Abstract Details
Activity Number:
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506
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Medical Devices and Diagnostics
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Abstract #313665
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View Presentation
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Title:
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A Location-Mixture Autoregressive Model for Online Forecasting of Lung Tumor Motion
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Author(s):
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Daniel Cervone*+ and Natesh S. Pillai and Debdeep Pati and John Henry Lewis and Ross Berbeco
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Companies:
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Harvard and Harvard and Florida State University and Dana-Farber Cancer Institute/Harvard Medical School and Brigham & Women's Hospital/Harvard Medical School
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Keywords:
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lung tumor tracking ;
nonlinear time series ;
mixture autoregressive process ;
time series motifs
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Abstract:
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Lung tumor tracking for radiotherapy requires real-time, multiple-step ahead forecasting of a quasi-periodic time series recording instantaneous tumor locations. We introduce a location-mixture autoregressive (LMAR) process, a novel time series model that admits multimodal conditional distributions, fast approximate inference using the Expectation-Maximization algorithm, and accurate multiple-step ahead predictive distributions. LMAR outperforms several commonly used methods in terms of out-of-sample prediction accuracy using clinical data from lung tumor patients. With its superior predictive performance and real-time computation, the LMAR model could be effectively implemented for use in current tumor tracking systems.
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Authors who are presenting talks have a * after their name.
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