عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
شماره کتابشناسی ملی
شماره
TLpq2354751740
زبان اثر
زبان متن نوشتاري يا گفتاري و مانند آن
انگلیسی
عنوان و نام پديدآور
عنوان اصلي
An Analytics Driven Decision Support System to Investigate the Risk Of Non_Index Hospital Readmission
نام عام مواد
[Thesis]
نام نخستين پديدآور
Yang, Yujing
نام ساير پديدآوران
Noor E Alam, Muhammad
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
Northeastern University
تاریخ نشرو بخش و غیره
2019
مشخصات ظاهری
نام خاص و کميت اثر
29
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.S.
کسي که مدرک را اعطا کرده
Northeastern University
امتياز متن
2019
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
Improving the quality of healthcare during hospitalization and after discharging can be realized by identification of 30-day unplanned hospital readmission risk. Prior research suggests that a significant proportion of preventable hospital readmission is attributed to non-index hospital readmission. In particular, followed by the implementation of Hospital Readmission Reduction Program (HRRP), non-index hospital readmission has increased although index hospital readmission has shown a decreasing trend. The existing models in prior researches might not capture the underlying association of predictors with non-index readmission and may lack the reliability and practicability when predicting non-index hospital readmission. Therefore, there exists a critical need to proactively predict non-index hospital readmission in an effort to recommend custom designed post-discharge protocols for patients at risk of experiencing readmission to a non-index hospital. To address this challenge, this study introduces a framework to examine the risk of non-index hospital readmission. Leveraging the state of California hospital discharge datasets, this study uses and compares the predictive models of four machine learning algorithms: logistic regression, random forest, decision tree, and gradient boosting, to predict the likelihood of non-index hospital readmission. AUC and recall scores are used to compare model performance. Results show that the logistic regression model outperforms the other tree-based algorithms, in terms of AUC and recall score. The prominent features shown from the results support previous research findings. This study has the potential to be implemented as a decision support system in clinical setting to help identify the risk of non-index hospital readmission, and thus to recommend effective interventions in order to improve healthcare quality.
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
Applied mathematics
موضوع مستند نشده
Biomedical engineering
موضوع مستند نشده
Health care management
موضوع مستند نشده
Industrial engineering
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )
مستند نام اشخاص تاييد نشده
Noor E Alam, Muhammad
مستند نام اشخاص تاييد نشده
Yang, Yujing
دسترسی و محل الکترونیکی
نام الکترونيکي
مطالعه متن کتاب وضعیت انتشار
فرمت انتشار
p
اطلاعات رکورد کتابشناسی
نوع ماده
[Thesis]
کد کاربرگه
276903
اطلاعات دسترسی رکورد
سطح دسترسي
a
تكميل شده
Y
