Modelling of cancer patient records: A structured approach to data mining and visual analytics
Modelling of cancer patient records: A structured approach to data mining and visual analytics
This research presents a methodology for health data analytics through a case study for modelling cancer patient records. Timeline-structured clinical data systems represent a new approach to the understanding of the relationship between clinical activity, disease pathologies and health outcomes. The novel Southampton Breast Cancer Data System contains episode and timeline-structured records on >17,000 patients who have been treated in University Hospital Southampton and affiliated hospitals since the late 1970s. The system is under continuous development and validation. Modern data mining software and visual analytics tools permit new insights into temporally-structured clinical data. The challenges and outcomes of the application of such software-based systems to this complex data environment are reported here. The core data was anonymised and put through a series of pre-processing exercises to identify and exclude anomalous and erroneous data, before restructuring within a remote data warehouse. A range of approaches was tested on the resulting dataset including multi-dimensional modelling, sequential patterns mining and classification. Visual analytics software has enabled the comparison of survival times and surgical treatments. The systems tested proved to be powerful in identifying episode sequencing patterns which were consistent with real-world clinical outcomes. It is concluded that, subject to further refinement and selection, modern data mining techniques can be applied to large and heterogeneous clinical datasets to inform decision making.
Clinical data environment, Data mining, Decision support, Electronic patient records, Health information systems, Visual analytics
30-51
Lu, Jing
51addc48-28e4-4a31-b68b-62d4d77c4c32
Hales, Alan
66a20906-7b0e-4d23-b65a-08932f23900b
Rew, David
36dcc3ad-2379-4b61-a468-5c623d796887
2017
Lu, Jing
51addc48-28e4-4a31-b68b-62d4d77c4c32
Hales, Alan
66a20906-7b0e-4d23-b65a-08932f23900b
Rew, David
36dcc3ad-2379-4b61-a468-5c623d796887
Lu, Jing, Hales, Alan and Rew, David
(2017)
Modelling of cancer patient records: A structured approach to data mining and visual analytics.
Renda, M. Elena, Holzinger, Andreas, Khuri, Sami and Bursa, Miroslav
(eds.)
In Information Technology in Bio- and Medical Informatics - 8th International Conference, ITBAM 2017, Proceedings.
vol. 10443 LNCS,
Springer.
.
(doi:10.1007/978-3-319-64265-9_4).
Record type:
Conference or Workshop Item
(Paper)
Abstract
This research presents a methodology for health data analytics through a case study for modelling cancer patient records. Timeline-structured clinical data systems represent a new approach to the understanding of the relationship between clinical activity, disease pathologies and health outcomes. The novel Southampton Breast Cancer Data System contains episode and timeline-structured records on >17,000 patients who have been treated in University Hospital Southampton and affiliated hospitals since the late 1970s. The system is under continuous development and validation. Modern data mining software and visual analytics tools permit new insights into temporally-structured clinical data. The challenges and outcomes of the application of such software-based systems to this complex data environment are reported here. The core data was anonymised and put through a series of pre-processing exercises to identify and exclude anomalous and erroneous data, before restructuring within a remote data warehouse. A range of approaches was tested on the resulting dataset including multi-dimensional modelling, sequential patterns mining and classification. Visual analytics software has enabled the comparison of survival times and surgical treatments. The systems tested proved to be powerful in identifying episode sequencing patterns which were consistent with real-world clinical outcomes. It is concluded that, subject to further refinement and selection, modern data mining techniques can be applied to large and heterogeneous clinical datasets to inform decision making.
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More information
e-pub ahead of print date: 26 July 2017
Published date: 2017
Venue - Dates:
8th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2017, , Lyon, France, 2017-08-28 - 2017-08-31
Keywords:
Clinical data environment, Data mining, Decision support, Electronic patient records, Health information systems, Visual analytics
Identifiers
Local EPrints ID: 432252
URI: http://eprints.soton.ac.uk/id/eprint/432252
ISSN: 0302-9743
PURE UUID: d58e4fff-9437-422e-b956-99942a0b4148
Catalogue record
Date deposited: 05 Jul 2019 16:30
Last modified: 06 Jun 2024 02:06
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Contributors
Author:
Jing Lu
Author:
Alan Hales
Editor:
M. Elena Renda
Editor:
Andreas Holzinger
Editor:
Sami Khuri
Editor:
Miroslav Bursa
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