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DC Field | Value | Language |
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dc.contributor.author | Tucker, A | - |
dc.contributor.author | Li, Y | - |
dc.contributor.author | Garway-Heath, D | - |
dc.date.accessioned | 2017-06-07T12:52:41Z | - |
dc.date.available | 2017-03 | - |
dc.date.available | 2017-06-07T12:52:41Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Artificial Intelligence in Medicine, 2017, 77 pp. 23 - 30 | en_US |
dc.identifier.issn | C | - |
dc.identifier.issn | C | - |
dc.identifier.issn | 0933-3657 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/14688 | - |
dc.description.abstract | Clinical trials are typically conducted over a population within a de ned time period in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essen- tial for modelling detailed prognostic predictions. Longitudinal studies on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This paper explores the application of intelligent data analysis techniques for building reliable models of disease progression from both cross-sectional and longitudinal studies. The aim is to learn disease `tra- jectories' from cross-sectional data by building realistic trajectories from healthy patients to those with advanced disease. We focus on exploring whether we can `calibrate' models learnt from these trajectories with real longitudinal data using Baum-Welch re-estimation. | en_US |
dc.format.extent | 23 - 30 | - |
dc.language.iso | en | en_US |
dc.subject | Disease Progression | en_US |
dc.subject | Cross-Sectional Studies | en_US |
dc.subject | Stochastic Networks | en_US |
dc.title | Updating markov models to integrate cross-sectional and longitudinal studies | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/j.artmed.2017.03.005 | - |
dc.relation.isPartOf | Artificial Intelligence in Medicine | - |
pubs.notes | publisher: Elsevier articletitle: Updating Markov models to integrate cross-sectional and longitudinal studies journaltitle: Artificial Intelligence in Medicine articlelink: http://dx.doi.org/10.1016/j.artmed.2017.03.005 content_type: article copyright: Crown Copyright © 2017 Published by Elsevier B.V. All rights reserved. | - |
pubs.publication-status | Published | - |
pubs.volume | 77 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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Fulltext.pdf | 540.14 kB | Adobe PDF | View/Open |
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