Sökning: WFRF:(Peterson G.)
> (2000-2004)
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Using hidden Markov...
Using hidden Markov models to characterize disease trajectories
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- Peterson, Carsten (författare)
- Lund University,Lunds universitet,Beräkningsbiologi och biologisk fysik - Genomgår omorganisation,Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation,Naturvetenskapliga fakulteten,Computational Biology and Biological Physics - Undergoing reorganization,Department of Astronomy and Theoretical Physics - Undergoing reorganization,Faculty of Science
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- Ohlsson, Mattias (författare)
- Lund University,Lunds universitet,Beräkningsbiologi och biologisk fysik - Genomgår omorganisation,Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation,Naturvetenskapliga fakulteten,Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS),Forskargrupper vid Lunds universitet,Computational Biology and Biological Physics - Undergoing reorganization,Department of Astronomy and Theoretical Physics - Undergoing reorganization,Faculty of Science,Artificial Intelligence in CardioThoracic Sciences (AICTS),Lund University Research Groups
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Papadourakis, G. M. (redaktör/utgivare)
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(creator_code:org_t)
- 2001
- 2001
- Engelska 3 s.
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Ingår i: Proceedings of the Neural Networks and Expert Systems in Medicine and Healthcare Conference, 324-326 (2001), eds. G.M. Papadourakis. ; , s. 324-326
- Relaterad länk:
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https://lup.lub.lu.s...
Abstract
Ämnesord
Stäng
- A novel approach is developed for predicting body trajectories for cancer progression, where conditional probabilities of clinical data are modeled using Hidden Markov Model techniques. Basically, each potential body site is encoded by an N-letter code, and a disease trajectory is described in terms of a string of letters. Patient data base records are then represented by such strings with different lengths, start points and end points. The approach is explored using pathology data for non-Hodgkin lymphoma augmented with an artificial data base generated according to observed distributions in the clinical data. For the Hidden Markov Models a Bayesian approach is taken using the Hybrid Monte Carlo method, producing an ensemble of models rather than a single one. Using a test set consisting of both real and random trajectories, we estimate the performance of our Hidden Markov Model models and also extract most probable profiles. Given the limited data set size the results are very encouraging.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Annan medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Other Medical Engineering (hsv//eng)
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