On Multidimensional Research Framework for Managing the Complex Disease Ecosystems

Cancer is a deadly complex disease. Poorly aligned institutions, their data sources, and doctor-patient disengagement motivate us, strengthening collaborative cancer research. The research explores informatics solutions, by collating various cancer-linked data attributes and interconnecting them wit...

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Main Authors: Nimmagadda, Shastri, Reiners, Torsten, Mani, N., Wood, L.
Format: Conference Paper
Published: 2019
Online Access:http://hdl.handle.net/20.500.11937/81145
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author Nimmagadda, Shastri
Reiners, Torsten
Mani, N.
Wood, L.
author_facet Nimmagadda, Shastri
Reiners, Torsten
Mani, N.
Wood, L.
author_sort Nimmagadda, Shastri
building Curtin Institutional Repository
collection Online Access
description Cancer is a deadly complex disease. Poorly aligned institutions, their data sources, and doctor-patient disengagement motivate us, strengthening collaborative cancer research. The research explores informatics solutions, by collating various cancer-linked data attributes and interconnecting them with spatial-temporal dimensions. The study is aimed at developing a methodological framework and investigating the open source empirical cancer data that involve diverse human ecosystems. We propose a Multidimensional Cancer Research Framework (MUCARF), responding to challenges of reporting, documenting and collaborating the data sources that characterize different cancer ailments including their worldwide causalities. Information system artefacts built based on cancer-domain ontologies, with similar and dissimilar attributes are integrated into MUCARF to dig the diagnostic cancer metadata views in new knowledge domains. Metadata model that replicates the design and development of MUCARF is a useful analytic therapy to examine and mitigate high rates of cancers and their types as preventive care and cancer disease management.
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spelling curtin-20.500.11937-811452021-02-10T05:52:51Z On Multidimensional Research Framework for Managing the Complex Disease Ecosystems Nimmagadda, Shastri Reiners, Torsten Mani, N. Wood, L. Cancer is a deadly complex disease. Poorly aligned institutions, their data sources, and doctor-patient disengagement motivate us, strengthening collaborative cancer research. The research explores informatics solutions, by collating various cancer-linked data attributes and interconnecting them with spatial-temporal dimensions. The study is aimed at developing a methodological framework and investigating the open source empirical cancer data that involve diverse human ecosystems. We propose a Multidimensional Cancer Research Framework (MUCARF), responding to challenges of reporting, documenting and collaborating the data sources that characterize different cancer ailments including their worldwide causalities. Information system artefacts built based on cancer-domain ontologies, with similar and dissimilar attributes are integrated into MUCARF to dig the diagnostic cancer metadata views in new knowledge domains. Metadata model that replicates the design and development of MUCARF is a useful analytic therapy to examine and mitigate high rates of cancers and their types as preventive care and cancer disease management. 2019 Conference Paper http://hdl.handle.net/20.500.11937/81145 restricted
spellingShingle Nimmagadda, Shastri
Reiners, Torsten
Mani, N.
Wood, L.
On Multidimensional Research Framework for Managing the Complex Disease Ecosystems
title On Multidimensional Research Framework for Managing the Complex Disease Ecosystems
title_full On Multidimensional Research Framework for Managing the Complex Disease Ecosystems
title_fullStr On Multidimensional Research Framework for Managing the Complex Disease Ecosystems
title_full_unstemmed On Multidimensional Research Framework for Managing the Complex Disease Ecosystems
title_short On Multidimensional Research Framework for Managing the Complex Disease Ecosystems
title_sort on multidimensional research framework for managing the complex disease ecosystems
url http://hdl.handle.net/20.500.11937/81145