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|Title:||Updating markov models to integrate cross-sectional and longitudinal studies|
|Keywords:||Disease Progression;Cross-Sectional Studies;Stochastic Networks|
|Citation:||Artificial Intelligence in Medicine, 2017, 77 pp. 23 - 30|
|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.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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