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عنوان
An Analytics Driven Decision Support System to Investigate the Risk Of Non_Index Hospital Readmission

پدید آورنده
Yang, Yujing

موضوع
Applied mathematics,Biomedical engineering,Health care management,Industrial engineering

رده

کتابخانه
کتابخانه مطالعات اسلامی به زبان های اروپایی

محل استقرار
استان: قم ـ شهر: قم

کتابخانه مطالعات اسلامی به زبان های اروپایی

تماس با کتابخانه : 32910706-025

TL51971

انگلیسی

An Analytics Driven Decision Support System to Investigate the Risk Of Non_Index Hospital Readmission
[Thesis]
Yang, Yujing
Noor E Alam, Muhammad

Northeastern University
2019

29 p.

M.S.
Northeastern University
2019

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.

Applied mathematics
Biomedical engineering
Health care management
Industrial engineering

Yang, Yujing

Noor E Alam, Muhammad

Northeastern University

 مطالعه متن کتاب 

p

[Thesis]
276903

a
Y

الاقتراح / اعلان الخلل

تحذیر! دقق في تسجیل المعلومات
ارسال عودة
تتم إدارة هذا الموقع عبر مؤسسة دار الحديث العلمية - الثقافية ومركز البحوث الكمبيوترية للعلوم الإسلامية (نور)
المكتبات هي المسؤولة عن صحة المعلومات كما أن الحقوق المعنوية للمعلومات متعلقة بها
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