research-article

Understanding chronic disease comorbidities from baseline networks: knowledge discovery utilising administrative healthcare data

Authors:

Shahadat Uddin,

Uma SrinivasanAuthors Info & Claims

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

Article No.: 57, Pages 1 - 9

https://doi.org/10.1145/3014812.3014871

Published: 31 January 2017 Publication History

Abstract

Hospitals routinely collect admitted patients' data for administrative purposes and for reporting to the government and health insurers. These heterogeneous and mostly untapped data contain rich semantic information about patients' health conditions in the form of standard disease codes. These traces of clinical information can be aggregated over patients to understand how their health progresses over time. When applied on particular chronic disease patients, this approach can potentially help in understanding chronic disease comorbidities as well as in the knowledge discovery on how the chronic disease progresses over time. In this paper, we propose a network-based approach to extract semantic information from hospital administrative data in order to develop a representation of chronic disease progression specifically - type 2 diabetes. We then propose measures for attribution adjustment that ranks the more prevalent comorbidities in chronic patients higher, compared to the non-chronic ones. We also have applied the framework on the administrative data of 2,760 sampled patients to understand how diabetes progresses over time through different comorbidities. This understanding can be effectively converted to actionable intelligence that can be useful in the formulation of better health policy and resource management.

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Cited By

Quang TLe NLe B(2022)Predictive Modelling of Diseases Based on a Network and Machine Learning ApproachRecent Challenges in Intelligent Information and Database Systems10.1007/978-981-19-8234-7_50(641-654)Online publication date: 24-Nov-2022
https://doi.org/10.1007/978-981-19-8234-7_50
Khan ASrinivasan UUddin S(2019)Development and exploration of polymedication network from Pharmaceutical and Medicare Benefits Scheme dataProceedings of the Australasian Computer Science Week Multiconference10.1145/3290688.3290738(1-6)Online publication date: 29-Jan-2019
https://dl.acm.org/doi/10.1145/3290688.3290738
Viloria ALis-Gutiérrez JGaitán-Angulo MGodoy AMoreno GKamatkar S(2018)Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data)Data Mining and Big Data10.1007/978-3-319-93803-5_63(670-679)Online publication date: 10-Jun-2018
https://doi.org/10.1007/978-3-319-93803-5_63

Index Terms

Understanding chronic disease comorbidities from baseline networks: knowledge discovery utilising administrative healthcare data

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cover image ACM Other conferences

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

January 2017

615 pages

ISBN:9781450347686

DOI:10.1145/3014812

Copyright © 2017 ACM.

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Published: 31 January 2017

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ACSW 2017: Australasian Computer Science Week 2017

January 30 - February 3, 2017

Geelong, Australia

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ACSW '17 Paper Acceptance Rate 78 of 156 submissions, 50%;

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Cited By

Quang TLe NLe B(2022)Predictive Modelling of Diseases Based on a Network and Machine Learning ApproachRecent Challenges in Intelligent Information and Database Systems10.1007/978-981-19-8234-7_50(641-654)Online publication date: 24-Nov-2022
https://doi.org/10.1007/978-981-19-8234-7_50
Khan ASrinivasan UUddin S(2019)Development and exploration of polymedication network from Pharmaceutical and Medicare Benefits Scheme dataProceedings of the Australasian Computer Science Week Multiconference10.1145/3290688.3290738(1-6)Online publication date: 29-Jan-2019
https://dl.acm.org/doi/10.1145/3290688.3290738
Viloria ALis-Gutiérrez JGaitán-Angulo MGodoy AMoreno GKamatkar S(2018)Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data)Data Mining and Big Data10.1007/978-3-319-93803-5_63(670-679)Online publication date: 10-Jun-2018
https://doi.org/10.1007/978-3-319-93803-5_63

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