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Record Nr. |
UNINA9910298544003321 |
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Autore |
Gartner Daniel |
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Titolo |
Optimizing Hospital-wide Patient Scheduling : Early Classification of Diagnosis-related Groups Through Machine Learning / / by Daniel Gartner |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
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ISBN |
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3-319-04065-0 |
3-319-04066-9 |
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Edizione |
[1st ed. 2014.] |
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Descrizione fisica |
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1 online resource (132 p.) |
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Collana |
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Lecture Notes in Economics and Mathematical Systems, , 0075-8442 ; ; 674 |
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Disciplina |
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330 |
36.210.681 |
502.85 |
519.6 |
658.40301 |
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Soggetti |
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Operations research |
Decision making |
Health informatics |
Management science |
Health care management |
Health services administration |
Operations Research/Decision Theory |
Health Informatics |
Operations Research, Management Science |
Health Care Management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Introduction -- Machine learning for early DRG classification -- Scheduling the hospital-wide flow of elective patients -- Experimental analyses -- Conclusion. |
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Sommario/riassunto |
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Diagnosis-related groups (DRGs) are used in hospitals for the reimbursement of inpatient services. The assignment of a patient to a |
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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 evaluated in order to increase early DRG classification accuracy. Employing mathematical programming, the hospital-wide flow of elective patients is modelled taking into account DRGs, clinical pathways and scarce hospital resources. The results of the early DRG classification part reveal 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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2. |
Record Nr. |
UNINA9910349523503321 |
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Autore |
Sharan Kishori |
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Titolo |
Java 13 Revealed : For Early Adoption and Migration / / by Kishori Sharan |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 |
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ISBN |
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Edizione |
[2nd ed. 2019.] |
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Descrizione fisica |
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1 online resource (286 pages) : illustrations |
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Disciplina |
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Soggetti |
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Java (Computer program language) |
Compilers (Computer programs) |
Computer programming |
Java |
Compilers and Interpreters |
Programming Techniques |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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1: Local Variable Type Inference -- 2: Java Versioning Scheme -- 3: The |
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HTTP Client API -- 4: Launching Single File Source Code Programs -- 5: Enhanced Switch -- 6: Text Blocks -- 7: Class Data Sharing -- 8: Tools and APIs Changes. |
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Sommario/riassunto |
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Explore the new Java programming language features and APIs introduced in Java 10 through Java 13. Java 13 Revealed is for experienced Java programmers looking to migrate to Java 13. Author Kishori Sharan begins by covering how to use local variable type inference to improve readability of your code and time-based release versioning of the Java platform to understand the Java release strategy. This book provides extensive coverage of the new HTTP Client APIs, which were introduced in Java 9 as a preview and was made a standard feature in Java 11. New Java features such as launching a single-file source code program and new switch syntax are discussed in detail. You will: Use local variable type inference to declare local variables using the var restricted type name introduced in Java 10 Take advantage of application class data sharing among JVMs for faster application startup Create HTTP requests, responses, and web sockets with the new HTTP Client APIs Run a single-file Java source code program using the java command without compiling it Apply the new switch statement and expressions to write compact and less error-prone code Work with text blocks in Java code About new APIs, deprecated APIs, and deprecated tools. |
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