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Content Provider | IEEE Xplore Digital Library |
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Author | Fernandes, A.S. Jarman, I.H. Etchells, T.A. Fonseca, J.M. Biganzoli, E. Bajdik, C. Lisboa, P. |
Copyright Year | 2008 |
Description | Author affiliation: Univ. degli Studi di Milano, Milan (Biganzoli, E.) || Fac. de Cienc. e Lecnologia, Univ. Nova de Lisboa, Lisboa (Fernandes, A.S.; Fonseca, J.M.; Lisboa, P.) || Sch. of Comput. & Math. Sci., Liverpool John Moores Univ., Liverpool (Jarman, I.H.; Etchells, T.A.) || British Columbia Cancer Agency, Vancouver, BC (Bajdik, C.) |
Abstract | Missing values are common in medical datasets and may be amenable to data imputation when modelling a given data set or validating on an external cohort. This paper discusses model averaging over samples of the imputed distribution and extends this approach to generic non-linear modelling with the Partial Logistic Artificial Neural Network (PLANN) regularised within the evidence-based framework with Automatic Relevance Determination (ARD). The study then applies the imputation to external validation over new patient cohorts, considering also the case of predictions made for individual patients. A prognostic index is defined for the non-linear model and validation results show that 4 statistically significant risk groups identified at the 95% level of confidence from the modelling data, from Christie Hospital (n=931), retain good separation during external validation with data from the British Columbia Cancer Agency (n=4,083). |
Starting Page | 644 |
Ending Page | 649 |
File Size | 487533 |
Page Count | 6 |
File Format | |
ISBN | 9780769534954 |
DOI | 10.1109/ICMLA.2008.106 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2008-12-11 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Hospitals Training data Medical treatment Machine learning Predictive models Breast cancer Metastasis Logistics Recruitment Tumors |
Content Type | Text |
Resource Type | Article |
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