Imperial College London
Alternate

DrMatthewWilliams

Faculty of Medicine, Department of Surgery & Cancer

Honorary Senior Research Fellow
 
 
 
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Contact

 

+44 (0)20 3311 0733matthew.williams Website CV

 
 
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Location

 

Charing Cross HospitalCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Rabinowicz:2018,
author = {Rabinowicz, S and Butz, R and Hommerson, A and Williams, M},
title = {CSBN: a hybrid approach for survival time prediction with missing data},
url = {http://hdl.handle.net/10044/1/83125},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - CSBN: A Hybrid Approach For Survival TimePrediction With Missing DataSimon Rabinowicz1, Raphaela Butz2,3, Arjen Hommersom3,4, and Matt Williams5,61Faculty Of Medicine, Imperial College London, UK2Institute for Computer Science, TH K oln, Germany3Department of Computer Science, Open University of the Netherlands4Department of Software Science, Radboud University, The Netherlands5Department of Radiotherapy, Charing Cross Hospital, London, UK6Computational Oncology Laboratory, Imperial College London, UKAbstract.Survival prediction models most commonly use Cox Proportional Hazards (CPH) models, and are frequently used in medical statistics and clinical practice. However, such models underperform when the predictor variables are missing. By building Bayesian networks we automatically construct a model with the most important risk factors and relationships between risk factors and Bayesian networks are able to infer the likely values of missing data. We therefore propose a hybrid solution, consisting of a CPH model and a BN, where the predictive variables in the CPH model are the child nodes of a BN, which we call CSBN. We learn the  CPH  and  BN  models separately,  using standard techniques, with the only constraint being that the variables that are predictors in the CPH model are child nodes in the BN. This allows us to fuse the two models, using the predictors of the CPH models as the join points. We test our approach by examining the performance of the CPH model, against the hybrid CSBN model, using both complete data cases and in cases with missing data.  We calculate the performance of  the survival prediction for both CPH and CSBN using the C-index and a normalised error function as metrics. For the CPH model, predictive error was significantly larger for missing data (±3120.8 days) compared to complete data (±1171.5 days;p= 3.6e−07). This was also true for the CSBN±1387.3 days for missing data compared with±1171.5 days with com
AU  - Rabinowicz,S
AU  - Butz,R
AU  - Hommerson,A
AU  - Williams,M
PY  - 2018///
TI  - CSBN: a hybrid approach for survival time prediction with missing data
UR  - http://hdl.handle.net/10044/1/83125
ER  -
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