Adapting graph theory and social network measures on healthcare data : a new framework to understand chronic disease progression

Arif Khan; Shahadat Uddin; Uma Srinivasan

doi:10.1145/2843043.2843380

Adapting graph theory and social network measures on healthcare data : a new framework to understand chronic disease progression

Arif Khan, Shahadat Uddin, Uma Srinivasan

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

12 Citations (Scopus)

Abstract

Governments all over the world are concerned about the disease burden caused by chronic conditions. A significant portion of this comes from potentially preventable hospital admissions. By adopting preventive measures, these admissions can be avoided which in turn can reduce cost and health risk, further benefitting the funders, providers and patients as well. One potential approach can be to look at healthcare information system, more specifically - hospital admission data that carries rich semantic information. In this paper we present a novel framework to apply social network and graph theoretic methods on modern healthcare data to analyse and understand chronic disease progression to enable all stakeholders to take appropriate preventive measures.

Original language	English
Title of host publication	ACSW '16: Proceedings of the Australasian Computer Science Week Multiconference, 1-5 February 2016, Canberra, A.C.T.
Publisher	Association for Computing Machinery
Number of pages	7
ISBN (Print)	9781450340427
DOIs	https://doi.org/10.1145/2843043.2843380
Publication status	Published - 2016
Event	Australasian Workshop on Health Informatics and Knowledge Management - Duration: 2 Feb 2016 → …

Conference

Conference	Australasian Workshop on Health Informatics and Knowledge Management
Period	2/02/16 → …

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10.1145/2843043.2843380

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Khan, A., Uddin, S., & Srinivasan, U. (2016). Adapting graph theory and social network measures on healthcare data : a new framework to understand chronic disease progression. In ACSW '16: Proceedings of the Australasian Computer Science Week Multiconference, 1-5 February 2016, Canberra, A.C.T. Association for Computing Machinery. https://doi.org/10.1145/2843043.2843380

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abstract = "Governments all over the world are concerned about the disease burden caused by chronic conditions. A significant portion of this comes from potentially preventable hospital admissions. By adopting preventive measures, these admissions can be avoided which in turn can reduce cost and health risk, further benefitting the funders, providers and patients as well. One potential approach can be to look at healthcare information system, more specifically - hospital admission data that carries rich semantic information. In this paper we present a novel framework to apply social network and graph theoretic methods on modern healthcare data to analyse and understand chronic disease progression to enable all stakeholders to take appropriate preventive measures.",

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booktitle = "ACSW '16: Proceedings of the Australasian Computer Science Week Multiconference, 1-5 February 2016, Canberra, A.C.T.",

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Khan, A, Uddin, S & Srinivasan, U 2016, Adapting graph theory and social network measures on healthcare data : a new framework to understand chronic disease progression. in ACSW '16: Proceedings of the Australasian Computer Science Week Multiconference, 1-5 February 2016, Canberra, A.C.T.. Association for Computing Machinery, Australasian Workshop on Health Informatics and Knowledge Management, 2/02/16. https://doi.org/10.1145/2843043.2843380

Adapting graph theory and social network measures on healthcare data : a new framework to understand chronic disease progression. / Khan, Arif; Uddin, Shahadat; Srinivasan, Uma.
ACSW '16: Proceedings of the Australasian Computer Science Week Multiconference, 1-5 February 2016, Canberra, A.C.T.. Association for Computing Machinery, 2016.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

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AU - Srinivasan, Uma

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N2 - Governments all over the world are concerned about the disease burden caused by chronic conditions. A significant portion of this comes from potentially preventable hospital admissions. By adopting preventive measures, these admissions can be avoided which in turn can reduce cost and health risk, further benefitting the funders, providers and patients as well. One potential approach can be to look at healthcare information system, more specifically - hospital admission data that carries rich semantic information. In this paper we present a novel framework to apply social network and graph theoretic methods on modern healthcare data to analyse and understand chronic disease progression to enable all stakeholders to take appropriate preventive measures.

AB - Governments all over the world are concerned about the disease burden caused by chronic conditions. A significant portion of this comes from potentially preventable hospital admissions. By adopting preventive measures, these admissions can be avoided which in turn can reduce cost and health risk, further benefitting the funders, providers and patients as well. One potential approach can be to look at healthcare information system, more specifically - hospital admission data that carries rich semantic information. In this paper we present a novel framework to apply social network and graph theoretic methods on modern healthcare data to analyse and understand chronic disease progression to enable all stakeholders to take appropriate preventive measures.

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DO - 10.1145/2843043.2843380

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BT - ACSW '16: Proceedings of the Australasian Computer Science Week Multiconference, 1-5 February 2016, Canberra, A.C.T.

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ER -

Adapting graph theory and social network measures on healthcare data : a new framework to understand chronic disease progression

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