Prediction of health outcomes using big (health) data

Ognjen Arandjelovic

doi:10.1109/EMBC.2015.7318910

Prediction of health outcomes using big (health) data

Ognjen Arandjelovic^*

^*Corresponding author for this work

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Citations (Scopus)

Abstract

The vast amounts of information in the form of electronic medical records are used to develop a novel model of disease progression. The proposed model is based on the representation of a patient's medical history in the form of a binary history vector, motivated by empirical evidence from previous work and validated using a large 'real-world' data corpus. The scope for the use of the described methodology is overarching and ranges from smarter allocation of resources and discovery of novel disease progression patterns and interactions, to incentivization of patients to make lifestyle changes.

Original language	English
Title of host publication	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2543-2546
Number of pages	4
Volume	2015-November
ISBN (Print)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7318910
Publication status	Published - 4 Nov 2015
Event	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: 25 Aug 2015 → 29 Aug 2015

Conference

Conference	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/Territory	Italy
City	Milan
Period	25/08/15 → 29/08/15

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10.1109/EMBC.2015.7318910

Cite this

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title = "Prediction of health outcomes using big (health) data",

abstract = "The vast amounts of information in the form of electronic medical records are used to develop a novel model of disease progression. The proposed model is based on the representation of a patient's medical history in the form of a binary history vector, motivated by empirical evidence from previous work and validated using a large 'real-world' data corpus. The scope for the use of the described methodology is overarching and ranges from smarter allocation of resources and discovery of novel disease progression patterns and interactions, to incentivization of patients to make lifestyle changes.",

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Arandjelovic, O 2015, Prediction of health outcomes using big (health) data. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. vol. 2015-November, 7318910, Institute of Electrical and Electronics Engineers Inc., pp. 2543-2546, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 25/08/15. https://doi.org/10.1109/EMBC.2015.7318910

Prediction of health outcomes using big (health) data. / Arandjelovic, Ognjen.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. p. 2543-2546 7318910.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Prediction of health outcomes using big (health) data

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