Titlebook: Optimizing Hospital-wide Patient Scheduling; Early Classification Daniel Gartner Book 2014 Springer International Publishing Switzerland 20

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Book 2014l that a small set of attributes is sufficient in order to substantially improve DRG classification accuracy as compared to the current approach of many hospitals. Moreover, the results of the patient scheduling part reveal that the contribution margin can be increased as compared to current practice.

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EWTON in seinen Untersuchungen zu gr?βerer begrifflicher Klarheit durchdrang, so haben sich doch die LEiBNIZschen Bezeichnungen und Rechenmethoden in h?herem Grade durchgesetzt als die NEWTONschen; noch heute bilden diese formalen Seiten der LEIBNIZschen Gedankenentwicklung ein unentbehrliches Eleme

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Daniel GartnerWeise, wobei die Verzweigungen stets dünner und dünner werden. Schlie?lich gehen die Arterien in zahlreiche Capillaren über: ?u?erst feine R?hrchen, die überall das Gewebe mit Blut versorgen. Nachdem sie die Gewebe durchstr?mt haben, vereinigen sich die Capillaren erst zu dünnen Venen, die mit ander

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Daniel Gartner of terms associated with volcan- logy or geophysics. To this end we have attempted ology, its processes and products. to create a book simple and general enough to be The atlas is, in a sense, an outgrowth of the useful even at the secondary school level, but with expanding interest in volcanology

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d glossary of terms associated with volcan- logy or geophysics. To this end we have attempted ology, its processes and products. to create a book simple and general enough to be The atlas is, in a sense, an outgrowth of the useful even at the secondary school level, but with expanding interest in volcanology 978-3-642-65152-6978-3-642-65150-2

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Lecture Notes in Economics and Mathematical Systemshttp://image.papertrans.cn/o/image/703345.jpg

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https://doi.org/10.1007/978-3-319-04066-0DRG Classification; Diagnosis-related groups; Hospital management; Machine learning; Patient scheduling

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Experimental Analyses,The structure of this chapter is as follows: In the first section, a thorough analysis of the presented machine learning methods for early DRG classification and its comparison with a DRG grouper is provided. In the second section, a computational and economic analysis of scheduling the hospital-wide patient flow of elective patients is given.