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).
Original language | English |
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Title of host publication | Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008 |
Pages | 644-649 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2008 |
Event | 7th International Conference on Machine Learning and Applications, ICMLA 2008 - San Diego, CA, United States Duration: Dec 11 2008 → Dec 13 2008 |
Other
Other | 7th International Conference on Machine Learning and Applications, ICMLA 2008 |
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Country/Territory | United States |
City | San Diego, CA |
Period | 12/11/08 → 12/13/08 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Software