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" Ensemble Learning for Predicting Gynecological Oncology Patients Using Travel Distance "
Jarett, Jamie
Khasawneh, Mohammad
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Latin Dissertation
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English
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| Record Number
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1051917
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| Doc. No
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TL51034
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| Main Entry
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Jarett, Jamie
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Ensemble Learning for Predicting Gynecological Oncology Patients Using Travel Distance\ Jarett, JamieKhasawneh, Mohammad
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| College
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State University of New York at Binghamton
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| Date
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2019
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| Degree
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M.S.
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2019
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| Note
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62 p.
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| Abstract
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Patient readmissions to a hospital within thirty days of discharge pose many problems for all health systems. These avoidable, unscheduled admissions cause issues with bed shortages, they are expensive, and they are used as a quality metric in rating and comparing health systems. For these reasons, it is in the best interest for health systems to reduce the number of avoidable thirty day readmissions. In an attempt to tackle this issue, a model predicting surgical oncology patient readmissions using two ensemble methods was created. Along with the predicted, and generally accepted, clinical factors that affect readmissions, it was found that a patient’s geographical location, including the mean income of the hometown, the traveling distance to the hospital, and the traveling time to the hospital all significantly impacted the probability of a patient being readmitted.
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Engineering
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Industrial engineering
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Khasawneh, Mohammad
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State University of New York at Binghamton
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