Using patient data similarities to predict radiation pneumonitis via a self-organizing map
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Combining multiple models to generate consensus: application to radiation-induced pneumonitis predictionStatistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods.A genetic algorithm for variable selection in logistic regression analysis of radiotherapy treatment outcomes.Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework.
P2860
Using patient data similarities to predict radiation pneumonitis via a self-organizing map
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2007 nî lūn-bûn
@nan
2007 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Using patient data similaritie ...... itis via a self-organizing map
@ast
Using patient data similaritie ...... itis via a self-organizing map
@en
type
label
Using patient data similaritie ...... itis via a self-organizing map
@ast
Using patient data similaritie ...... itis via a self-organizing map
@en
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Using patient data similaritie ...... itis via a self-organizing map
@ast
Using patient data similaritie ...... itis via a self-organizing map
@en
P2093
P2860
P356
P1476
Using patient data similaritie ...... itis via a self-organizing map
@en
P2093
Fang-Fang Yin
Lawrence B Marks
Shifeng Chen
Shiva K Das
Sumin Zhou
P2860
P304
P356
10.1088/0031-9155/53/1/014
P577
2007-12-19T00:00:00Z