[PDF.51ey] Optimizing Hospital-wide Patient Scheduling: Early Classification of Diagnosis-related Groups Through Machine Learning (Lecture Notes in Economics and Mathematical Systems)
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Optimizing Hospital-wide Patient Scheduling: Early Classification of Diagnosis-related Groups Through Machine Learning (Lecture Notes in Economics and Mathematical Systems)
Daniel Gartner
[PDF.uj82] Optimizing Hospital-wide Patient Scheduling: Early Classification of Diagnosis-related Groups Through Machine Learning (Lecture Notes in Economics and Mathematical Systems)
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| #6459250 in Books | Daniel G rtner | 2015-05-24 | 2015-05-28 | Original language:English | PDF # 1 | 9.25 x.31 x6.10l, | File type: PDF | 119 pages | Optimizing Hospital wide Patient Scheduling Early Classification of Diagnosis related Groups Through Machine Learning Lecture Notes in Economics and Mathematical Systems||From the Back Cover|Diagnosis-related groups (DRGs) are used in hospitals for the reimbursement of inpatient services. The assignment of a patient to a DRG can be distinguished into billing- and operations-driven DRG classification. The topic of this monograp
Diagnosis-related groups (DRGs) are used in hospitals for the reimbursement of inpatient services. The assignment of a patient to a DRG can be distinguished into billing- and operations-driven DRG classification. The topic of this monograph is operations-driven DRG classification, in which DRGs of inpatients are employed to improve contribution margin-based patient scheduling decisions. In the first part, attribute selection and classification techniques are evaluat...
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