AAAI Publications, Twenty-Fifth IAAI Conference

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Assessing the Predictability of Hospital Readmission Using Machine Learning
Arian Hosseinzadeh, Masoumeh Izadi, Aman Verma, Doina Precup, David Buckeridge

Last modified: 2013-06-28


Unplanned hospital readmissions raise health care costs
and cause significant distress to patients. Hence, predicting
which patients are at risk to be readmitted is
of great interest. In this paper, we mine large amounts
of administrative information from claim data, including
patients demographics, dispensed drugs, medical or
surgical procedures performed, and medical diagnosis,
in order to predict readmission using supervised learning
methods. Our objective is to gain knowledge about
the predictive power of the available information. Our
preliminary results on data from the provincial hospital
system in Quebec illustrate the potential for this approach
to reveal important information on factors that
trigger hospital readmission. Our findings suggest that
a substantial portion of readmissions is inherently hard
to predict. Consequently, the use of the raw readmission
rate as an indicator of the quality of provided care might
not be appropriate.


hospital readmission; prediction modeling; supervised learning

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