عنوان

Ensemble Learning for Predicting Gynecological Oncology Patients Using Travel Distance

شماره

TL51034

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Ensemble Learning for Predicting Gynecological Oncology Patients Using Travel Distance

نام عام مواد

[Thesis]

نام نخستين پديدآور

Jarett, Jamie

نام ساير پديدآوران

Khasawneh, Mohammad

نام ناشر، پخش کننده و غيره

State University of New York at Binghamton

تاریخ نشرو بخش و غیره

2019

متن يادداشت

62 p.

جزئيات پايان نامه و نوع درجه آن

M.S.

کسي که مدرک را اعطا کرده

State University of New York at Binghamton

امتياز متن

2019

متن يادداشت

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.

اصطلاح موضوعی

Engineering

اصطلاح موضوعی

Industrial engineering

مستند نام اشخاص تاييد نشده

Jarett, Jamie

مستند نام اشخاص تاييد نشده

Khasawneh, Mohammad

مستند نام تنالگان تاييد نشده

State University of New York at Binghamton

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y