Reducing COPD Readmissions: A Causal Bayesian Network Model

Sujee Lee; Sijie Wang; Philip A. Bain; Christine Baker; Tammy Kundinger; Craig Sommers; Jingshan Li

doi:10.1109/LRA.2018.2861084

Reducing COPD Readmissions: A Causal Bayesian Network Model

Sujee Lee, Sijie Wang, Philip A. Bain, Christine Baker, Tammy Kundinger, Craig Sommers, Jingshan Li

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

9 Citations (Scopus)

Abstract

This letter introduces a causal Bayesian network model to study readmissions reduction for chronic obstructive pulmonary disease (COPD) patients. The model employs a Bayesian network learning method and adopts domain knowledge. Using this model, we analyze the impacts of critical variables on a patient's readmission risk by the manipulation of such variables. Through this analysis, effective intervention options to reduce readmission can be identified, which can provide a quantitative tool for designing personalized interventions to reduce COPD readmissions. © 2016 IEEE.

Original language	English
Article number	8423078
Pages (from-to)	4046-4053
Journal	IEEE Robotics and Automation Letters
Volume	3
Issue number	4
DOIs	https://doi.org/10.1109/LRA.2018.2861084
Publication status	Published - 1 Oct 2018
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work was supported by the National Science Foundation under Grant CMMI-1536987.

Research Keywords

causal Bayesian network
Chronic obstructive pulmonary disease (COPD)
intervention
prediction
readmission

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abstract = "This letter introduces a causal Bayesian network model to study readmissions reduction for chronic obstructive pulmonary disease (COPD) patients. The model employs a Bayesian network learning method and adopts domain knowledge. Using this model, we analyze the impacts of critical variables on a patient's readmission risk by the manipulation of such variables. Through this analysis, effective intervention options to reduce readmission can be identified, which can provide a quantitative tool for designing personalized interventions to reduce COPD readmissions. {\textcopyright} 2016 IEEE.",

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AU - Sommers, Craig

AU - Li, Jingshan

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Reducing COPD Readmissions: A Causal Bayesian Network Model

Abstract

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