnoses in New York State

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this