Communications of the ACM
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Enhancing HMM-based biomedical named entity recognition by studying special phenomena
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
A Bayesian network approach to explaining time series with changing structure
Intelligent Data Analysis
Dynamic Bayesian networks as prognostic models for clinical patient management
Journal of Biomedical Informatics
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
Making time: pseudo time-series for the temporal analysis of cross section data
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
The pseudotemporal bootstrap for predicting glaucoma from cross-sectional visual field data
IEEE Transactions on Information Technology in Biomedicine
Learning Non-Stationary Dynamic Bayesian Networks
The Journal of Machine Learning Research
Predicting glaucomatous visual field deterioration through short multivariate time series modelling
Artificial Intelligence in Medicine
Comparison of regression tree data mining methods for prediction of mortality in head injury
Expert Systems with Applications: An International Journal
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Clinical 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 give us a 'snapshot' of this disease process over a large number of people but do not allow us to model the temporal nature of disease, thereby allowing for modelling detailed prognostic predictions. The aim of this paper is to explore an extension of the temporal bootstrap to identify intermediate stages in a disease process and sub-categories of the disease exhibiting subtly different symptoms. Our approach is compared to a strawman method and investigated in its ability to explain the dynamics of progression on biomedical data from three diseases: Glaucoma, Breast Cancer and Parkinson's disease. We focus on creating reliable time-series models from large amounts of historical cross-sectional data using the temporal bootstrap technique. Two issues are explored: how to build time-series models from cross-sectional data, and how to automatically identify different disease states along these trajectories, as well as the transitions between them. Our approach of relabeling trajectories allows us to explore the temporal nature of how diseases progress even when time-series data is not available (if the cross-sectional study is large enough). We intend to expand this research to deal with multiple studies where we can combine both cross-sectional and longitudinal datasets and to focus on the junctions of the trajectories as key stages in the progression of disease.