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Carlo Berzuini, Cristiana Larizza, "A Unified Approach for Modeling Longitudinal and Failure Time Data, With Application in Medical Monitoring," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 2, pp. 109123, February, 1996.  
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@article{ 10.1109/34.481537, author = {Carlo Berzuini and Cristiana Larizza}, title = {A Unified Approach for Modeling Longitudinal and Failure Time Data, With Application in Medical Monitoring}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {18}, number = {2}, issn = {01628828}, year = {1996}, pages = {109123}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.481537}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  A Unified Approach for Modeling Longitudinal and Failure Time Data, With Application in Medical Monitoring IS  2 SN  01628828 SP109 EP123 EPD  109123 A1  Carlo Berzuini, A1  Cristiana Larizza, PY  1996 KW  Bayesian inference KW  statistical forecasting KW  analysis of time series data KW  analysis of failure data KW  Markov chain Monte Carlo methods KW  conditional independence graphs KW  model determination KW  medical monitoring. VL  18 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—This paper considers biomedical problems in which a sample of subjects, for example clinical patients, is monitored through time for purposes of individual prediction. Emphasis is on situations in which the monitoring generates data both in the form of a time series and in the form of events (development of a disease, death, etc.) observed on each subject over specified intervals of time. A Bayesian approach to the combined modeling of both types of data for purposes of prediction is presented. The proposed method merges ideas of Bayesian hierarchical modeling, nonparametric smoothing of time series data, survival analysis, and forecasting into a unified framework. Emphasis is on flexible modeling of the time series data based on stochastic process theory. The use of Markov Chain Monte Carlo simulation to calculate the predictions of interest is discussed. Conditional independence graphs are used throughout for a clear presentation of the models. An application in the monitoring of transplant patients is presented.
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