Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity

Rebecca Rollins; Adele H. Marshall; Eibhlin McLoone; Sarah Chamney

doi:10.1109/CBMS.2015.78

Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity

Rebecca Rollins, Adele H. Marshall, Eibhlin McLoone, Sarah Chamney

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Retinopathy of prematurity (ROP) is a rare disease in which retinal blood vessels of premature infants fail to develop normally, and is one of the major causes of childhood blindness throughout the world. The Discrete Conditional Phase-type (DC-Ph) model consists of two components, the conditional component measuring the inter-relationships between covariates and the survival component which models the survival distribution using a Coxian phase-type distribution. This paper expands the DC-Ph models by introducing a support vector machine (SVM), in the role of the conditional component. The SVM is capable of classifying multiple outcomes and is used to identify the infant's risk of developing ROP. Class imbalance makes predicting rare events difficult. A new class decomposition technique, which deals with the problem of multiclass imbalance, is introduced. Based on the SVM classification, the length of stay in the neonatal ward is modelled using a 5, 8 or 9 phase Coxian distribution.

Original language	English
Title of host publication	Computer-Based Medical Systems (CBMS), 2015 IEEE 28th International Symposium on
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	250-255
Number of pages	6
DOIs	https://doi.org/10.1109/CBMS.2015.78
Publication status	Published - Jun 2015
Event	2015 IEEE 28th International Symposium on Computer-Based Medical Systems (CBMS) - São Carlos and Ribeirão Preto, Brazil Duration: 22 Jun 2015 → 25 Jun 2015

Conference

Conference	2015 IEEE 28th International Symposium on Computer-Based Medical Systems (CBMS)
Country	Brazil
City	São Carlos and Ribeirão Preto
Period	22/06/2015 → 25/06/2015

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10.1109/CBMS.2015.78

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Rollins, R., Marshall, A. H., McLoone, E., & Chamney, S. (2015). Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity. In Computer-Based Medical Systems (CBMS), 2015 IEEE 28th International Symposium on (pp. 250-255). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CBMS.2015.78

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title = "Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity",

abstract = "Retinopathy of prematurity (ROP) is a rare disease in which retinal blood vessels of premature infants fail to develop normally, and is one of the major causes of childhood blindness throughout the world. The Discrete Conditional Phase-type (DC-Ph) model consists of two components, the conditional component measuring the inter-relationships between covariates and the survival component which models the survival distribution using a Coxian phase-type distribution. This paper expands the DC-Ph models by introducing a support vector machine (SVM), in the role of the conditional component. The SVM is capable of classifying multiple outcomes and is used to identify the infant's risk of developing ROP. Class imbalance makes predicting rare events difficult. A new class decomposition technique, which deals with the problem of multiclass imbalance, is introduced. Based on the SVM classification, the length of stay in the neonatal ward is modelled using a 5, 8 or 9 phase Coxian distribution.",

author = "Rebecca Rollins and Marshall, {Adele H.} and Eibhlin McLoone and Sarah Chamney",

year = "2015",

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doi = "10.1109/CBMS.2015.78",

language = "English",

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booktitle = "Computer-Based Medical Systems (CBMS), 2015 IEEE 28th International Symposium on",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

note = "2015 IEEE 28th International Symposium on Computer-Based Medical Systems (CBMS) ; Conference date: 22-06-2015 Through 25-06-2015",

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Rollins, R, Marshall, AH, McLoone, E & Chamney, S 2015, Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity. in Computer-Based Medical Systems (CBMS), 2015 IEEE 28th International Symposium on. Institute of Electrical and Electronics Engineers (IEEE), pp. 250-255, 2015 IEEE 28th International Symposium on Computer-Based Medical Systems (CBMS), São Carlos and Ribeirão Preto, Brazil, 22/06/2015. https://doi.org/10.1109/CBMS.2015.78

Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity. / Rollins, Rebecca; Marshall, Adele H.; McLoone, Eibhlin; Chamney, Sarah.

Computer-Based Medical Systems (CBMS), 2015 IEEE 28th International Symposium on. Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 250-255.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Retinopathy of prematurity (ROP) is a rare disease in which retinal blood vessels of premature infants fail to develop normally, and is one of the major causes of childhood blindness throughout the world. The Discrete Conditional Phase-type (DC-Ph) model consists of two components, the conditional component measuring the inter-relationships between covariates and the survival component which models the survival distribution using a Coxian phase-type distribution. This paper expands the DC-Ph models by introducing a support vector machine (SVM), in the role of the conditional component. The SVM is capable of classifying multiple outcomes and is used to identify the infant's risk of developing ROP. Class imbalance makes predicting rare events difficult. A new class decomposition technique, which deals with the problem of multiclass imbalance, is introduced. Based on the SVM classification, the length of stay in the neonatal ward is modelled using a 5, 8 or 9 phase Coxian distribution.

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Rollins R, Marshall AH, McLoone E, Chamney S. Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity. In Computer-Based Medical Systems (CBMS), 2015 IEEE 28th International Symposium on. Institute of Electrical and Electronics Engineers (IEEE). 2015. p. 250-255 https://doi.org/10.1109/CBMS.2015.78

Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity

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