Imperial College London
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ProfessorAndrewBush

Faculty of Medicine, National Heart & Lung Institute

Professor of Paediatric Respirology
 
 
 
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Contact

 

+44 (0)20 7352 8121 ext 2255a.bush

 
 
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Location

 

Chelsea WingRoyal Brompton Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Belgrave:2018:10.1109/ICMLA.2017.0-176,
author = {Belgrave, D and Cassidy, R and Custovic, A and Stamate, D and Fleming, L and Bush, A and Saglani, S},
doi = {10.1109/ICMLA.2017.0-176},
pages = {68--75},
publisher = {IEEE},
title = {Predictive Modelling Strategies to Understand Heterogeneous Manifestations of Asthma in Early Life},
url = {http://dx.doi.org/10.1109/ICMLA.2017.0-176},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Wheezing is common among children and ~50% of those under 6 years of age are thought to experience at least one episode of wheeze. However, due to the heterogeneity of symptoms there are difficulties in treating and diagnosing these children. `Phenotype specific therapy' is one possible avenue of treatment, whereby we use significant pathology and physiology to identify and treat pre-schoolers with wheeze. By performing feature selection algorithms and predictive modelling techniques, this study will attempt to determine if it is possible to robustly distinguish patient diagnostic categories among pre-school children. Univariate feature analysis identified more objective variables and recursive feature elimination a larger number of subjective variables as important in distinguishing between patient categories. Predicative modelling saw a drop in performance when subjective variables were removed from analysis, indicating that these variables are important in distinguishing wheeze classes. We achieved 90%+ performance in AUC, sensitivity, specificity, and accuracy, and 80%+ in kappa statistic, in distinguishing ill from healthy patients. Developed in a synergistic statistical - machine learning approach, our methodologies propose also a novel ROC Cross Evaluation method for model post-processing and evaluation. Our predictive modelling's stability was assessed in computationally intensive Monte Carlo simulations.
AU  - Belgrave,D
AU  - Cassidy,R
AU  - Custovic,A
AU  - Stamate,D
AU  - Fleming,L
AU  - Bush,A
AU  - Saglani,S
DO  - 10.1109/ICMLA.2017.0-176
EP  - 75
PB  - IEEE
PY  - 2018///
SP  - 68
TI  - Predictive Modelling Strategies to Understand Heterogeneous Manifestations of Asthma in Early Life
UR  - http://dx.doi.org/10.1109/ICMLA.2017.0-176
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000425853000011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/58586
ER  -
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