Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11132
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dc.contributor.authorTucker, A-
dc.contributor.authorLi, Y-
dc.coverage.spatialPavia-
dc.coverage.spatialPavia-
dc.date.accessioned2015-07-10T11:19:15Z-
dc.date.available2015-06-17-
dc.date.available2015-07-10T11:19:15Z-
dc.date.issued2015-
dc.identifier.citationArtificial Intelligence in Medicine, Lecture Notes in Computer Science, 9105: 113 - 122, (2015)en_US
dc.identifier.issn0302-9743-
dc.identifier.urihttp://link.springer.com/chapter/10.1007%2F978-3-319-19551-3_14-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/11132-
dc.description.abstractClinical trials are typically conducted over a population within a defined 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 essential 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 ‘trajectories’ 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.extent113 - 122-
dc.language.isoenen_US
dc.publisherSpringer International Publishingen_US
dc.sourceArtificial Intelligence in Medicine-
dc.sourceArtificial Intelligence in Medicine-
dc.subjectDisease progressionen_US
dc.subjectCross-sectional studiesen_US
dc.subjectStochastic networksen_US
dc.titleUpdating stochastic networks to integrate cross-sectional and longitudinal studiesen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-19551-3_14-
dc.relation.isPartOfLecture Notes in Computer Science Volume-
pubs.publication-statusPublished-
pubs.publication-statusPublished-
pubs.volume9105-
Appears in Collections:Dept of Computer Science Research Papers

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