Finding Indicator Diseases of Psychiatric Disorders in BigData using Clustered Association Rule Mining

Markus Bertl; Mahtab Shahin; Peeter Ross; Dirk Draheim

doi:10.1145/3555776.3577594

Finding Indicator Diseases of Psychiatric Disorders in BigData using Clustered Association Rule Mining

Markus Bertl
, Mahtab Shahin
, Peeter Ross
, Dirk Draheim

Publikation: Beitrag in Buch/Konferenzband › Beitrag in Konferenzband

Abstract

Psychiatric disorders represent critical non-communicable diseases of the 21st century and are ranked as the leading cause of years lived with disabilities. Nevertheless, data that could be used to improve our understanding of psychiatric diseases remain underutilized. In this research, we apply clustered association rule mining to find comorbidities and indicator diseases for patients with psychiatric illnesses. The model was trained with health insurance billing data from 60,115 patients with a total of 904,821 ICD-10 coded diseases. Nine association rules were found without clustering, 40 with clustering of F diagnoses. The approach proves suitable for further use in the implementation of indicator-based digital decision support systems in psychiatry.

Originalsprache	Englisch
Titel des Sammelwerks	SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
Untertitel des Sammelwerks	Tallinn, Estonia, March 27-31 2023
Erscheinungsort	New York
Verlag	Association for Computing Machinery
Seiten	826-833
Seitenumfang	8
ISBN (elektronisch)	978-1-4503-9517-5
DOIs	https://doi.org/10.1145/3555776.3577594
Publikationsstatus	Veröffentlicht - 27 März 2023
Extern publiziert	Ja
Veranstaltung	38th Annual ACM Symposium on Applied Computing, SAC 2023 - Tallinn, Estland Dauer: 27 März 2023 → 31 März 2023

Konferenz

Konferenz	38th Annual ACM Symposium on Applied Computing, SAC 2023
Land/Gebiet	Estland
Ort	Tallinn
Zeitraum	27/03/23 → 31/03/23

Bibliographische Notiz

Publisher Copyright:
© 2023 ACM.

Zugriff auf Dokument

10.1145/3555776.3577594Lizenz: Nicht angegeben

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

Zitat

@inproceedings{ac87fea1bab4474ba06a50a2131891ec,

title = "Finding Indicator Diseases of Psychiatric Disorders in BigData using Clustered Association Rule Mining",

abstract = "Psychiatric disorders represent critical non-communicable diseases of the 21st century and are ranked as the leading cause of years lived with disabilities. Nevertheless, data that could be used to improve our understanding of psychiatric diseases remain underutilized. In this research, we apply clustered association rule mining to find comorbidities and indicator diseases for patients with psychiatric illnesses. The model was trained with health insurance billing data from 60,115 patients with a total of 904,821 ICD-10 coded diseases. Nine association rules were found without clustering, 40 with clustering of F diagnoses. The approach proves suitable for further use in the implementation of indicator-based digital decision support systems in psychiatry.",

keywords = "artificial intelligence (AI), association rule mining (ARM), comorbidity, decision support systems (DSS), electronic health record (EHR), explainable artificial intelligence (XAI), health insurance data, indicator diseases, machine learning (ML), psychiatry",

author = "Markus Bertl and Mahtab Shahin and Peeter Ross and Dirk Draheim",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 38th Annual ACM Symposium on Applied Computing, SAC 2023 ; Conference date: 27-03-2023 Through 31-03-2023",

year = "2023",

month = mar,

day = "27",

doi = "10.1145/3555776.3577594",

language = "English",

pages = "826--833",

booktitle = "SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing",

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Bertl, M, Shahin, M, Ross, P & Draheim, D 2023, Finding Indicator Diseases of Psychiatric Disorders in BigData using Clustered Association Rule Mining. in SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing: Tallinn, Estonia, March 27-31 2023. Association for Computing Machinery, New York, S. 826-833, 38th Annual ACM Symposium on Applied Computing, SAC 2023, Tallinn, Estland, 27/03/23. https://doi.org/10.1145/3555776.3577594

Finding Indicator Diseases of Psychiatric Disorders in BigData using Clustered Association Rule Mining. / Bertl, Markus; Shahin, Mahtab; Ross, Peeter et al.
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing: Tallinn, Estonia, March 27-31 2023. New York: Association for Computing Machinery, 2023. S. 826-833.

Publikation: Beitrag in Buch/Konferenzband › Beitrag in Konferenzband

TY - GEN

T1 - Finding Indicator Diseases of Psychiatric Disorders in BigData using Clustered Association Rule Mining

AU - Bertl, Markus

AU - Shahin, Mahtab

AU - Ross, Peeter

AU - Draheim, Dirk

PY - 2023/3/27

Y1 - 2023/3/27

N2 - Psychiatric disorders represent critical non-communicable diseases of the 21st century and are ranked as the leading cause of years lived with disabilities. Nevertheless, data that could be used to improve our understanding of psychiatric diseases remain underutilized. In this research, we apply clustered association rule mining to find comorbidities and indicator diseases for patients with psychiatric illnesses. The model was trained with health insurance billing data from 60,115 patients with a total of 904,821 ICD-10 coded diseases. Nine association rules were found without clustering, 40 with clustering of F diagnoses. The approach proves suitable for further use in the implementation of indicator-based digital decision support systems in psychiatry.

AB - Psychiatric disorders represent critical non-communicable diseases of the 21st century and are ranked as the leading cause of years lived with disabilities. Nevertheless, data that could be used to improve our understanding of psychiatric diseases remain underutilized. In this research, we apply clustered association rule mining to find comorbidities and indicator diseases for patients with psychiatric illnesses. The model was trained with health insurance billing data from 60,115 patients with a total of 904,821 ICD-10 coded diseases. Nine association rules were found without clustering, 40 with clustering of F diagnoses. The approach proves suitable for further use in the implementation of indicator-based digital decision support systems in psychiatry.

KW - artificial intelligence (AI)

KW - association rule mining (ARM)

KW - comorbidity

KW - decision support systems (DSS)

KW - electronic health record (EHR)

KW - explainable artificial intelligence (XAI)

KW - health insurance data

KW - indicator diseases

KW - machine learning (ML)

KW - psychiatry

UR - https://www.scopus.com/pages/publications/85162905543

U2 - 10.1145/3555776.3577594

DO - 10.1145/3555776.3577594

M3 - Contribution to conference proceedings

AN - SCOPUS:85162905543

SP - 826

EP - 833

BT - SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing

PB - Association for Computing Machinery

CY - New York

T2 - 38th Annual ACM Symposium on Applied Computing, SAC 2023

Y2 - 27 March 2023 through 31 March 2023

ER -