A cost sensitive approach to predicting 30-day hospital readmission in COPD patients

Baechle, C; Agarwal, A; Behara, R; Zhu, X

A cost sensitive approach to predicting 30-day hospital readmission in COPD patients

Baechle, C Agarwal, A Behara, R Zhu, X

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Publication Type:: Conference Proceeding
Citation:: 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017, 2017, pp. 317 - 320
Issue Date:: 2017-04-11

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Field	Value	Language
dc.contributor.author	Baechle, C	en_US
dc.contributor.author	Agarwal, A	en_US
dc.contributor.author	Behara, R	en_US
dc.contributor.author	Zhu, X	en_US
dc.date.issued	2017-04-11	en_US
dc.identifier.citation	2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017, 2017, pp. 317 - 320	en_US
dc.identifier.isbn	9781509041794	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125680
dc.description.abstract	© 2017 IEEE. Chronic Obstructive Pulmonary Disease is a painful chronic disease responsible for many unplanned hospital readmissions. Recent Federal legislation has begun to financially penalize hospitals which have excess patient readmissions. Predictive analytics offers a method to statistically predict which patients are at greatest risk for hospital readmission. Many readmission models currently exist, but few incorporate cost. Our research proposes several methods to directly incorporate cost into patient readmission prediction. Additionally, a method for evaluating the cost of existing models is proposed. Results show traditional evaluation methods such as AUC to have little relation to actual financial penalties (correlation = -0.21) and that dynamic cost evaluation to result in the largest cost savings.	en_US
dc.relation.ispartof	2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017	en_US
dc.relation.isbasedon	10.1109/BHI.2017.7897269	en_US
dc.title	A cost sensitive approach to predicting 30-day hospital readmission in COPD patients	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Chronic Obstructive Pulmonary Disease is a painful chronic disease responsible for many unplanned hospital readmissions. Recent Federal legislation has begun to financially penalize hospitals which have excess patient readmissions. Predictive analytics offers a method to statistically predict which patients are at greatest risk for hospital readmission. Many readmission models currently exist, but few incorporate cost. Our research proposes several methods to directly incorporate cost into patient readmission prediction. Additionally, a method for evaluating the cost of existing models is proposed. Results show traditional evaluation methods such as AUC to have little relation to actual financial penalties (correlation = -0.21) and that dynamic cost evaluation to result in the largest cost savings.

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http://hdl.handle.net/10453/125680